coderabbit comments

README.md

@@ -112,6 +112,13 @@ That's it! Check out our [Getting Started Guide](https://docs.axolotl.ai/docs/ge
 
 Contributions are welcome! Please see our [Contributing Guide](https://github.com/axolotl-ai-cloud/axolotl/blob/main/.github/CONTRIBUTING.md) for details.
 
+## 📈 Telemetry
+
+Axolotl has opt-in telemetry that helps us understand how the project is being used
+and prioritize improvements. We collect basic system information, model types, and
+error rates—never personal data or file paths. Telemetry is disabled by default. To
+enable it, set AXOLOTL_DO_NOT_TRACK=0. For more details, see our [telemetry documentation](https://axolotl-ai-cloud.github.io/axolotl/docs/telemetry.html).
+
 ## Supported Models
 
 |             | fp16/fp32 | lora | qlora | gptq | gptq w/flash attn | flash attn | xformers attn |

_quarto.yml

@@ -236,6 +236,7 @@ website:
             - docs/inference.qmd
             - docs/cli.qmd
             - docs/config.qmd
+            - docs/telemetry.qmd
             - text: "API Reference"
               href: docs/api
 

docs/telemetry.qmd

@@ -0,0 +1,59 @@
+---
+title: Telemetry
+description: A description of the opt-in telemetry implementation in Axolotl.
+---
+
+# Telemetry in Axolotl
+
+Axolotl implements anonymous telemetry to help maintainers understand how the library
+is used and where users encounter issues. This data helps prioritize features, optimize
+performance, and fix bugs.
+
+## Data Collection
+
+We collect:
+
+- System info: OS, Python version, Axolotl version, PyTorch version, Transformers
+version, etc.
+- Hardware info: CPU count, memory, GPU count and models
+- Runtime metrics: Training progress, memory usage, timing information
+- Usage patterns: Models (from a whitelist) and configurations used
+- Error tracking: Stack traces and error messages (sanitized to remove personal
+information)
+
+Personally identifiable information (PII) is not collected.
+
+## Implementation
+
+Telemetry is implemented using PostHog and consists of:
+
+- `axolotl.telemetry.TelemetryManager`: A singleton class that initializes the
+telemetry system and provides methods for tracking events.
+- `axolotl.telemetry.errors.send_errors`: A decorator that captures exceptions and
+sends sanitized stack traces.
+- `axolotl.telemetry.runtime_metrics.RuntimeMetricsTracker`: A class that tracks
+runtime metrics during training.
+- `axolotl.telemetry.callbacks.TelemetryCallback`: A Trainer callback that sends
+runtime metrics telemetry.
+
+The telemetry system will block training startup for 15 seconds to ensure users are
+aware of data collection, unless telemetry is explicitly enabled or disabled.
+
+## Opt-In Mechanism
+
+Telemetry is **disabled by default** on an opt-in basis. To enable it, set `AXOLOTL_DO_NOT_TRACK=0`.
+
+To remove the warning message about telemetry that is displayed on train, etc. startup,
+explicitly set: `AXOLOTL_DO_NOT_TRACK=0` (enable telemetry) or `AXOLOTL_DO_NOT_TRACK=1`
+(explicitly disable telemetry).
+
+**Note**: Telemetry will move to an opt-out model in a later release.
+
+## Privacy
+
+- All path-like config information is automatically redacted from telemetry data
+- Model information is only collected for whitelisted organizations
+    - See `axolotl/telemetry/whitelist.yaml` for the set of whitelisted organizations
+- Each run generates a unique anonymous ID
+    - This allows us to link different telemetry events in a single same training run
+- Telemetry is only sent from the main process to avoid duplicate events

requirements.txt

@@ -20,7 +20,6 @@ datasets==3.6.0
 deepspeed>=0.17.0
 trl==0.18.1
 hf_xet==1.1.2
-mistral-common[hf-hub]==1.6.0
 
 optimum==1.16.2
 hf_transfer
@@ -68,3 +67,6 @@ schedulefree==1.4.1
 
 axolotl-contribs-lgpl==0.0.6
 axolotl-contribs-mit==0.0.3
+
+# telemetry
+posthog>=4.2.0

src/axolotl/cli/config.py

@@ -14,6 +14,8 @@ import yaml
 from transformers.utils import is_torch_bf16_gpu_available
 
 from axolotl.integrations.base import PluginManager
+from axolotl.telemetry.errors import send_errors
+from axolotl.telemetry.manager import TelemetryManager
 from axolotl.utils.comet_ import setup_comet_env_vars
 from axolotl.utils.config import (
     normalize_cfg_datasets,
@@ -28,6 +30,8 @@ from axolotl.utils.wandb_ import setup_wandb_env_vars
 
 LOG = get_logger(__name__, use_environ=True)
 
+TELEMETRY_MANAGER = TelemetryManager.get_instance()
+
 
 def check_remote_config(config: Union[str, Path]) -> Union[str, Path]:
     """
@@ -159,6 +163,7 @@ def plugin_set_cfg(cfg: DictDefault):
         plugin_manager.cfg = cfg
 
 
+@send_errors
 def load_cfg(
     config: str | Path | DictDefault = Path("examples/"), **kwargs
 ) -> DictDefault:
@@ -192,6 +197,8 @@ def load_cfg(
             temp_file.close()
         cfg.axolotl_config_path = temp_file.name
 
+    TELEMETRY_MANAGER.send_event(event_type="config-loaded", properties=cfg)
+
     # If there are any options passed in the cli, if it is something that seems valid
     # from the yaml, then overwrite the value
     cfg_keys = cfg.keys()
@@ -233,4 +240,6 @@ def load_cfg(
     setup_comet_env_vars(cfg)
     plugin_set_cfg(cfg)
 
+    TELEMETRY_MANAGER.send_event(event_type="config-processed", properties=cfg)
+
     return cfg

src/axolotl/cli/inference.py

@@ -16,6 +16,7 @@ from axolotl.cli.args import InferenceCliArgs
 from axolotl.cli.art import print_axolotl_text_art
 from axolotl.cli.config import load_cfg
 from axolotl.cli.utils import load_model_and_tokenizer
+from axolotl.telemetry.errors import send_errors
 from axolotl.utils.chat_templates import (
     get_chat_template,
     get_chat_template_from_config,
@@ -42,6 +43,7 @@ def get_multi_line_input() -> str:
     return instruction
 
 
+@send_errors
 def do_inference(
     *,
     cfg: DictDefault,
@@ -135,6 +137,7 @@ def do_inference(
         print(tokenizer.decode(generated["sequences"].cpu().tolist()[0]))
 
 
+@send_errors
 def do_inference_gradio(
     *,
     cfg: DictDefault,

src/axolotl/cli/merge_lora.py

@@ -9,12 +9,14 @@ from dotenv import load_dotenv
 from axolotl.cli.art import print_axolotl_text_art
 from axolotl.cli.config import load_cfg
 from axolotl.cli.utils import load_model_and_tokenizer
+from axolotl.telemetry.errors import send_errors
 from axolotl.utils.dict import DictDefault
 from axolotl.utils.logging import get_logger
 
 LOG = get_logger(__name__)
 
 
+@send_errors
 def do_merge_lora(*, cfg: DictDefault) -> None:
     """
     Calls `transformers`' `merge_and_unload` on the model given in the `axolotl` config

src/axolotl/cli/merge_sharded_fsdp_weights.py

@@ -24,6 +24,7 @@ from torch.distributed.checkpoint.format_utils import _EmptyStateDictLoadPlanner
 
 from axolotl.cli.art import print_axolotl_text_art
 from axolotl.cli.config import load_cfg
+from axolotl.telemetry.errors import send_errors
 from axolotl.utils.logging import get_logger
 
 LOG = get_logger(__name__)
@@ -118,6 +119,7 @@ def _distributed_checkpoint_to_merged_weights(
     return save_path_
 
 
+@send_errors
 def merge_fsdp_weights(
     checkpoint_dir: str,
     output_path: str,

src/axolotl/cli/preprocess.py

@@ -18,6 +18,7 @@ from axolotl.cli.config import load_cfg
 from axolotl.common.const import DEFAULT_DATASET_PREPARED_PATH
 from axolotl.common.datasets import load_datasets, load_preference_datasets
 from axolotl.integrations.base import PluginManager
+from axolotl.telemetry.errors import send_errors
 from axolotl.utils.dict import DictDefault
 from axolotl.utils.logging import get_logger
 from axolotl.utils.trainer import disable_datasets_caching
@@ -25,6 +26,7 @@ from axolotl.utils.trainer import disable_datasets_caching
 LOG = get_logger(__name__)
 
 
+@send_errors
 def do_preprocess(cfg: DictDefault, cli_args: PreprocessCliArgs) -> None:
     """
     Preprocesses dataset specified in axolotl config.

src/axolotl/cli/utils.py

@@ -305,8 +305,8 @@ def load_model_and_tokenizer(
     ProcessorMixin | None,
 ]:
     """
-    Helper function for loading a model, tokenizer, and processor specified in the
-    given `axolotl` config.
+    Helper function for loading a model, tokenizer, and processor specified in the given `axolotl`
+    config.
 
     Args:
         cfg: Dictionary mapping `axolotl` config keys to values.

src/axolotl/common/datasets.py

@@ -10,6 +10,7 @@ from datasets import Dataset
 import axolotl.monkeypatch.data.batch_dataset_fetcher  # pylint: disable=unused-import  # noqa: F401
 from axolotl.cli.args import PreprocessCliArgs, TrainerCliArgs
 from axolotl.loaders import load_processor, load_tokenizer
+from axolotl.telemetry.errors import send_errors
 from axolotl.utils.data import prepare_dataset
 from axolotl.utils.data.rl import load_prepare_preference_datasets
 from axolotl.utils.dict import DictDefault
@@ -45,6 +46,7 @@ def sample_dataset(dataset: Dataset, num_samples: int) -> Dataset:
     )
 
 
+@send_errors
 def load_datasets(
     *,
     cfg: DictDefault,
@@ -112,6 +114,7 @@ def load_datasets(
     )
 
 
+@send_errors
 def load_preference_datasets(
     *,
     cfg: DictDefault,

src/axolotl/core/builders/base.py

@@ -31,6 +31,8 @@ from transformers.training_args import OptimizerNames
 
 from axolotl.integrations.base import PluginManager
 from axolotl.monkeypatch.trainer.lr import patch_trainer_get_lr
+from axolotl.telemetry.callbacks import TelemetryCallback
+from axolotl.telemetry.manager import TelemetryManager
 from axolotl.utils import is_comet_available, is_mlflow_available
 from axolotl.utils.callbacks import (
     GCCallback,
@@ -145,6 +147,10 @@ class TrainerBuilderBase(abc.ABC):
 
         callbacks.append(GPUStatsCallback(cfg=self.cfg))
 
+        telemetry_manager = TelemetryManager.get_instance()
+        if telemetry_manager.enabled:
+            callbacks.append(TelemetryCallback())
+
         return callbacks
 
     def get_post_trainer_create_callbacks(self, trainer):

src/axolotl/core/trainers/grpo/trainer.py

@@ -3,7 +3,6 @@
 # pylint: disable=too-many-lines,duplicate-code,protected-access,no-member
 
 import warnings
-from functools import partial
 from typing import Any
 
 import datasets
@@ -59,42 +58,6 @@ class AxolotlGRPOTrainer(
 
     _tag_names = ["trl", "grpo", "axolotl"]
 
-    def get_train_dataloader(self):
-        if self.train_dataset is None:
-            raise ValueError("Trainer: training requires a train_dataset.")
-
-        train_dataset = self.train_dataset
-        data_collator = self.data_collator
-        if isinstance(train_dataset, datasets.Dataset):
-            train_dataset = self._remove_unused_columns(
-                train_dataset, description="training"
-            )
-        else:
-            data_collator = self._get_collator_with_removed_columns(
-                data_collator, description="training"
-            )
-
-        dataloader_params = {
-            "batch_size": self._train_batch_size
-            * self.args.steps_per_generation,  # < this is the change
-            "collate_fn": data_collator,
-            "num_workers": self.args.dataloader_num_workers,
-            "pin_memory": self.args.dataloader_pin_memory,
-            "persistent_workers": self.args.dataloader_persistent_workers,
-        }
-
-        if not isinstance(train_dataset, torch.utils.data.IterableDataset):
-            dataloader_params["sampler"] = self._get_train_sampler()
-            dataloader_params["drop_last"] = self.args.dataloader_drop_last
-            dataloader_params["worker_init_fn"] = partial(
-                seed_worker,
-                num_workers=self.args.dataloader_num_workers,
-                rank=self.args.process_index,
-            )
-            dataloader_params["prefetch_factor"] = self.args.dataloader_prefetch_factor
-
-        return self.accelerator.prepare(DataLoader(train_dataset, **dataloader_params))
-
 
 class AxolotlGRPOSequenceParallelTrainer(AxolotlGRPOTrainer):
     """Extend the base GRPOTrainer for sequence parallelism handling"""

src/axolotl/datasets.py

@@ -64,10 +64,6 @@ class TokenizedPromptDataset(Dataset):
                 desc="Strategy Filtering Rows",
             )
 
-        import ipdb
-
-        ipdb.set_trace()
-
         return dataset.map(
             self.prompt_tokenizer.tokenize_prompt,
             num_proc=num_proc,

src/axolotl/evaluate.py

@@ -11,6 +11,7 @@ from accelerate.logging import get_logger
 from datasets import Dataset
 from transformers.trainer import Trainer
 
+from axolotl.telemetry.errors import send_errors
 from axolotl.train import (
     TrainDatasetMeta,
     setup_model_and_tokenizer,
@@ -63,6 +64,7 @@ def evaluate_dataset(
     return metrics
 
 
+@send_errors
 def evaluate(*, cfg: DictDefault, dataset_meta: TrainDatasetMeta) -> Dict[str, float]:
     """
     Evaluate a model on training and validation datasets.

src/axolotl/loaders/adapter.py

@@ -19,6 +19,7 @@ from peft import (
 from transformers import PreTrainedModel
 
 from axolotl.loaders.utils import get_linear_embedding_layers
+from axolotl.telemetry.errors import send_errors
 from axolotl.utils.dict import DictDefault
 from axolotl.utils.logging import get_logger
 
@@ -162,6 +163,7 @@ def load_lora(
     return model, lora_config
 
 
+@send_errors
 def load_adapter(
     model: PreTrainedModel,
     cfg: DictDefault,

src/axolotl/loaders/model.py

@@ -46,6 +46,7 @@ from axolotl.loaders.utils import (
     load_model_config,
 )
 from axolotl.models.mamba import fix_mamba_attn_for_loss
+from axolotl.telemetry.errors import send_errors
 from axolotl.utils.bench import log_gpu_memory_usage
 from axolotl.utils.dict import DictDefault
 from axolotl.utils.distributed import (
@@ -145,6 +146,7 @@ class ModelLoader:
         """Property that determines if FSDP with QLoRA is enabled."""
         return self.cfg.fsdp and self.cfg.adapter == "qlora"
 
+    @send_errors
     def load(self) -> tuple[PreTrainedModel | PeftModelForCausalLM, PeftConfig | None]:
         """Load and prepare the model with all configurations and patches.
 

src/axolotl/loaders/processor.py

@@ -8,12 +8,14 @@ from transformers import (
     PreTrainedTokenizerBase,
 )
 
+from axolotl.telemetry.errors import send_errors
 from axolotl.utils.dict import DictDefault
 from axolotl.utils.logging import get_logger
 
 LOG = get_logger(__name__)
 
 
+@send_errors
 def load_processor(cfg: DictDefault, tokenizer: PreTrainedTokenizerBase):
     processor_kwargs: dict[str, Any] = {}  # Do we actually need this?
 

src/axolotl/loaders/tokenizer.py

@@ -2,16 +2,8 @@
 
 import json
 import os
-from typing import Any, Dict, List, Optional, Union
 
-import torch
 import transformers
-from huggingface_hub import hf_hub_download
-from mistral_common.protocol.instruct.messages import SystemMessage, UserMessage
-from mistral_common.protocol.instruct.request import ChatCompletionRequest
-from mistral_common.tokens.tokenizers.mistral import (
-    MistralTokenizer,
-)
 from transformers import (
     AddedToken,
     AutoTokenizer,
@@ -20,6 +12,7 @@ from transformers import (
 from axolotl.integrations.base import PluginManager
 from axolotl.loaders.utils import get_linear_embedding_layers, load_model_config
 from axolotl.prompt_tokenizers import LLAMA_DEFAULT_EOS_TOKEN
+from axolotl.telemetry.errors import send_errors
 from axolotl.utils.chat_templates import get_chat_template_from_config
 from axolotl.utils.distributed import (
     barrier,
@@ -31,622 +24,240 @@ from axolotl.utils.logging import get_logger
 LOG = get_logger(__name__)
 PLUGIN_MANAGER = PluginManager.get_instance()
 
-# Constants
-LLAMA_TOKENIZER_CLASSES = {
-    "LlamaTokenizer",
-    "LlamaTokenizerFast",
-    "CodeLlamaTokenizer",
-    "CodeLlamaTokenizerFast",
-}
 
-FAST_LLAMA_TOKENIZER_CLASSES = {"LlamaTokenizerFast", "CodeLlamaTokenizerFast"}
-
-QWEN_DEFAULT_TOKEN = "<|endoftext|>"
-GPTNEOX_PAD_TOKEN = "[PAD]"
-CHATML_DEFAULT_SYSTEM_MESSAGE = "You are a helpful assistant."
-
-
-class MistralTokenizerWrapper:
-    """
-    Wrapper to make MistralTokenizer compatible with Hugging Face tokenizer interface.
-    This provides a bridge between Mistral's native tokenizer and axolotl's expectations.
+def modify_tokenizer_files(
+    tokenizer_path: str, token_mappings: dict[int, str], output_dir: str
+) -> str:
     """
+    Modify tokenizer files to replace added_tokens strings, save to output directory,
+    and return the path to the modified tokenizer.
 
-    def __init__(self, mistral_tokenizer: "MistralTokenizer", model_id: str):
-        self.mistral_tokenizer = mistral_tokenizer
-        self.model_id = model_id
-        self._system_prompt = None
-        self.padding_side = "right"  # Default padding side
-        self.chat_template = None
-
-        # Cache token IDs by inspecting the actual tokenizer
-        self._token_ids = self._discover_token_ids()
-
-        # Try to load system prompt if available
-        try:
-            self._system_prompt = self._load_system_prompt(
-                model_id, "SYSTEM_PROMPT.txt"
-            )
-        except Exception as e:
-            LOG.debug(f"Could not load system prompt: {e}")
-
-    def _discover_token_ids(self) -> Dict[str, int]:
-        """Discover the actual token IDs used by this Mistral tokenizer."""
-        token_ids = {}
-
-        try:
-            if hasattr(self.mistral_tokenizer, "instruct_tokenizer"):
-                instruct_tokenizer = self.mistral_tokenizer.instruct_tokenizer
-
-                # Get BOS token ID from instruct_tokenizer
-                token_ids["bos_token_id"] = getattr(instruct_tokenizer, "BOS", 1)
-
-                # Get token IDs from the underlying Tekkenizer
-                if hasattr(instruct_tokenizer, "tokenizer"):
-                    tekkenizer = instruct_tokenizer.tokenizer
-
-                    # Get BOS ID from tekkenizer (should match instruct_tokenizer.BOS)
-                    if hasattr(tekkenizer, "bos_id"):
-                        token_ids["bos_token_id"] = tekkenizer.bos_id
-
-                    # Get vocab size to help find EOS token
-                    vocab_size = getattr(tekkenizer, "_vocab_size", None)
-
-                    # Check special tokens
-                    if hasattr(tekkenizer, "_all_special_tokens"):
-                        special_tokens = tekkenizer._all_special_tokens
-                        keys = (
-                            list(special_tokens.keys())
-                            if hasattr(special_tokens, "keys")
-                            else special_tokens
-                        )
-                        LOG.debug(f"Special tokens available: {keys}")
-
-                        # Try to find EOS token in special tokens
-                        if hasattr(special_tokens, "get"):
-                            # Common EOS token patterns
-                            for eos_pattern in ["</s>", "<|endoftext|>", "eos", "EOS"]:
-                                if eos_pattern in special_tokens:
-                                    token_ids["eos_token_id"] = special_tokens[
-                                        eos_pattern
-                                    ]
-                                    break
-
-                    # Check special tokens reverse vocab
-                    if hasattr(tekkenizer, "_special_tokens_reverse_vocab"):
-                        reverse_vocab = tekkenizer._special_tokens_reverse_vocab
-                        LOG.debug(f"Reverse special tokens: {reverse_vocab}")
-
-                        # Look for common special token IDs
-                        for token_id, token_str in reverse_vocab.items():
-                            if token_str in ["</s>", "<|endoftext|>"]:
-                                token_ids["eos_token_id"] = token_id
-                            elif token_str in ["<unk>", "<UNK>"]:
-                                token_ids["unk_token_id"] = token_id
-
-                    # If we have vocab_size, EOS is often vocab_size - 1 or similar
-                    if "eos_token_id" not in token_ids and vocab_size:
-                        # Common patterns: EOS could be 2, vocab_size-1, or other values
-                        # Let's try a safer approach by checking what tokens decode to
-                        for candidate_id in [2, vocab_size - 1, vocab_size - 2]:
-                            try:
-                                # Try to decode and see if it looks like EOS
-                                decoded = tekkenizer.decode([candidate_id])
-                                if decoded in ["</s>", "<|endoftext|>", ""]:
-                                    token_ids["eos_token_id"] = candidate_id
-                                    break
-                            except Exception:
-                                continue
-
-        except Exception as e:
-            LOG.debug(f"Could not discover token IDs: {e}")
-
-        # Set reasonable defaults for any missing token IDs
-        token_ids.setdefault("bos_token_id", 1)
-        token_ids.setdefault("eos_token_id", 2)
-        token_ids.setdefault("unk_token_id", 0)
-        token_ids.setdefault(
-            "pad_token_id", token_ids["eos_token_id"]
-        )  # Use EOS as pad
-
-        LOG.info(f"Discovered Mistral token IDs: {token_ids}")
-        return token_ids
-
-    def _load_system_prompt(self, repo_id: str, filename: str) -> str:
-        """Load system prompt from HuggingFace Hub"""
-        file_path = hf_hub_download(repo_id=repo_id, filename=filename)
-        with open(file_path, "r") as file:
-            return file.read()
-
-    def encode(self, text: str, add_special_tokens: bool = True, **kwargs) -> List[int]:
-        """Encode text to token IDs"""
-        if isinstance(text, str):
-            # For simple string encoding, create a user message
-            messages = []
-            if self._system_prompt and add_special_tokens:
-                messages.append(SystemMessage(content=self._system_prompt))
-            messages.append(UserMessage(content=text))
-
-            tokenized = self.mistral_tokenizer.encode_chat_completion(
-                ChatCompletionRequest(messages=messages)
-            )
-            return tokenized.tokens
-        else:
-            raise ValueError("MistralTokenizer wrapper only supports string input")
-
-    def decode(
-        self,
-        token_ids: Union[List[int], torch.Tensor],
-        skip_special_tokens: bool = True,
-    ) -> str:
-        """Decode token IDs to text"""
-        if isinstance(token_ids, torch.Tensor):
-            token_ids = token_ids.tolist()
-        return self.mistral_tokenizer.decode(token_ids)
-
-    def __call__(self, text: str, **kwargs):
-        """Make the tokenizer callable like HF tokenizers"""
-        tokens = self.encode(text, **kwargs)
-        return {"input_ids": torch.tensor([tokens])}
-
-    @property
-    def eos_token_id(self):
-        return self._token_ids["eos_token_id"]
-
-    @property
-    def bos_token_id(self):
-        return self._token_ids["bos_token_id"]
-
-    @property
-    def pad_token_id(self):
-        return self._token_ids["pad_token_id"]
-
-    @property
-    def unk_token_id(self):
-        return self._token_ids["unk_token_id"]
-
-    @property
-    def eos_token(self):
-        return "</s>"  # Standard Mistral EOS token
-
-    @property
-    def bos_token(self):
-        return "<s>"  # Standard Mistral BOS token
-
-    @property
-    def pad_token(self):
-        return self.eos_token  # Use EOS as pad token
-
-    @property
-    def unk_token(self):
-        return "<unk>"  # Standard UNK token
-
-    @property
-    def __class__(self):
-        # Create a mock class for compatibility checks
-        class MistralTokenizerWrapperClass:
-            __name__ = "MistralTokenizerWrapper"
-
-        return MistralTokenizerWrapperClass
-
-    def add_special_tokens(self, special_tokens_dict: Dict[str, str]) -> int:
-        """Placeholder for special token addition - Mistral tokenizer handles this internally"""
-        LOG.warning(
-            "add_special_tokens called on MistralTokenizer wrapper - this is handled internally"
-        )
-        return 0
-
-    def add_tokens(self, tokens) -> int:
-        """Placeholder for token addition - Mistral tokenizer handles this internally"""
-        LOG.warning(
-            "add_tokens called on MistralTokenizer wrapper - this is handled internally"
-        )
-        return 0
-
-
-class TokenizerFileModifier:
-    """Handles modification of tokenizer files for token overrides."""
-
-    def __init__(
-        self, tokenizer_path: str, token_mappings: Dict[int, str], output_dir: str
-    ):
-        self.tokenizer_path = tokenizer_path
-        self.token_mappings = token_mappings
-        self.output_dir = output_dir
-        self.tokenizer_dir = os.path.join(output_dir, "tokenizer")
-
-    def modify_and_save(self) -> str:
-        """Modify tokenizer files and return path to modified tokenizer."""
-        os.makedirs(self.tokenizer_dir, exist_ok=True)
-
-        if is_local_main_process():
-            self._perform_modifications()
-        barrier()
-
-        return self.tokenizer_dir
-
-    def _perform_modifications(self):
-        """Perform the actual file modifications."""
-        # Load and save tokenizer to output directory
-        temp_tokenizer = AutoTokenizer.from_pretrained(
-            self.tokenizer_path, use_fast=True
-        )
-        temp_tokenizer.save_pretrained(self.tokenizer_dir)
-
-        # Convert token mappings to proper format
-        token_id_mappings = {
-            int(token_id): new_value
-            for token_id, new_value in self.token_mappings.items()
-        }
-
-        # Update both tokenizer files
-        self._update_tokenizer_config(token_id_mappings)
-        self._update_tokenizer_json(token_id_mappings)
-
-    def _update_tokenizer_config(self, token_id_mappings: Dict[int, str]):
-        """Update tokenizer_config.json with new token mappings."""
-        config_path = os.path.join(self.tokenizer_dir, "tokenizer_config.json")
-        if not os.path.exists(config_path):
-            return
-
-        with open(config_path, "r", encoding="utf-8") as f:
-            config_data = json.load(f)
-
-        if "added_tokens_decoder" in config_data:
-            self._update_added_tokens_decoder(config_data, token_id_mappings)
-
-        with open(config_path, "w", encoding="utf-8") as f:
-            json.dump(config_data, f, indent=2)
-
-    def _update_added_tokens_decoder(
-        self, config_data: Dict, token_id_mappings: Dict[int, str]
-    ):
-        """Update the added_tokens_decoder section."""
-        for token_id, new_value in token_id_mappings.items():
-            token_id_str = str(token_id)
-            if token_id_str in config_data["added_tokens_decoder"]:
-                config_data["added_tokens_decoder"][token_id_str]["content"] = new_value
-            else:
-                raise ValueError(
-                    f"Token ID {token_id_str} not found in added_tokens_decoder"
-                )
-
-    def _update_tokenizer_json(self, token_id_mappings: Dict[int, str]):
-        """Update tokenizer.json with new token mappings."""
-        tokenizer_json_path = os.path.join(self.tokenizer_dir, "tokenizer.json")
-        if not os.path.exists(tokenizer_json_path):
-            return
-
-        with open(tokenizer_json_path, "r", encoding="utf-8") as f:
-            tokenizer_data = json.load(f)
-
-        self._update_added_tokens_list(tokenizer_data, token_id_mappings)
-        self._update_vocab_mappings(tokenizer_data, token_id_mappings)
-
-        with open(tokenizer_json_path, "w", encoding="utf-8") as f:
-            json.dump(tokenizer_data, f, indent=2)
-
-    def _update_added_tokens_list(
-        self, tokenizer_data: Dict, token_id_mappings: Dict[int, str]
-    ):
-        """Update the added_tokens list in tokenizer.json."""
-        if "added_tokens" not in tokenizer_data:
-            return
-
-        for token_id, new_value in token_id_mappings.items():
-            for i, token_entry in enumerate(tokenizer_data["added_tokens"]):
-                if token_entry["id"] == token_id:
-                    tokenizer_data["added_tokens"][i]["content"] = new_value
-                    break
-            else:
-                raise ValueError(f"Token ID {token_id} not found in added_tokens")
-
-    def _update_vocab_mappings(
-        self, tokenizer_data: Dict, token_id_mappings: Dict[int, str]
-    ):
-        """Update vocab mappings in tokenizer.json."""
-        if not (tokenizer_data.get("model") and tokenizer_data["model"].get("vocab")):
-            return
-
-        vocab = tokenizer_data["model"]["vocab"]
-        for token_id, new_value in token_id_mappings.items():
-            # Find and update the vocab entry
-            for entry_val, entry_id in list(vocab.items()):
-                if entry_id == token_id:
-                    del vocab[entry_val]
-                    vocab[new_value] = token_id
-                    break
-
-
-class TokenizerConfiguration:
-    """Handles tokenizer configuration and initialization."""
-
-    def __init__(self, cfg):
-        self.cfg = cfg
-        self.model_config = load_model_config(cfg)
-
-    def load_mistral_tokenizer(self) -> MistralTokenizerWrapper:
-        """Load Mistral tokenizer from model configuration."""
-        # Instantiate Mistral tokenizer
-        model_id = self.cfg.base_model
-        mistral_tokenizer = MistralTokenizer.from_hf_hub(model_id)
-
-        # Wrap it for compatibility
-        tokenizer = MistralTokenizerWrapper(mistral_tokenizer, model_id)
-        LOG.info(f"Loaded Mistral tokenizer for model: {model_id}")
-
-        return tokenizer
-
-    def get_tokenizer_class(self):
-        """Get the appropriate tokenizer class."""
-        if self.cfg.tokenizer_type:
-            return getattr(transformers, self.cfg.tokenizer_type)
-        return AutoTokenizer
-
-    def get_tokenizer_kwargs(self) -> Dict[str, Any]:
-        """Build tokenizer initialization kwargs."""
-        kwargs = {}
-        if self.cfg.tokenizer_legacy is not None:
-            kwargs["legacy"] = self.cfg.tokenizer_legacy
-        return kwargs
-
-    def get_tokenizer_path(self) -> str:
-        """Get the tokenizer path, applying overrides if needed."""
-        tokenizer_path = self.cfg.tokenizer_config
-
-        if self.cfg.added_tokens_overrides:
-            modifier = TokenizerFileModifier(
-                tokenizer_path, self.cfg.added_tokens_overrides, self.cfg.output_dir
-            )
-            tokenizer_path = modifier.modify_and_save()
-
-        return tokenizer_path
-
-    def should_use_fast_tokenizer(self) -> bool:
-        """Determine if fast tokenizer should be used."""
-        return (
-            self.cfg.tokenizer_use_fast
-            if self.cfg.tokenizer_use_fast is not None
-            else True
-        )
-
-
-class TokenizerPostProcessor:
-    """Handles post-processing configuration of loaded tokenizers."""
-
-    def __init__(self, tokenizer, cfg):
-        self.tokenizer = tokenizer
-        self.cfg = cfg
-        self.model_config = load_model_config(cfg)
-
-    def apply_all_configurations(self):
-        """Apply all post-processing configurations to the tokenizer."""
-        # Skip most configurations for Mistral wrapper
-        if isinstance(self.tokenizer, MistralTokenizerWrapper):
-            self._configure_mistral_wrapper()
-            return
-
-        self._configure_padding_token()
-        self._configure_gptneox_settings()
-        self._configure_mistral_padding()
-        self._configure_qwen_tokens()
-        self._add_special_tokens()
-        self._add_regular_tokens()
-        self._configure_chat_template()
-
-    def _configure_mistral_wrapper(self):
-        """Apply limited configurations for Mistral wrapper."""
-        # Set padding side if needed
-        if (
-            self.cfg.is_mistral_derived_model
-            and self.cfg.flash_attention
-            and not self.cfg.sample_packing
-        ):
-            self.tokenizer.padding_side = "left"
-
-        # Configure chat template for Mistral
-        self._configure_chat_template()
-
-    def _configure_padding_token(self):
-        """Configure padding token for Llama-based tokenizers."""
-        if (
-            self.tokenizer.__class__.__name__ in LLAMA_TOKENIZER_CLASSES
-            and hasattr(self.tokenizer, "pad_token")
-            and not self.tokenizer.pad_token
-        ):
-            self.tokenizer.pad_token = LLAMA_DEFAULT_EOS_TOKEN
-
-    def _configure_gptneox_settings(self):
-        """Configure GPTNeoX-specific settings."""
-        if self.tokenizer.__class__.__name__ == "GPTNeoXTokenizerFast":
-            self.tokenizer.add_special_tokens({"pad_token": GPTNEOX_PAD_TOKEN})
-            os.environ["TOKENIZERS_PARALLELISM"] = "false"
-
-    def _configure_mistral_padding(self):
-        """Configure left padding for Mistral models with Flash Attention."""
-        if (
-            self.cfg.is_mistral_derived_model
-            and self.cfg.flash_attention
-            and not self.cfg.sample_packing
-        ):
-            self.tokenizer.padding_side = "left"
-
-    def _configure_qwen_tokens(self):
-        """Configure special tokens for Qwen models."""
-        if not self.cfg.is_qwen_derived_model:
-            return
-
-        # Set token IDs
-        token_id_attributes = [
-            "bos_token_id",
-            "eos_token_id",
-            "pad_token_id",
-            "unk_token_id",
-        ]
-        for attr_name in token_id_attributes:
-            if getattr(self.tokenizer, attr_name) is None:
-                setattr(self.tokenizer, attr_name, self.tokenizer.eod_id)
-
-        # Set token strings
-        token_name_attributes = ["bos_token", "eos_token", "pad_token", "unk_token"]
-        for attr_name in token_name_attributes:
-            if getattr(self.tokenizer, attr_name) is None:
-                setattr(self.tokenizer, attr_name, QWEN_DEFAULT_TOKEN)
-
-    def _add_special_tokens(self):
-        """Add special tokens from configuration."""
-        if not self.cfg.special_tokens:
-            return
-
-        special_tokens_dict = self.cfg.special_tokens.to_dict()
-        additional_special_tokens = special_tokens_dict.pop(
-            "additional_special_tokens", None
-        )
-
-        self._validate_and_add_special_tokens(special_tokens_dict)
-        self._update_post_processor_if_needed(special_tokens_dict)
-        self._add_additional_special_tokens_if_present(additional_special_tokens)
-
-    def _validate_and_add_special_tokens(self, special_tokens: Dict[str, str]):
-        """Validate special tokens for adapter training and add them."""
-        lora_modules_to_save = get_linear_embedding_layers(self.model_config.model_type)
-
-        for key, value in special_tokens.items():
-            self._validate_token_for_adapter(key, value, lora_modules_to_save)
-            self.tokenizer.add_special_tokens(
-                {key: AddedToken(value, rstrip=False, lstrip=False, normalized=False)}
-            )
-
-    def _validate_token_for_adapter(
-        self, key: str, value: str, lora_modules_to_save: List[str]
-    ):
-        """Validate a single token for adapter training requirements."""
-        if not self._should_validate_token_for_adapter(
-            key, value, lora_modules_to_save
-        ):
-            return
-
-        modules_str = ", ".join(f"`{x}`" for x in lora_modules_to_save)
-        raise ValueError(
-            f"Please set lora_modules_to_save to [{modules_str}] "
-            f"when using an adapter and changing the special tokens."
-        )
-
-    def _should_validate_token_for_adapter(
-        self, key: str, value: str, lora_modules_to_save: List[str]
-    ) -> bool:
-        """Check if token should be validated for adapter configuration."""
-        if key == "pad_token" or not self.cfg.adapter:
-            return False
-
-        current_token = getattr(self.tokenizer, key)
-        token_changed = current_token is None or current_token != value
-        token_is_multi_char = (
-            len(self.tokenizer.encode(value, add_special_tokens=False)) > 2
-        )
-        lora_modules_missing = not self.cfg.lora_modules_to_save or not all(
-            x in self.cfg.lora_modules_to_save for x in lora_modules_to_save
-        )
-
-        return token_changed and token_is_multi_char and lora_modules_missing
-
-    def _update_post_processor_if_needed(self, special_tokens: Dict[str, str]):
-        """Update post processor for Llama tokenizers when BOS/EOS tokens are added."""
-        has_bos_and_eos = (
-            "bos_token" in special_tokens and "eos_token" in special_tokens
-        )
-        is_fast_llama = (
-            self.tokenizer.__class__.__name__ in FAST_LLAMA_TOKENIZER_CLASSES
-        )
-
-        if is_fast_llama and has_bos_and_eos:
-            self.tokenizer.update_post_processor()
-
-    def _add_additional_special_tokens_if_present(
-        self, additional_special_tokens: Optional[List[str]]
-    ):
-        """Add additional special tokens if they exist."""
-        if additional_special_tokens is not None:
-            self.tokenizer.add_special_tokens(
-                {"additional_special_tokens": additional_special_tokens}
-            )
-
-    def _add_regular_tokens(self):
-        """Add regular (non-special) tokens from configuration."""
-        if self.cfg.tokens:
-            self.tokenizer.add_tokens(
-                [
-                    AddedToken(token, rstrip=False, lstrip=False, normalized=False)
-                    for token in self.cfg.tokens
-                ]
-            )
-
-    def _configure_chat_template(self):
-        """Configure chat template if specified."""
-        if not self.cfg.chat_template:
-            LOG.info(
-                "No Chat template selected. Consider adding a chat template for easier inference."
-            )
-            return
-
-        chat_template_string = get_chat_template_from_config(
-            cfg=self.cfg,
-            tokenizer=self.tokenizer,
-        )
-
-        if self._should_replace_default_system_message():
-            chat_template_string = chat_template_string.replace(
-                CHATML_DEFAULT_SYSTEM_MESSAGE, self.cfg.default_system_message
-            )
-
-        self.tokenizer.chat_template = chat_template_string
-
-    def _should_replace_default_system_message(self) -> bool:
-        """Check if default system message should be replaced."""
-        return self.cfg.default_system_message and self.cfg.chat_template == "chatml"
-
-
-def load_tokenizer(cfg):
-    """Load and configure the tokenizer based on the provided config.
-
-    This function handles the complete tokenizer loading pipeline:
-        - Check if Mistral tokenizer should be used
-        - Configure tokenizer parameters and get the appropriate class
-        - Handle token file modifications if needed
-        - Initialize the tokenizer with the correct parameters
-        - Apply all post-processing configurations (padding, special tokens, etc.)
-        - Set up chat templates and logging
+    This only works with reserved tokens that were added to the tokenizer, not tokens
+    already part of the vocab.
 
     Args:
-        cfg: Dictionary mapping `axolotl` config keys to values.
+        tokenizer_path: Path or name of the original tokenizer
+        token_mappings: Dict mapping {token_id (int): new_token_string}
+        output_dir: Directory to save the modified tokenizer
 
     Returns:
-        Fully configured tokenizer instance.
+        Path to the modified tokenizer directory
+
+    Ref: https://github.com/huggingface/transformers/issues/27974#issuecomment-1854188941
     """
-    # Configure tokenizer parameters
-    config = TokenizerConfiguration(cfg)
+    # Create the tokenizer directory in output_dir if it doesn't exist
+    tokenizer_dir = os.path.join(output_dir, "tokenizer")
+    os.makedirs(tokenizer_dir, exist_ok=True)
 
-    # Check if we should use Mistral tokenizer
-    try:
-        tokenizer = config.load_mistral_tokenizer()
-    except:
-        # Standard tokenizer loading
-        tokenizer_cls = config.get_tokenizer_class()
-        tokenizer_path = config.get_tokenizer_path()
-        use_fast = config.should_use_fast_tokenizer()
-        tokenizer_kwargs = config.get_tokenizer_kwargs()
+    if is_local_main_process():  # pylint: disable=too-many-nested-blocks
+        # Load the tokenizer
+        temp_tokenizer = AutoTokenizer.from_pretrained(tokenizer_path, use_fast=True)
 
-        # Initialize the tokenizer
-        tokenizer = tokenizer_cls.from_pretrained(
-            tokenizer_path,
-            trust_remote_code=cfg.trust_remote_code or False,
-            use_fast=use_fast,
-            **tokenizer_kwargs,
+        # Save the tokenizer to the output directory
+        temp_tokenizer.save_pretrained(tokenizer_dir)
+
+        # Get the token IDs and map them to their new values
+        token_id_mappings = {
+            int(token_id): new_value for token_id, new_value in token_mappings.items()
+        }
+
+        # 1. Update tokenizer_config.json - added_tokens_decoder
+        config_path = os.path.join(tokenizer_dir, "tokenizer_config.json")
+        if os.path.exists(config_path):
+            with open(config_path, "r", encoding="utf-8") as f:
+                config_data = json.load(f)
+
+            # Update added_tokens_decoder
+            if "added_tokens_decoder" in config_data:
+                for token_id, new_value in token_id_mappings.items():
+                    token_id_str = str(token_id)
+                    if token_id_str in config_data["added_tokens_decoder"]:
+                        config_data["added_tokens_decoder"][token_id_str][
+                            "content"
+                        ] = new_value
+                    else:
+                        raise ValueError(
+                            f"Token ID {token_id_str} not found in added_tokens_decoder"
+                        )
+
+            # Write the updated config back
+            with open(config_path, "w", encoding="utf-8") as f:
+                json.dump(config_data, f, indent=2)
+
+        # 2. Update tokenizer.json - added_tokens
+        tokenizer_path = os.path.join(tokenizer_dir, "tokenizer.json")
+        if os.path.exists(tokenizer_path):
+            with open(tokenizer_path, "r", encoding="utf-8") as f:
+                tokenizer_data = json.load(f)
+
+            # Update added_tokens
+            if "added_tokens" in tokenizer_data:
+                for token_id, new_value in token_id_mappings.items():
+                    for i, token_entry in enumerate(tokenizer_data["added_tokens"]):
+                        if token_entry["id"] == token_id:
+                            tokenizer_data["added_tokens"][i]["content"] = new_value
+                            break
+                    else:
+                        # Reaching this section means the token_id was not found in tokenizer.json added_tokens
+                        raise ValueError(
+                            f"Token ID {token_id} not found in added_tokens"
+                        )
+            if "model" in tokenizer_data and "vocab" in tokenizer_data["model"]:
+                for token_id, new_value in token_id_mappings.items():
+                    for entry_val, entry_id in tokenizer_data["model"]["vocab"].items():
+                        if entry_id == token_id:
+                            del tokenizer_data["model"]["vocab"][entry_val]
+                            tokenizer_data["model"]["vocab"][new_value] = token_id
+                            break
+
+            # Write the updated tokenizer data back
+            with open(tokenizer_path, "w", encoding="utf-8") as f:
+                json.dump(tokenizer_data, f, indent=2)
+
+    barrier()
+    return tokenizer_dir
+
+
+@send_errors
+def load_tokenizer(cfg):
+    """Load and configure the tokenizer based on the provided config."""
+    model_config = load_model_config(cfg)
+    tokenizer_kwargs = {}
+    use_fast = True  # this is the default
+
+    if cfg.tokenizer_use_fast is not None:
+        use_fast = cfg.tokenizer_use_fast
+    if cfg.tokenizer_legacy is not None:
+        # True is the default w/ https://github.com/huggingface/transformers/pull/25224
+        tokenizer_kwargs["legacy"] = cfg.tokenizer_legacy
+
+    tokenizer_cls = AutoTokenizer
+    if cfg.tokenizer_type:
+        tokenizer_cls = getattr(transformers, cfg.tokenizer_type)
+
+    # Set base tokenizer path
+    tokenizer_path = cfg.tokenizer_config
+
+    # Apply token string overrides if specified
+    if cfg.added_tokens_overrides:
+        # Modify tokenizer files and get path to modified tokenizer
+        tokenizer_path = modify_tokenizer_files(
+            tokenizer_path, cfg.added_tokens_overrides, output_dir=cfg.output_dir
         )
 
-    # Apply all post-processing configurations
-    post_processor = TokenizerPostProcessor(tokenizer, cfg)
-    post_processor.apply_all_configurations()
+    tokenizer = tokenizer_cls.from_pretrained(
+        tokenizer_path,
+        trust_remote_code=cfg.trust_remote_code or False,
+        use_fast=use_fast,
+        **tokenizer_kwargs,
+    )
+
+    if (
+        tokenizer.__class__.__name__
+        in [
+            "LlamaTokenizer",
+            "LlamaTokenizerFast",
+            "CodeLlamaTokenizer",
+            "CodeLlamaTokenizerFast",
+        ]
+        and hasattr(tokenizer, "pad_token")
+        and not tokenizer.pad_token
+    ):
+        # set a pad_token, but use eos_token so we don't add a new token
+        tokenizer.pad_token = LLAMA_DEFAULT_EOS_TOKEN
+
+    if tokenizer.__class__.__name__ == "GPTNeoXTokenizerFast":
+        tokenizer.add_special_tokens({"pad_token": "[PAD]"})
+        os.environ["TOKENIZERS_PARALLELISM"] = "false"
+
+    # Mistral's official FA implementation requires left padding
+    if cfg.is_mistral_derived_model and cfg.flash_attention and not cfg.sample_packing:
+        tokenizer.padding_side = "left"
+
+    # Qwen base only has single token, so we need to set the special tokens
+    if cfg.is_qwen_derived_model:
+        token_ids = ["bos_token_id", "eos_token_id", "pad_token_id", "unk_token_id"]
+        for attr_name in token_ids:
+            if getattr(tokenizer, attr_name) is None:
+                setattr(tokenizer, attr_name, tokenizer.eod_id)
+
+        token_names = ["bos_token", "eos_token", "pad_token", "unk_token"]
+        for attr_name in token_names:
+            if getattr(tokenizer, attr_name) is None:
+                setattr(tokenizer, attr_name, "<|endoftext|>")
+
+    additional_special_tokens = None
+    if cfg.special_tokens:
+        special_tokens = cfg.special_tokens.to_dict()
+        additional_special_tokens = special_tokens.pop(
+            "additional_special_tokens", None
+        )
+        lora_modules_to_save = get_linear_embedding_layers(model_config.model_type)
+        for k, val in special_tokens.items():
+            # check if new special token is not already in tokenizer and
+            # is adapter training to make sure lora_modules_to_save is set
+            # pylint: disable=too-many-boolean-expressions
+            if (
+                (getattr(tokenizer, k) is None or getattr(tokenizer, k) != val)
+                and (len(tokenizer.encode(val, add_special_tokens=False)) > 2)
+                and cfg.adapter
+                and (
+                    not cfg.lora_modules_to_save
+                    or not all(
+                        x in cfg.lora_modules_to_save for x in lora_modules_to_save
+                    )
+                )
+                and k != "pad_token"
+            ):
+                lora_modules_to_save = ", ".join(
+                    [f"`{x}`" for x in lora_modules_to_save]
+                )
+                raise ValueError(
+                    f"Please set lora_modules_to_save to [{lora_modules_to_save}] when using an adapter and changing the special tokens."
+                )
+
+            tokenizer.add_special_tokens(
+                {k: AddedToken(val, rstrip=False, lstrip=False, normalized=False)}
+            )
+
+        # If we add bos_token and eos_token, we need to update the post processor to
+        # handle them correctly.
+        # https://github.com/huggingface/transformers/pull/24132
+        bos_or_eos_in_special_tokens = (
+            "bos_token" in cfg.special_tokens and "eos_token" in cfg.special_tokens
+        )
+        if (
+            tokenizer.__class__.__name__
+            in (
+                "LlamaTokenizerFast",
+                "CodeLlamaTokenizerFast",
+            )
+            and bos_or_eos_in_special_tokens
+        ):
+            tokenizer.update_post_processor()
+
+    if cfg.tokens:
+        tokenizer.add_tokens(
+            [
+                AddedToken(token, rstrip=False, lstrip=False, normalized=False)
+                for token in cfg.tokens
+            ]
+        )
+
+    # Additional special tokens are a List, and need to be treated differently than regular special
+    # tokens. We add them after we have called `add_tokens` in case these additional special tokens
+    # are new tokens.
+    #
+    # Usage:
+    #
+    # ```py
+    # special_tokens:
+    #   additional_special_tokens: ["<|im_start|>", "<|im_end|>"]
+    # ```
+    if additional_special_tokens is not None:
+        tokenizer.add_special_tokens(
+            {"additional_special_tokens": additional_special_tokens}
+        )
 
     if is_main_process(use_environ=True):
         LOG.debug(f"EOS: {tokenizer.eos_token_id} / {tokenizer.eos_token}")
@@ -654,4 +265,19 @@ def load_tokenizer(cfg):
         LOG.debug(f"PAD: {tokenizer.pad_token_id} / {tokenizer.pad_token}")
         LOG.debug(f"UNK: {tokenizer.unk_token_id} / {tokenizer.unk_token}")
 
+    if cfg.chat_template:
+        chat_template_string = get_chat_template_from_config(
+            cfg=cfg,
+            tokenizer=tokenizer,
+        )
+        if cfg.default_system_message and cfg.chat_template == "chatml":
+            chat_template_string = chat_template_string.replace(
+                "You are a helpful assistant.", cfg.default_system_message
+            )
+
+        tokenizer.chat_template = chat_template_string
+    else:
+        LOG.info(
+            "No Chat template selected. Consider adding a chat template for easier inference."
+        )
     return tokenizer

src/axolotl/prompt_tokenizers.py

@@ -67,10 +67,6 @@ class PromptTokenizingStrategy(abc.ABC):
             LOG.warning("Empty text requested for tokenization.")
             return empty
 
-        import ipdb
-
-        ipdb.set_trace()
-
         result = self.tokenizer(
             prompt,
             truncation=True,

src/axolotl/telemetry/callbacks.py

@@ -0,0 +1,164 @@
+"""Trainer callbacks for reporting runtime metrics at regular intervals."""
+
+import logging
+import time
+
+from transformers import (
+    TrainerCallback,
+    TrainerControl,
+    TrainerState,
+    TrainingArguments,
+)
+
+from axolotl.telemetry.manager import TelemetryManager
+from axolotl.telemetry.runtime_metrics import RuntimeMetricsTracker
+
+LOG = logging.getLogger(__name__)
+
+TIME_SINCE_LAST = 30
+
+
+class TelemetryCallback(TrainerCallback):
+    """
+    Trainer callback for tracking and reporting runtime metrics.
+
+    This callback tracks training progress, runtime, and memory usage,
+    sending telemetry at configurable intervals.
+    """
+
+    report_interval_steps: int = 100
+
+    def __init__(self):
+        """Initialize the metrics callback."""
+        self.tracker = RuntimeMetricsTracker()
+        self.telemetry_manager = TelemetryManager.get_instance()
+        self.current_epoch = -1
+        self.start_time = time.time()
+        self.last_report_time = None
+        self.last_report_step = 0
+
+    # pylint: disable=unused-argument
+    def on_train_begin(
+        self,
+        args: TrainingArguments,
+        state: TrainerState,
+        control: TrainerControl,
+        **kwargs,
+    ):
+        """Handle training start."""
+        self.telemetry_manager.send_event(event_type="train-start")
+
+    # pylint: disable=unused-argument
+    def on_train_end(
+        self,
+        args: TrainingArguments,
+        state: TrainerState,
+        control: TrainerControl,
+        **kwargs,
+    ):
+        """Handle training end."""
+        # Send training completion event
+        self.telemetry_manager.send_event(
+            event_type="train-end",
+            properties=self._extract_last_metrics(state)
+            | self.tracker.metrics.to_dict(),
+        )
+
+    # pylint: disable=unused-argument
+    def on_epoch_begin(
+        self,
+        args: TrainingArguments,
+        state: TrainerState,
+        control: TrainerControl,
+        **kwargs,
+    ):
+        """Handle epoch start."""
+        self.current_epoch += 1
+        self.tracker.start_epoch(self.current_epoch)
+
+    # pylint: disable=unused-argument
+    def on_epoch_end(
+        self,
+        args: TrainingArguments,
+        state: TrainerState,
+        control: TrainerControl,
+        **kwargs,
+    ):
+        """Handle epoch end."""
+        self.tracker.end_epoch(self.current_epoch)
+
+    # pylint: disable=unused-argument
+    def on_step_end(
+        self,
+        args: TrainingArguments,
+        state: TrainerState,
+        control: TrainerControl,
+        **kwargs,
+    ):
+        """Handle step end."""
+        step = state.global_step
+        self.tracker.update_step(step)
+
+        # Check if we should report metrics
+        should_report = (
+            step % self.report_interval_steps == 0
+            or step == 1  # Always report first step
+            or step - self.last_report_step >= self.report_interval_steps
+        )
+
+        if should_report:
+            current_time = time.time()
+            if self.last_report_time is not None:
+                time_since_last_report = current_time - self.last_report_time
+            else:
+                time_since_last_report = current_time - self.start_time
+            steps_since_last_report = step - self.last_report_step
+
+            # Only report if enough time has passed
+            if (
+                step == 1
+                or time_since_last_report >= TIME_SINCE_LAST
+                or steps_since_last_report >= self.report_interval_steps
+            ):
+                # Calculate steps per second for this interval
+                if time_since_last_report > 0 and steps_since_last_report > 0:
+                    steps_per_second = steps_since_last_report / time_since_last_report
+                else:
+                    steps_per_second = 0
+
+                # Update memory metrics
+                self.tracker.update_memory_metrics()
+
+                # Prepare metrics to report
+                metrics = self._extract_last_metrics(state) | {
+                    "step": step,
+                    "epoch": self.current_epoch,
+                    "progress": state.epoch,  # Fractional epoch progress
+                    "steps_per_second": steps_per_second,
+                    "elapsed_time": current_time - self.start_time,
+                    "time_since_last_report": time_since_last_report,
+                }
+
+                # Add memory metrics
+                memory_metrics = self.tracker.get_memory_metrics()
+                metrics.update({"memory": memory_metrics})
+
+                # Send telemetry
+                self.telemetry_manager.send_event(
+                    event_type="train-progress", properties=metrics
+                )
+
+                # Update last report time and step
+                self.last_report_time = current_time
+                self.last_report_step = step
+
+    def _extract_last_metrics(self, state: TrainerState) -> dict:
+        """Extract last loss and learning_rate from log history."""
+        if not state.log_history:
+            return {"loss": 0, "learning_rate": 0}
+
+        last_log = state.log_history[-1]
+        return {
+            "loss": last_log.get("loss", 0),
+            "learning_rate": last_log.get("learning_rate", 0),
+        }

src/axolotl/telemetry/errors.py

@@ -0,0 +1,160 @@
+"""Telemetry utilities for exception and traceback information."""
+
+import logging
+import os
+import re
+import traceback
+from functools import wraps
+from inspect import getmodule
+from typing import Any, Callable
+
+from axolotl.telemetry.manager import TelemetryManager
+
+LOG = logging.getLogger(__name__)
+
+ERROR_HANDLED = False
+
+
+def sanitize_stack_trace(stack_trace: str) -> str:
+    """
+    Remove personal information from stack trace messages while keeping Python package codepaths.
+
+    This function identifies Python packages by looking for common patterns in virtual environment
+    and site-packages directories, preserving the package path while removing user-specific paths.
+
+    Args:
+        stack_trace: The original stack trace string.
+
+    Returns:
+        A sanitized version of the stack trace with Python package paths preserved.
+    """
+    # Split the stack trace into lines to process each file path separately
+    lines = stack_trace.split("\n")
+    sanitized_lines = []
+
+    # Regular expression to find file paths in the stack trace
+    path_pattern = re.compile(r'(?:File ")(.*?)(?:")')
+
+    # Regular expression to identify paths in site-packages or dist-packages
+    # This matches path segments like "site-packages/package_name" or "dist-packages/package_name"
+    site_packages_pattern = re.compile(
+        r"(?:site-packages|dist-packages)[/\\]([\w\-\.]+)"
+    )
+
+    # Additional common virtual environment patterns
+    venv_lib_pattern = re.compile(
+        r"(?:lib|Lib)[/\\](?:python\d+(?:\.\d+)?[/\\])?(?:site-packages|dist-packages)[/\\]([\w\-\.]+)"
+    )
+
+    for line in lines:
+        # Check if this line contains a file path
+        path_match = path_pattern.search(line)
+
+        if path_match:
+            full_path = path_match.group(1)
+            sanitized_path = ""
+
+            # Try to match site-packages pattern
+            site_packages_match = site_packages_pattern.search(full_path)
+            venv_lib_match = venv_lib_pattern.search(full_path)
+
+            if site_packages_match:
+                # Find the index where the matched pattern starts
+                idx = full_path.find("site-packages")
+                if idx == -1:
+                    idx = full_path.find("dist-packages")
+
+                # Keep from 'site-packages' onward
+                if idx >= 0:
+                    sanitized_path = full_path[idx:]
+            elif venv_lib_match:
+                # For other virtual environment patterns, find the package directory
+                match_idx = venv_lib_match.start(1)
+                if match_idx > 0:
+                    # Keep from the package name onward
+                    package_name = venv_lib_match.group(1)
+                    idx = full_path.rfind(
+                        package_name, 0, match_idx + len(package_name)
+                    )
+                    if idx >= 0:
+                        sanitized_path = full_path[idx:]
+
+            # If we couldn't identify a package pattern but path contains 'axolotl'
+            elif "axolotl" in full_path:
+                idx = full_path.rfind("axolotl")
+                if idx >= 0:
+                    sanitized_path = full_path[idx:]
+
+            # Apply the sanitization to the line
+            if sanitized_path:
+                line = line.replace(full_path, sanitized_path)
+            else:
+                # If we couldn't identify a package pattern, just keep the filename
+                filename = os.path.basename(full_path)
+                if filename:
+                    line = line.replace(full_path, filename)
+                else:
+                    line = line.replace(full_path, "")
+
+        sanitized_lines.append(line)
+
+    return "\n".join(sanitized_lines)
+
+
+def send_errors(func: Callable) -> Callable:
+    """
+    Decorator to send exception info in a function. If an exception is raised, we send
+    telemetry containing the stack trace and error message.
+
+    If an error occurs in a decorated function that is called by another decorated
+    function, we'll only send telemetry corresponding to the lower-level function.
+
+    Args:
+        func: Function to decorate.
+
+    Returns:
+        Decorated function.
+    """
+
+    @wraps(func)
+    def wrapper(*args, **kwargs) -> Any:
+        telemetry_manager = TelemetryManager.get_instance()
+
+        if not telemetry_manager.enabled:
+            return func(*args, **kwargs)
+
+        try:
+            return func(*args, **kwargs)
+        except Exception as exception:
+            # Only track if we're not already handling an error. This prevents us from
+            # capturing an error more than once in nested decorated function calls.
+            global ERROR_HANDLED  # pylint: disable=global-statement
+            if not ERROR_HANDLED:
+                ERROR_HANDLED = True
+
+                # Get function module path
+                module = getmodule(func)
+                module_path = (
+                    f"{module.__name__}.{func.__name__}" if module else func.__name__
+                )
+
+                # Get stack trace
+                stack_trace = "".join(
+                    traceback.format_exception(
+                        type(exception), exception, exception.__traceback__
+                    )
+                )
+                stack_trace = sanitize_stack_trace(stack_trace)
+
+                # Send error telemetry
+                telemetry_manager.send_event(
+                    event_type=f"{module_path}-error",
+                    properties={
+                        "exception": str(exception),
+                        "stack_trace": stack_trace,
+                    },
+                )
+
+            raise
+
+    return wrapper

src/axolotl/telemetry/manager.py

@@ -0,0 +1,417 @@
+"""Telemetry manager and associated utilities."""
+
+import atexit
+import importlib
+import logging
+import os
+import platform
+import time
+import uuid
+from pathlib import Path
+from typing import Any
+
+import posthog
+import psutil
+import torch
+import yaml
+
+LOG = logging.getLogger(__name__)
+
+POSTHOG_HOST = "https://app.posthog.com"
+POSTHOG_WRITE_KEY = "phc_1kUR0o04oJKKTTeSsIz2Mfm5mpiVsQEf2WOlzljMD7y"
+
+OPT_IN_WARNING_SLEEP_SECONDS = 10
+OPT_IN_WARNING = (
+    "\nTelemetry is currently disabled by default. If you'd like to help improve "
+    "Axolotl, consider enabling it by setting AXOLOTL_DO_NOT_TRACK=0 in your environment.\n\n"
+    "Telemetry data helps us understand:\n"
+    "- Which features are most used\n"
+    "- What hardware configurations to prioritize\n"
+    "- Where users encounter errors\n\n"
+    "Personally identifiable information (PII) is not collected.\n\n"
+    "To remove this warning, explicitly set AXOLOTL_DO_NOT_TRACK=0 (enable telemetry) "
+    "or AXOLOTL_DO_NOT_TRACK=1 (explicitly disable telemetry).\n\n"
+    "Note: Telemetry will move to an opt-out in a later release.\n\n"
+    "For details, see: https://axolotl-ai-cloud.github.io/axolotl/docs/telemetry.html\n\n"
+    f"Sleeping for {OPT_IN_WARNING_SLEEP_SECONDS}s..."
+)
+
+WHITELIST_PATH = str(Path(__file__).parent / "whitelist.yaml")
+
+# NOTE: Need to keep these up to date with any config schema changes
+FIELDS_TO_REDACT = {
+    "base_model",
+    "tokenizer_config",
+    "base_model_config",
+    "pretraining_dataset",  # NOTE: this field may be a string or a dictionary
+    "resume_from_checkpoint",
+    "hub_model_id",
+}
+PREFIXES_TO_REDACT = {"wandb_", "comet_", "mlflow_", "gradio_"}
+PATH_INDICATORS = {"path", "dir"}
+
+# pylint: disable=duplicate-code
+RELEVANT_PACKAGES = {
+    "torch",
+    "transformers",
+    "trl",
+    "datasets",
+    "peft",
+    "bitsandbytes",
+    "accelerate",
+    "optimum",
+    "deepspeed",
+    "ray",
+    "axolotl",
+    "triton",
+    "mamba-ssm",
+    "flash-attn",
+    "xformers",
+    "autoawq",
+    "tokenizers",
+    "sentencepiece",
+    "torchao",
+    "lm_eval",
+}
+
+
+def is_main_process() -> bool:
+    """
+    Check whether we're running in the main process.
+
+    Note:
+        We're using this function instead of `torch.utils.distributed.is_main_process`
+        causes issues with DeepSpeed world_size since. This function avoids that issue
+        by checking env vars that are set by various launchers.
+
+    Returns:
+        Whether we're running in the main process.
+    """
+    # If PyTorch distributed is already initialized, use it
+    if torch.distributed.is_initialized():
+        return torch.distributed.get_rank() == 0
+
+    # Otherwise check environment variables for global rank
+    # NOTE: need to verify this in SLURM / OpenMPI environments
+    global_rank = int(
+        os.environ.get(
+            "RANK",
+            os.environ.get(
+                "GLOBAL_RANK",
+                os.environ.get(
+                    "SLURM_PROCID",
+                    os.environ.get(
+                        "OMPI_COMM_WORLD_RANK",
+                        "0",
+                    ),
+                ),
+            ),
+        )
+    )
+
+    return global_rank == 0
+
+
+class TelemetryManager:
+    """Manages telemetry collection and transmission"""
+
+    _instance = None
+    _initialized = False
+
+    def __new__(cls):
+        """
+        Telemetry manager constructor. Creates the singleton instance of this class if
+        it doesn't already exist.
+        """
+        if cls._instance is None:
+            cls._instance = super(TelemetryManager, cls).__new__(cls)
+            cls._instance._initialized = False
+
+        return cls._instance
+
+    def __init__(self):
+        """Telemetry manager initializer"""
+        if self._initialized:
+            return
+
+        self.enabled = self._check_telemetry_enabled()
+
+        if self.enabled:
+            self.run_id = str(uuid.uuid4())
+            self.whitelist = self._load_whitelist()
+
+            try:
+                self.system_info = self._get_system_info()
+            except Exception as e:  # pylint: disable=broad-exception-caught
+                LOG.warning(f"Error during system info collection: {e}")
+                self.system_info = None
+
+            self._init_posthog()
+
+            # Register shutdown method to flush posthog telemetry
+            atexit.register(self.shutdown)
+
+        self._initialized = True
+
+    @classmethod
+    def get_instance(cls) -> "TelemetryManager":
+        if cls._instance is None:
+            cls._instance = TelemetryManager()
+
+        return cls._instance
+
+    def _check_telemetry_enabled(self) -> bool:
+        """
+        Check if telemetry is enabled based on environment variables. We also check
+        whether this is the main process (for the distributed setting and to avoid
+        sending duplicate PostHog events per GPU).
+
+        Note: This is disabled by default on an opt-in basis. Set
+        `AXOLOTL_DO_NOT_TRACK=0` to enable telemetry. We plan to move to an opt-out
+        model in a later release. For more details, see
+        https://axolotl-ai-cloud.github.io/axolotl/docs/telemetry.html.
+
+        Returns:
+            Boolean denoting whether telemetry is enabled or not.
+        """
+        # Parse relevant env vars
+        axolotl_do_not_track = os.getenv("AXOLOTL_DO_NOT_TRACK")
+        do_not_track = os.getenv("DO_NOT_TRACK")
+
+        # Default to disabled (opt-in model for initial release)
+        if axolotl_do_not_track is None or axolotl_do_not_track.lower() not in (
+            "0",
+            "1",
+            "false",
+            "true",
+        ):
+            # Print opt-in info message for main process only
+            if is_main_process():
+                LOG.warning(OPT_IN_WARNING)
+            time.sleep(OPT_IN_WARNING_SLEEP_SECONDS)
+
+            return False
+
+        # Only rank 0 will send telemetry
+        if not is_main_process():
+            return False
+
+        if do_not_track is None:
+            do_not_track = "0"
+
+        # Respect AXOLOTL_DO_NOT_TRACK, DO_NOT_TRACK if enabled
+        enabled = axolotl_do_not_track.lower() not in (
+            "1",
+            "true",
+        ) and do_not_track.lower() not in ("1", "true")
+
+        return enabled
+
+    def _load_whitelist(self) -> dict:
+        """Load HuggingFace Hub organization whitelist"""
+        with open(WHITELIST_PATH, encoding="utf-8") as f:
+            whitelist = yaml.safe_load(f)
+
+            # Send org strings to lowercase since model names are case insensitive
+            whitelist["organizations"] = {
+                org.lower() for org in whitelist["organizations"]
+            }
+
+            return whitelist
+
+    def _is_whitelisted(self, value: str) -> bool:
+        """
+        Check if model / dataset / etc. org is in whitelist.
+
+        Args:
+            value: Value for one of `axolotl.telemetry.manager.FIELDS_WITH_ORGS`
+                ("base_model", etc.).
+
+        Returns:
+            Boolean indicating whitelist membership.
+        """
+        # NOTE: This membership-checking logic can be improved.
+        # What happens when a local model path matches a whitelisted org?
+        parts = value.split("/")
+        if len(parts) < 2:
+            return False
+        org = parts[0]
+        whitelisted = org.lower() in self.whitelist["organizations"]
+
+        return whitelisted
+
+    def _init_posthog(self):
+        """Initialize PostHog client"""
+        posthog.host = POSTHOG_HOST
+        posthog.project_api_key = POSTHOG_WRITE_KEY
+
+    def _redact_paths(self, properties: dict[str, Any]) -> dict[str, Any]:
+        """
+        Redact properties to remove any paths, so as to avoid inadvertently collecting
+        private or personally identifiable information (PII). We also remove
+        information related to Wandb, MLflow, etc. configuration.
+
+        Args:
+            properties: Dictionary of properties to redact.
+
+        Returns:
+            Properties dictionary with redaction applied.
+        """
+        if not properties:
+            return {}
+
+        def redact_value(value: Any, key: str = "") -> Any:
+            """Recursively sanitize values, redacting those with path-like keys"""
+            if isinstance(key, str) and isinstance(value, str):
+                # Other redaction special cases
+                if (
+                    key in FIELDS_TO_REDACT
+                    or any(prefix in key for prefix in PREFIXES_TO_REDACT)
+                    or any(indicator in key.lower() for indicator in PATH_INDICATORS)
+                ):
+                    # Fields with whitelisted orgs don't need to be redacted
+                    if not self._is_whitelisted(value):
+                        return "[REDACTED]"
+
+            # Handle nested values
+            if isinstance(value, dict):
+                return {k: redact_value(v, k) for k, v in value.items()}
+            if isinstance(value, list):
+                return [redact_value(item) for item in value]
+
+            return value
+
+        # Create new dict with redacted values
+        redacted = {k: redact_value(v, k) for k, v in properties.items()}
+
+        return redacted
+
+    def _get_system_info(self) -> dict[str, Any]:
+        """Collect system information for various hardware accelerators"""
+        gpu_info = []
+        accelerator_type = "none"
+
+        # NVIDIA GPUs
+        if torch.cuda.is_available():
+            accelerator_type = "cuda"
+            for i in range(torch.cuda.device_count()):
+                gpu_info.append(
+                    {
+                        "name": torch.cuda.get_device_name(i),
+                        "memory": torch.cuda.get_device_properties(i).total_memory,
+                    }
+                )
+
+        # AMD GPUs
+        elif hasattr(torch, "hip") and torch.hip.is_available():
+            accelerator_type = "hip"
+            for i in range(torch.hip.device_count()):
+                gpu_info.append(
+                    {
+                        "name": torch.hip.get_device_name(i),
+                        "memory": (
+                            torch.hip.get_device_properties(i).total_memory
+                            if hasattr(torch.hip, "get_device_properties")
+                            else None
+                        ),
+                    }
+                )
+
+        # Apple Silicon
+        elif hasattr(torch.backends, "mps") and torch.backends.mps.is_available():
+            accelerator_type = "mps"
+            gpu_info.append(
+                {
+                    "name": "Apple Silicon",
+                    # NOTE: this is memory allocated to this process, not total memory
+                    "memory": torch.mps.driver_allocated_memory(),
+                }
+            )
+
+        # Intel GPUs
+        elif hasattr(torch, "xpu") and torch.xpu.is_available():
+            accelerator_type = "xpu"
+            for i in range(torch.xpu.device_count()):
+                memory = None
+                if hasattr(torch.xpu, "get_device_properties"):
+                    memory = torch.xpu.get_device_properties(i).total_memory
+
+                gpu_info.append(
+                    {
+                        "name": torch.xpu.get_device_name(i),
+                        "memory": memory,
+                    }
+                )
+
+        # NPUs
+        elif hasattr(torch, "npu") and torch.npu.is_available():
+            accelerator_type = "npu"
+            for i in range(torch.npu.device_count()):
+                memory = None
+                if hasattr(torch.npu, "get_device_properties"):
+                    memory = torch.npu.get_device_properties(i).total_memory
+
+                gpu_info.append(
+                    {
+                        "name": torch.npu.get_device_name(i),
+                        "memory": memory,
+                    }
+                )
+
+        # Get relevant package versions
+        installed_packages = {}
+        for package in RELEVANT_PACKAGES:
+            try:
+                version = importlib.metadata.version(package)
+                installed_packages[f"{package}_version"] = version
+            except importlib.metadata.PackageNotFoundError:
+                pass
+
+        return {
+            "os": platform.system(),
+            "python_version": platform.python_version(),
+            "cpu_count": psutil.cpu_count(),
+            "memory_total": psutil.virtual_memory().total,
+            "accelerator_type": accelerator_type,
+            "accelerator_count": len(gpu_info),
+            "accelerator_info": gpu_info,
+            **installed_packages,
+        }
+
+    def send_event(self, event_type: str, properties: dict[str, Any] | None = None):
+        """Send a telemetry event"""
+        if not self.enabled:
+            return
+
+        if properties is None:
+            properties = {}
+
+        # Sanitize properties to remove PII
+        properties = self._redact_paths(properties)
+
+        # Wrap PostHog errors in try / except to not raise errors during Axolotl usage
+        try:
+            # Send event via PostHog
+            posthog.capture(
+                distinct_id=self.run_id,
+                event=event_type,
+                properties=properties,
+                disable_geoip=True,
+            )
+        except Exception as e:  # pylint: disable=broad-exception-caught
+            LOG.warning(f"Failed to send telemetry event: {e}")
+
+        # Additionally, send system info telemetry when loading config.
+        # NOTE: Is this the best place for this?
+        if event_type == "config-loaded":
+            self.send_system_info()
+
+    def send_system_info(self):
+        """Helper method for sending system info"""
+        if self.system_info is not None:
+            self.send_event(event_type="system-info", properties=self.system_info)
+
+    def shutdown(self):
+        """Ensure all queued events are processed before shutdown"""
+        if self.enabled:
+            posthog.flush()

src/axolotl/telemetry/runtime_metrics.py

@@ -0,0 +1,209 @@
+"""Telemetry utilities for runtime and memory metrics."""
+
+import logging
+import time
+from dataclasses import dataclass, field
+from typing import Any
+
+import psutil
+import torch
+
+from axolotl.telemetry.manager import TelemetryManager
+
+LOG = logging.getLogger(__name__)
+
+
+@dataclass
+class RuntimeMetrics:
+    """Container for runtime metrics to be tracked throughout training."""
+
+    # Timing metrics
+    start_time: float
+    epoch_start_times: dict[int, float] = field(init=False)
+    epoch_end_times: dict[int, float] = field(init=False)
+
+    # Memory metrics
+    peak_cpu_memory: int = 0
+    peak_gpu_memory: dict[int, int] = field(init=False)
+
+    # Progress metrics
+    total_steps: int = 0
+    current_epoch: int = 0
+    current_step: int = 0
+
+    def __post_init__(self):
+        """Initialize empty metric mappings."""
+        self.epoch_start_times = {}
+        self.epoch_end_times = {}
+        self.peak_gpu_memory = {}
+
+    @property
+    def elapsed_time(self) -> float:
+        """Calculate total elapsed time in seconds."""
+        return time.time() - self.start_time
+
+    def epoch_time(self, epoch: int) -> float | None:
+        """Calculate time taken for a specific epoch in seconds."""
+        if epoch in self.epoch_start_times and epoch in self.epoch_end_times:
+            return self.epoch_end_times[epoch] - self.epoch_start_times[epoch]
+
+        return None
+
+    def average_epoch_time(self) -> float | None:
+        """Calculate average time per epoch in seconds."""
+        completed_epochs = [
+            epoch for epoch in self.epoch_start_times if epoch in self.epoch_end_times
+        ]
+        if not completed_epochs:
+            return None
+
+        total_time = 0.0
+        for epoch in completed_epochs:
+            epoch_time = self.epoch_time(epoch)
+            if epoch_time is not None:  # Check to avoid mypy warning
+                total_time += epoch_time
+
+        return total_time / len(completed_epochs)
+
+    def steps_per_second(self) -> float | None:
+        """Calculate average steps per second across all training."""
+        if self.total_steps == 0 or self.elapsed_time == 0:
+            return None
+
+        return self.total_steps / self.elapsed_time
+
+    def to_dict(self) -> dict[str, Any]:
+        """Convert metrics to a dictionary for telemetry reporting."""
+        metrics = {
+            "total_time_seconds": self.elapsed_time,
+            "total_steps": self.total_steps,
+            "steps_per_second": self.steps_per_second(),
+            "epochs_completed": len(
+                [
+                    epoch
+                    for epoch in self.epoch_start_times
+                    if epoch in self.epoch_end_times
+                ]
+            ),
+            "peak_cpu_memory_bytes": self.peak_cpu_memory,
+        }
+
+        # Add per-epoch timing if available
+        epoch_times: dict[str, float] = {}
+        for epoch in sorted(self.epoch_end_times.keys()):
+            time_taken = self.epoch_time(epoch)
+            if time_taken is not None:
+                epoch_times[f"epoch_{epoch}_seconds"] = time_taken
+
+        if epoch_times:
+            metrics["epoch_times"] = epoch_times  # type: ignore
+            metrics["average_epoch_time_seconds"] = self.average_epoch_time()
+
+        # Add GPU memory metrics if available
+        if self.peak_gpu_memory:
+            gpu_metrics: dict[str, int] = {}
+            for gpu_id, memory in self.peak_gpu_memory.items():
+                gpu_metrics[f"gpu_{gpu_id}_peak_memory_bytes"] = memory
+            metrics["gpu_memory"] = gpu_metrics  # type: ignore
+
+        return metrics
+
+
+class RuntimeMetricsTracker:
+    """Tracker for runtime metrics during training."""
+
+    update_interval = 100
+
+    def __init__(self):
+        """Initialize the runtime metrics tracker."""
+        self.metrics = RuntimeMetrics(start_time=time.time())
+        self.telemetry_manager = TelemetryManager.get_instance()
+
+    def start_epoch(self, epoch: int):
+        """Record the start of a new epoch."""
+        self.metrics.current_epoch = epoch
+        self.metrics.epoch_start_times[epoch] = time.time()
+        self.update_memory_metrics()
+
+    def end_epoch(self, epoch: int):
+        """Record the end of an epoch."""
+        self.metrics.epoch_end_times[epoch] = time.time()
+
+    def update_step(self, step: int):
+        """Update the current step count."""
+        self.metrics.current_step = step
+        self.metrics.total_steps += 1
+
+        # Periodically update memory metrics
+        if step % self.update_interval == 0:
+            self.update_memory_metrics()
+
+    def _get_allocated_memory(self) -> dict[int, int]:
+        """
+        Helper function for getting accelerator-agnostic allocated memory.
+
+        Returns:
+            A dictionary mapping device IDs to allocated memory in bytes
+        """
+        memory_used: dict[int, int] = {}
+
+        # NVIDIA GPUs
+        if torch.cuda.is_available():
+            for i in range(torch.cuda.device_count()):
+                memory_used[i] = torch.cuda.memory_allocated(i)
+
+        # AMD GPUs
+        elif hasattr(torch, "hip") and torch.hip.is_available():
+            for i in range(torch.hip.device_count()):
+                if hasattr(torch.hip, "memory_allocated"):
+                    memory_used[i] = torch.hip.memory_allocated(i)
+
+        # Apple Silicon
+        elif hasattr(torch.backends, "mps") and torch.backends.mps.is_available():
+            # MPS doesn't have per-device memory stats since there's only one device
+            if hasattr(torch.mps, "current_allocated_memory"):
+                memory_used[0] = torch.mps.current_allocated_memory()
+
+        # Intel GPUs
+        elif hasattr(torch, "xpu") and torch.xpu.is_available():
+            for i in range(torch.xpu.device_count()):
+                if hasattr(torch.xpu, "memory_allocated"):
+                    memory_used[i] = torch.xpu.memory_allocated(i)
+
+        # NPUs
+        elif hasattr(torch, "npu") and torch.npu.is_available():
+            for i in range(torch.npu.device_count()):
+                if hasattr(torch.npu, "memory_allocated"):
+                    memory_used[i] = torch.npu.memory_allocated(i)
+
+        return memory_used
+
+    def update_memory_metrics(self):
+        """Update peak memory usage metrics."""
+        # CPU memory
+        cpu_memory = psutil.Process().memory_info().rss
+        self.metrics.peak_cpu_memory = max(self.metrics.peak_cpu_memory, cpu_memory)
+
+        # GPU memory (if available)
+        memory_used = self._get_allocated_memory()
+        for i, memory in memory_used.items():
+            self.metrics.peak_gpu_memory[i] = max(
+                self.metrics.peak_gpu_memory.get(i, 0), memory
+            )
+
+    def get_memory_metrics(self) -> dict[str, Any]:
+        """Get the current memory metrics as a dictionary."""
+        memory_metrics = {
+            "cpu_memory_bytes": psutil.Process().memory_info().rss,
+            "peak_cpu_memory_bytes": self.metrics.peak_cpu_memory,
+        }
+
+        # GPU memory (if available)
+        memory_used = self._get_allocated_memory()
+        for i, memory in memory_used.items():
+            memory_metrics[f"gpu_{i}_memory_bytes"] = memory
+            memory_metrics[f"gpu_{i}_peak_memory_bytes"] = (
+                self.metrics.peak_gpu_memory.get(i, 0)
+            )
+
+        return memory_metrics

src/axolotl/telemetry/whitelist.yaml

@@ -0,0 +1,17 @@
+organizations:
+  - "axolotl-ai-co"
+  - "meta-llama"
+  - "huggingface"
+  - "nvidia"
+  - "facebook"
+  - "google"
+  - "microsoft"
+  - "deepseek-ai"
+  - "HuggingFaceTB"
+  - "mistralai"
+  - "Qwen"
+  - "unsloth"
+  - "NousResearch"
+  - "allenai"
+  - "amd"
+  - "tiiuae"

src/axolotl/train.py

@@ -32,6 +32,8 @@ from axolotl.loaders import (
     load_processor,
     load_tokenizer,
 )
+from axolotl.telemetry.errors import send_errors
+from axolotl.telemetry.manager import TelemetryManager
 from axolotl.utils.ctx_managers.sequence_parallel import SequenceParallelContextManager
 from axolotl.utils.dict import DictDefault
 from axolotl.utils.distributed import cleanup_distributed
@@ -47,6 +49,9 @@ except ImportError:
 
 LOG = get_logger(__name__)
 
+TELEMETRY_MANAGER = TelemetryManager.get_instance()
+PLUGIN_MANAGER = PluginManager.get_instance()
+
 
 def setup_model_and_tokenizer(
     cfg: DictDefault,
@@ -64,7 +69,10 @@ def setup_model_and_tokenizer(
             `None`), and processor (if multimodal, else `None`).
     """
     # Load tokenizer
-    LOG.debug(f"loading tokenizer... {cfg.tokenizer_config or cfg.base_model_config}")
+    LOG.debug(
+        f"loading tokenizer... {cfg.tokenizer_config or cfg.base_model_config}",
+        main_process_only=True,
+    )
     tokenizer = load_tokenizer(cfg)
 
     # Load processor for multimodal models if needed
@@ -83,6 +91,14 @@ def setup_model_and_tokenizer(
     if model.generation_config is not None:
         model.generation_config.do_sample = True
 
+    TELEMETRY_MANAGER.send_event(
+        event_type="model-load", properties=model.config.to_dict()
+    )
+    if peft_config:
+        TELEMETRY_MANAGER.send_event(
+            event_type="peft-config-load", properties=peft_config.to_dict()
+        )
+
     # Apply freezing if specified
     if cfg.unfrozen_parameters:
         freeze_layers_except(model, cfg.unfrozen_parameters)
@@ -517,6 +533,7 @@ def setup_model_and_trainer(cfg: DictDefault, dataset_meta: TrainDatasetMeta) ->
         model_ref=model_ref,
         peft_config=peft_config,
     )
+    PLUGIN_MANAGER.post_trainer_create(cfg, trainer)
 
     return (
         trainer,
@@ -527,6 +544,7 @@ def setup_model_and_trainer(cfg: DictDefault, dataset_meta: TrainDatasetMeta) ->
     )
 
 
+@send_errors
 def train(
     cfg: DictDefault, dataset_meta: TrainDatasetMeta
 ) -> tuple[PeftModel | PreTrainedModel, PreTrainedTokenizer, Trainer]:
@@ -551,8 +569,11 @@ def train(
         processor,
     ) = setup_model_and_trainer(cfg, dataset_meta)
 
-    plugin_manager = PluginManager.get_instance()
-    plugin_manager.post_trainer_create(cfg, trainer)
+    # Determine if we need to resume from a checkpoint
+    resume_from_checkpoint = determine_resume_checkpoint(cfg)
+
+    # Configuration for saving
+    safe_serialization = cfg.save_safetensors is True
 
     # Handle untrained tokens if configured
     safe_serialization = cfg.save_safetensors is True
@@ -567,7 +588,6 @@ def train(
     setup_model_card(cfg)
 
     # Execute the training
-    resume_from_checkpoint = determine_resume_checkpoint(cfg)
     execute_training(cfg, trainer, resume_from_checkpoint)
 
     # Save the trained model and cleanup
@@ -575,7 +595,6 @@ def train(
     create_model_card(cfg, trainer)
     if not cfg.use_ray:
         cleanup_distributed()
-
-    plugin_manager.post_train(cfg, model)
+    PLUGIN_MANAGER.post_train(cfg, model)
 
     return model, tokenizer, trainer

src/axolotl/utils/data/sft.py

@@ -486,10 +486,6 @@ def get_dataset_wrapper(
         f"Loading dataset: {config_dataset['path']} with base_type: {d_base_type} and prompt_style: {d_prompt_style}"
     )
 
-    import ipdb
-
-    ipdb.set_trace()
-
     if (
         isinstance(dataset, Dataset)
         and "input_ids" in dataset.features

src/axolotl/utils/schemas/config.py

@@ -1259,7 +1259,7 @@ class AxolotlInputConfig(
 
 
 class AxolotlConfigWCapabilities(AxolotlInputConfig):
-    """wrapper to valdiate gpu capabilities with the configured options"""
+    """Wrapper to validate GPU capabilities with the config options"""
 
     capabilities: GPUCapabilities
     env_capabilities: EnvCapabilities

tests/conftest.py

@@ -1,6 +1,4 @@
-"""
-shared pytest fixtures
-"""
+"""Shared pytest fixtures"""
 
 import functools
 import importlib
@@ -559,3 +557,9 @@ def test_load_fixtures(
     download_llama2_model_fixture,
 ):
     pass
+
+
+@pytest.fixture(autouse=True)
+def disable_telemetry(monkeypatch):
+    monkeypatch.setenv("AXOLOTL_DO_NOT_TRACK", "1")
+    yield

tests/telemetry/conftest.py

@@ -0,0 +1,9 @@
+"""Shared pytest fixtures for telemetry tests."""
+
+import pytest
+
+
+@pytest.fixture(autouse=True)
+def disable_telemetry(monkeypatch):
+    monkeypatch.delenv("AXOLOTL_DO_NOT_TRACK", raising=False)
+    yield

tests/telemetry/test_callbacks.py

@@ -0,0 +1,373 @@
+"""Tests for telemetry callback module."""
+
+# pylint: disable=redefined-outer-name
+
+import time
+from unittest.mock import MagicMock, patch
+
+import pytest
+from transformers import TrainerControl, TrainerState, TrainingArguments
+
+from axolotl.telemetry.callbacks import TIME_SINCE_LAST, TelemetryCallback
+
+
+def calc_expected_metrics(step, last_step, current_time, last_time, start_time=900.0):
+    """Calculate expected metrics values for tests"""
+    time_diff = current_time - last_time
+    step_diff = step - last_step
+    return {
+        "steps_per_second": (
+            step_diff / time_diff if time_diff > 0 and step_diff > 0 else 0
+        ),
+        "time_since_last_report": time_diff,
+        "elapsed_time": current_time - start_time,
+    }
+
+
+@pytest.fixture
+def mock_time():
+    """Mock time.time() to have predictable values in tests"""
+    with patch("axolotl.telemetry.callbacks.time") as mock_time:
+        mock_time.time.return_value = 1000.0
+        yield mock_time
+
+
+@pytest.fixture
+def mock_telemetry_manager():
+    """Create a mock TelemetryManager"""
+    with patch("axolotl.telemetry.callbacks.TelemetryManager") as mock_manager_class:
+        mock_manager = MagicMock()
+        mock_manager_class.get_instance.return_value = mock_manager
+        yield mock_manager
+
+
+@pytest.fixture
+def mock_runtime_metrics_tracker():
+    """Create a mock RuntimeMetricsTracker"""
+    with patch(
+        "axolotl.telemetry.callbacks.RuntimeMetricsTracker"
+    ) as mock_tracker_class:
+        mock_tracker = MagicMock()
+        # Set up metrics property on the tracker
+        mock_metrics = MagicMock()
+        mock_metrics.to_dict.return_value = {
+            "total_steps": 100,
+            "peak_cpu_memory_bytes": 1024,
+        }
+        mock_tracker.metrics = mock_metrics
+
+        # Make the constructor return our mock
+        mock_tracker_class.return_value = mock_tracker
+        yield mock_tracker
+
+
+@pytest.fixture
+def training_args():
+    """Create a minimal TrainingArguments instance"""
+    return TrainingArguments(output_dir="./output")
+
+
+@pytest.fixture
+def trainer_state():
+    """Create a mock TrainerState"""
+    state = MagicMock(spec=TrainerState)
+    state.global_step = 10
+    state.epoch = 0.5  # halfway through first epoch
+    state.log_history = [{"loss": 2.5, "learning_rate": 5e-5}]
+    return state
+
+
+@pytest.fixture
+def trainer_control():
+    """Create a mock TrainerControl"""
+    return MagicMock(spec=TrainerControl)
+
+
+# pylint: disable=unused-argument
+@pytest.fixture
+def callback(mock_telemetry_manager, mock_runtime_metrics_tracker):
+    """Create a TelemetryCallback instance with mocked dependencies"""
+    return TelemetryCallback()
+
+
+class TestTelemetryCallback:
+    """Tests for the TelemetryCallback class."""
+
+    def test_initialization(self, callback, mock_runtime_metrics_tracker):
+        """Test callback initialization."""
+        assert callback.current_epoch == -1
+        assert callback.tracker == mock_runtime_metrics_tracker
+        assert callback.last_report_step == 0
+        assert hasattr(callback, "start_time")
+        assert hasattr(callback, "last_report_time")
+        assert callback.report_interval_steps == 100
+
+    def test_on_train_begin(
+        self,
+        callback,
+        mock_telemetry_manager,
+        training_args,
+        trainer_state,
+        trainer_control,
+    ):
+        """Test on_train_begin sends expected event."""
+        callback.on_train_begin(training_args, trainer_state, trainer_control)
+
+        mock_telemetry_manager.send_event.assert_called_once_with(
+            event_type="train-start"
+        )
+
+    def test_on_train_end(
+        self,
+        callback,
+        mock_telemetry_manager,
+        training_args,
+        trainer_state,
+        trainer_control,
+    ):
+        """Test on_train_end sends expected event with metrics."""
+        callback.on_train_end(training_args, trainer_state, trainer_control)
+
+        mock_telemetry_manager.send_event.assert_called_once()
+        call_args = mock_telemetry_manager.send_event.call_args[1]
+
+        assert call_args["event_type"] == "train-end"
+        assert "loss" in call_args["properties"]
+        assert call_args["properties"]["loss"] == 2.5
+        assert "learning_rate" in call_args["properties"]
+        assert call_args["properties"]["learning_rate"] == 5e-5
+
+        # Check that metrics from RuntimeMetricsTracker are included
+        assert "total_steps" in call_args["properties"]
+        assert call_args["properties"]["total_steps"] == 100
+        assert "peak_cpu_memory_bytes" in call_args["properties"]
+        assert call_args["properties"]["peak_cpu_memory_bytes"] == 1024
+
+    def test_on_epoch_begin(
+        self,
+        callback,
+        mock_runtime_metrics_tracker,
+        training_args,
+        trainer_state,
+        trainer_control,
+    ):
+        """Test on_epoch_begin updates epoch counter and calls tracker."""
+        initial_epoch = callback.current_epoch
+
+        callback.on_epoch_begin(training_args, trainer_state, trainer_control)
+
+        assert callback.current_epoch == initial_epoch + 1
+        mock_runtime_metrics_tracker.start_epoch.assert_called_once_with(
+            initial_epoch + 1
+        )
+
+    def test_on_epoch_end(
+        self,
+        callback,
+        mock_runtime_metrics_tracker,
+        training_args,
+        trainer_state,
+        trainer_control,
+    ):
+        """Test on_epoch_end calls tracker."""
+        # Set current epoch
+        callback.current_epoch = 2
+
+        callback.on_epoch_end(training_args, trainer_state, trainer_control)
+
+        mock_runtime_metrics_tracker.end_epoch.assert_called_once_with(2)
+
+    def test_on_step_end_no_report(
+        self,
+        callback,
+        mock_telemetry_manager,
+        mock_runtime_metrics_tracker,
+        training_args,
+        trainer_state,
+        trainer_control,
+    ):
+        """Test on_step_end updates tracker but doesn't report if criteria not met."""
+        # Set up state to avoid reporting
+        trainer_state.global_step = 42  # Not divisible by report_interval_steps
+        callback.last_report_step = 41  # Just 1 step since last report
+        callback.last_report_time = time.time()  # Just now
+
+        callback.on_step_end(training_args, trainer_state, trainer_control)
+
+        # Should update tracker
+        mock_runtime_metrics_tracker.update_step.assert_called_once_with(42)
+
+        # Should not send telemetry
+        mock_telemetry_manager.send_event.assert_not_called()
+
+        # Should not update last report time/step
+        assert callback.last_report_step == 41
+
+    def test_on_step_end_report_interval_steps(
+        self,
+        callback,
+        mock_telemetry_manager,
+        mock_runtime_metrics_tracker,
+        mock_time,
+        training_args,
+        trainer_state,
+        trainer_control,
+    ):
+        """Test on_step_end reports when step interval is reached."""
+        # Set up state with clear values
+        current_step = 100  # Exactly matches report_interval_steps
+        last_step = 0
+        start_time = 900.0
+        current_time = 1000.0
+        time_diff = current_time - start_time  # 100 seconds
+
+        # Configure state and callback
+        trainer_state.global_step = current_step
+        callback.report_interval_steps = 100
+        callback.last_report_step = last_step
+        callback.start_time = start_time
+        callback.last_report_time = start_time
+
+        # Mock time.time() to return consistent values
+        mock_time.time.return_value = current_time
+
+        callback.on_step_end(training_args, trainer_state, trainer_control)
+
+        # Should update tracker
+        mock_runtime_metrics_tracker.update_step.assert_called_once_with(current_step)
+        mock_runtime_metrics_tracker.update_memory_metrics.assert_called_once()
+
+        # Should send telemetry
+        mock_telemetry_manager.send_event.assert_called_once()
+        call_args = mock_telemetry_manager.send_event.call_args[1]
+        assert call_args["event_type"] == "train-progress"
+
+        # Properties should include expected values
+        props = call_args["properties"]
+        assert props["step"] == current_step
+        assert props["elapsed_time"] == time_diff  # 1000 - 900 = 100
+        assert props["time_since_last_report"] == time_diff  # 1000 - 900 = 100
+        assert props["steps_per_second"] == 1.0  # 100 steps / 100 seconds
+
+        # Should update last report time/step
+        assert callback.last_report_step == current_step
+        assert callback.last_report_time == current_time
+
+    def test_on_step_end_report_time_elapsed(
+        self,
+        callback,
+        mock_telemetry_manager,
+        mock_runtime_metrics_tracker,  # pylint: disable=unused-argument
+        mock_time,
+        training_args,
+        trainer_state,
+        trainer_control,
+    ):
+        """Test on_step_end reports when enough time has elapsed."""
+        # Set up state with clear values
+        current_step = 120
+        last_step = 10
+        start_time = 900.0
+        current_time = 1000.0
+        time_diff = TIME_SINCE_LAST + 1  # Just over the threshold
+
+        # Configure state and callback
+        trainer_state.global_step = current_step
+        callback.report_interval_steps = 100
+        callback.last_report_step = last_step
+        callback.start_time = start_time
+        callback.last_report_time = current_time - time_diff
+
+        # Mock time.time() to return consistent values
+        mock_time.time.return_value = current_time
+
+        callback.on_step_end(training_args, trainer_state, trainer_control)
+
+        # Should send telemetry
+        mock_telemetry_manager.send_event.assert_called_once()
+
+        # Properties should include expected values
+        props = mock_telemetry_manager.send_event.call_args[1]["properties"]
+        expected_metrics = calc_expected_metrics(
+            current_step, last_step, current_time, current_time - time_diff, start_time
+        )
+        assert props["steps_per_second"] == expected_metrics["steps_per_second"]
+        assert (
+            props["time_since_last_report"]
+            == expected_metrics["time_since_last_report"]
+        )
+
+    def test_on_step_end_first_step(
+        self,
+        callback,
+        mock_telemetry_manager,
+        mock_runtime_metrics_tracker,  # pylint: disable=unused-argument
+        mock_time,
+        training_args,
+        trainer_state,
+        trainer_control,
+    ):
+        """Test on_step_end always reports on first step."""
+        # Set up state with clear values
+        current_step = 1  # First step
+        last_step = 0
+        start_time = 900.0
+        current_time = 1000.0
+        last_report_time = 999.0  # Just 1 second ago
+
+        # Configure state and callback
+        trainer_state.global_step = current_step
+        callback.report_interval_steps = 100
+        callback.last_report_step = last_step
+        callback.start_time = start_time
+        callback.last_report_time = last_report_time
+
+        # Mock time.time() to return consistent values
+        mock_time.time.return_value = current_time
+
+        callback.on_step_end(training_args, trainer_state, trainer_control)
+
+        # Should send telemetry even though not much time has passed
+        mock_telemetry_manager.send_event.assert_called_once()
+
+        # Properties should include expected values for first step
+        props = mock_telemetry_manager.send_event.call_args[1]["properties"]
+        assert props["step"] == current_step
+        expected_metrics = calc_expected_metrics(
+            current_step, last_step, current_time, last_report_time, start_time
+        )
+        assert props["steps_per_second"] == expected_metrics["steps_per_second"]
+
+    def test_log_history_empty(
+        self,
+        callback,
+        mock_telemetry_manager,
+        mock_runtime_metrics_tracker,  # pylint: disable=unused-argument
+        mock_time,
+        training_args,
+        trainer_state,
+        trainer_control,
+    ):
+        """Test handling of empty log history."""
+        # Set up state with clear values
+        current_step = 1
+        start_time = 900.0
+        current_time = 1000.0
+
+        # Configure state and callback
+        trainer_state.global_step = current_step
+        trainer_state.log_history = []
+        callback.start_time = start_time
+
+        # Mock time.time() to return consistent values
+        mock_time.time.return_value = current_time
+
+        callback.on_step_end(training_args, trainer_state, trainer_control)
+
+        # Should still send telemetry
+        mock_telemetry_manager.send_event.assert_called_once()
+
+        # Properties should have default values for missing log data
+        props = mock_telemetry_manager.send_event.call_args[1]["properties"]
+        assert props["loss"] == 0
+        assert props["learning_rate"] == 0

tests/telemetry/test_errors.py

@@ -0,0 +1,341 @@
+"""Tests for telemetry error utilities"""
+
+# pylint: disable=redefined-outer-name
+
+from unittest.mock import MagicMock, patch
+
+import pytest
+
+from axolotl.telemetry.errors import sanitize_stack_trace, send_errors
+
+
+@pytest.fixture(autouse=True)
+def reset_error_flag(monkeypatch):
+    """Reset ERROR_HANDLED flag using monkeypatch"""
+    import axolotl.telemetry.errors
+
+    monkeypatch.setattr(axolotl.telemetry.errors, "ERROR_HANDLED", False)
+    yield
+    monkeypatch.setattr(axolotl.telemetry.errors, "ERROR_HANDLED", False)
+
+
+@pytest.fixture
+def example_stack_trace():
+    """Provide a sample stack trace with mixed paths"""
+    return """Traceback (most recent call last):
+  File "/home/user/.local/lib/python3.9/site-packages/axolotl/cli/train.py", line 83, in main
+    trainer = get_trainer(cfg)
+  File "/home/user/.local/lib/python3.9/site-packages/axolotl/train.py", line 214, in get_trainer
+    model = get_model(cfg, tokenizer)
+  File "/home/user/.local/lib/python3.9/site-packages/axolotl/utils/models.py", line 120, in get_model
+    raise ValueError("Model path not found")
+ValueError: Model path not found
+"""
+
+
+@pytest.fixture
+def windows_stack_trace():
+    """Provide a sample stack trace with Windows paths"""
+    return """Traceback (most recent call last):
+  File "C:\\Users\\name\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\axolotl\\cli\\train.py", line 83, in main
+    trainer = get_trainer(cfg)
+  File "C:\\Users\\name\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\axolotl\\train.py", line 214, in get_trainer
+    model = get_model(cfg, tokenizer)
+  File "C:\\Users\\name\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\transformers\\models\\auto\\modeling_auto.py", line 482, in from_pretrained
+    raise ValueError(f"Unrecognized configuration class {config.__class__}")
+ValueError: Unrecognized configuration class <class 'transformers.models.llama.configuration_llama.LlamaConfig'>
+"""
+
+
+@pytest.fixture
+def mixed_stack_trace():
+    """Provide a sample stack trace with both axolotl and non-axolotl paths"""
+    return """Traceback (most recent call last):
+  File "/home/user/.local/lib/python3.9/site-packages/axolotl/cli/train.py", line 83, in main
+    trainer = get_trainer(cfg)
+  File "/home/user/.local/lib/python3.9/site-packages/transformers/trainer.py", line 520, in train
+    self._inner_training_loop()
+  File "/home/user/.local/lib/python3.9/site-packages/axolotl/utils/trainer.py", line 75, in _inner_training_loop
+    super()._inner_training_loop()
+  File "/home/user/.local/lib/python3.9/site-packages/torch/utils/data/dataloader.py", line 631, in __next__
+    data = self._next_data()
+RuntimeError: CUDA out of memory
+"""
+
+
+@pytest.fixture
+def venv_stack_trace():
+    """Provide a sample stack trace with virtual environment paths"""
+    return """Traceback (most recent call last):
+  File "/home/user/venv/lib/python3.9/site-packages/transformers/trainer.py", line 1729, in train
+    self._inner_training_loop()
+  File "/home/user/venv/lib/python3.9/site-packages/transformers/trainer.py", line 2013, in _inner_training_loop
+    self.accelerator.backward(loss)
+  File "/home/user/venv/lib/python3.9/site-packages/accelerate/accelerator.py", line 1851, in backward
+    self.scaler.scale(loss).backward(**kwargs)
+  File "/home/user/venv/lib/python3.9/site-packages/torch/_tensor.py", line 487, in backward
+    torch.autograd.backward(self, gradient, retain_graph, create_graph, inputs=inputs)
+RuntimeError: CUDA out of memory
+"""
+
+
+@pytest.fixture
+def dist_packages_stack_trace():
+    """Provide a sample stack trace with dist-packages paths"""
+    return """Traceback (most recent call last):
+  File "/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py", line 631, in __next__
+    data = self._next_data()
+  File "/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py", line 675, in _next_data
+    data = self._dataset_fetcher.fetch(index)
+  File "/usr/local/lib/python3.8/dist-packages/torch/utils/data/_utils/fetch.py", line 51, in fetch
+    data = [self.dataset[idx] for idx in possibly_batched_index]
+  File "/usr/local/lib/python3.8/dist-packages/datasets/arrow_dataset.py", line 2808, in __getitem__
+    raise IndexError(f"Index {key} out of range for dataset of length {len(self)}.")
+IndexError: Index 10000 out of range for dataset of length 9832.
+"""
+
+
+@pytest.fixture
+def project_stack_trace():
+    """Provide a sample stack trace from a project directory (not a virtual env)"""
+    return """Traceback (most recent call last):
+  File "/home/user/projects/myproject/run.py", line 25, in <module>
+    main()
+  File "/home/user/projects/myproject/src/cli.py", line 45, in main
+    app.run()
+  File "/home/user/projects/myproject/src/app.py", line 102, in run
+    raise ValueError("Configuration missing")
+ValueError: Configuration missing
+"""
+
+
+def test_sanitize_stack_trace(example_stack_trace):
+    """Test that sanitize_stack_trace properly preserves axolotl paths"""
+    sanitized = sanitize_stack_trace(example_stack_trace)
+
+    # Check that personal paths are removed
+    assert "/home/user" not in sanitized
+    assert ".local/lib/python3.9" not in sanitized
+
+    # Check that site-packages is preserved
+    assert "site-packages/axolotl/cli/train.py" in sanitized
+    assert "site-packages/axolotl/train.py" in sanitized
+    assert "site-packages/axolotl/utils/models.py" in sanitized
+
+    # Check that error message is preserved
+    assert "ValueError: Model path not found" in sanitized
+
+
+def test_sanitize_windows_paths(windows_stack_trace):
+    """Test that sanitize_stack_trace handles Windows paths"""
+    sanitized = sanitize_stack_trace(windows_stack_trace)
+
+    # Check that personal paths are removed
+    assert "C:\\Users\\name" not in sanitized
+    assert "AppData\\Local\\Programs\\Python" not in sanitized
+
+    # Check that both axolotl and transformers packages are preserved
+    assert (
+        "site-packages\\axolotl\\cli\\train.py" in sanitized
+        or "site-packages/axolotl/cli/train.py" in sanitized
+    )
+    assert (
+        "site-packages\\axolotl\\train.py" in sanitized
+        or "site-packages/axolotl/train.py" in sanitized
+    )
+    assert (
+        "site-packages\\transformers\\models\\auto\\modeling_auto.py" in sanitized
+        or "site-packages/transformers/models/auto/modeling_auto.py" in sanitized
+    )
+
+    # Check that error message is preserved
+    assert "ValueError: Unrecognized configuration class" in sanitized
+
+
+def test_sanitize_mixed_paths(mixed_stack_trace):
+    """Test that sanitize_stack_trace preserves all package paths"""
+    sanitized = sanitize_stack_trace(mixed_stack_trace)
+
+    # Check that all package paths are preserved
+    assert "site-packages/axolotl/cli/train.py" in sanitized
+    assert "site-packages/transformers/trainer.py" in sanitized
+    assert "site-packages/axolotl/utils/trainer.py" in sanitized
+    assert "site-packages/torch/utils/data/dataloader.py" in sanitized
+
+    # Check that error message is preserved
+    assert "RuntimeError: CUDA out of memory" in sanitized
+
+
+def test_sanitize_venv_paths(venv_stack_trace):
+    """Test that sanitize_stack_trace preserves virtual environment package paths"""
+    sanitized = sanitize_stack_trace(venv_stack_trace)
+
+    # Check that personal paths are removed
+    assert "/home/user/venv" not in sanitized
+
+    # Check that all package paths are preserved
+    assert "site-packages/transformers/trainer.py" in sanitized
+    assert "site-packages/accelerate/accelerator.py" in sanitized
+    assert "site-packages/torch/_tensor.py" in sanitized
+
+    # Check that error message is preserved
+    assert "RuntimeError: CUDA out of memory" in sanitized
+
+
+def test_sanitize_dist_packages(dist_packages_stack_trace):
+    """Test that sanitize_stack_trace preserves dist-packages paths"""
+    sanitized = sanitize_stack_trace(dist_packages_stack_trace)
+
+    # Check that system paths are removed
+    assert "/usr/local/lib/python3.8" not in sanitized
+
+    # Check that all package paths are preserved
+    assert "dist-packages/torch/utils/data/dataloader.py" in sanitized
+    assert "dist-packages/torch/utils/data/_utils/fetch.py" in sanitized
+    assert "dist-packages/datasets/arrow_dataset.py" in sanitized
+
+    # Check that error message is preserved
+    assert (
+        "IndexError: Index 10000 out of range for dataset of length 9832." in sanitized
+    )
+
+
+def test_sanitize_project_paths(project_stack_trace):
+    """Test handling of project paths (non-virtual env)"""
+    sanitized = sanitize_stack_trace(project_stack_trace)
+
+    # Check that personal paths are removed
+    assert "/home/user/projects" not in sanitized
+
+    # For non-package paths, we should at least preserve the filename
+    assert "run.py" in sanitized
+    assert "cli.py" in sanitized
+    assert "app.py" in sanitized
+
+    # Check that error message is preserved
+    assert "ValueError: Configuration missing" in sanitized
+
+
+@pytest.fixture
+def mock_telemetry_manager():
+    """Create a mock TelemetryManager"""
+    with patch("axolotl.telemetry.errors.TelemetryManager") as mock_manager_class:
+        mock_manager = MagicMock()
+        mock_manager.enabled = True
+        mock_manager_class.get_instance.return_value = mock_manager
+        yield mock_manager
+
+
+def test_send_errors_successful_execution(mock_telemetry_manager):
+    """Test that send_errors doesn't send telemetry for successful function execution"""
+
+    @send_errors
+    def test_func():
+        return "success"
+
+    result = test_func()
+    assert result == "success"
+    mock_telemetry_manager.send_event.assert_not_called()
+
+
+def test_send_errors_with_exception(mock_telemetry_manager):
+    """Test that send_errors sends telemetry when an exception occurs"""
+    test_error = ValueError("Test error")
+
+    @send_errors
+    def test_func():
+        raise test_error
+
+    with pytest.raises(ValueError) as excinfo:
+        test_func()
+
+    assert excinfo.value == test_error
+    mock_telemetry_manager.send_event.assert_called_once()
+
+    # Check that the error info was passed correctly
+    call_args = mock_telemetry_manager.send_event.call_args[1]
+    assert "test_func-error" in call_args["event_type"]
+    assert "Test error" in call_args["properties"]["exception"]
+    assert "stack_trace" in call_args["properties"]
+
+
+def test_send_errors_nested_calls(mock_telemetry_manager):
+    """Test that send_errors only sends telemetry once for nested decorated functions"""
+
+    @send_errors
+    def inner_func():
+        raise ValueError("Inner error")
+
+    @send_errors
+    def outer_func():
+        return inner_func()
+
+    with pytest.raises(ValueError):
+        outer_func()
+
+    # Telemetry should be sent only once for the inner function
+    assert mock_telemetry_manager.send_event.call_count == 1
+    call_args = mock_telemetry_manager.send_event.call_args[1]
+    assert "inner_func-error" in call_args["event_type"]
+
+
+def test_send_errors_telemetry_disable():
+    """Test that send_errors doesn't attempt to send telemetry when disabled"""
+
+    with patch("axolotl.telemetry.errors.TelemetryManager") as mock_manager_class:
+        mock_manager = MagicMock()
+        mock_manager.enabled = False
+        mock_manager_class.get_instance.return_value = mock_manager
+
+        @send_errors
+        def test_func():
+            raise ValueError("Test error")
+
+        with pytest.raises(ValueError):
+            test_func()
+
+        mock_manager.send_event.assert_not_called()
+
+
+def test_error_handled_reset():
+    """Test that ERROR_HANDLED flag is properly reset"""
+    with patch("axolotl.telemetry.errors.TelemetryManager") as mock_manager_class:
+        # Create and configure the mock manager
+        mock_manager = MagicMock()
+        mock_manager.enabled = True
+        mock_manager_class.get_instance.return_value = mock_manager
+
+        from axolotl.telemetry.errors import ERROR_HANDLED
+
+        @send_errors
+        def test_func():
+            raise ValueError("Test error")
+
+        assert not ERROR_HANDLED
+
+        with pytest.raises(ValueError):
+            test_func()
+
+        from axolotl.telemetry.errors import ERROR_HANDLED
+
+        assert ERROR_HANDLED
+
+
+def test_module_path_resolution(mock_telemetry_manager):
+    """Test that the module path is correctly resolved for the event type"""
+    import inspect
+
+    current_module = inspect.getmodule(test_module_path_resolution).__name__
+
+    @send_errors
+    def test_func():
+        raise ValueError("Test error")
+
+    with pytest.raises(ValueError):
+        test_func()
+
+    assert mock_telemetry_manager.send_event.called
+    event_type = mock_telemetry_manager.send_event.call_args[1]["event_type"]
+
+    expected_event_type = f"{current_module}.test_func-error"
+    assert expected_event_type == event_type

tests/telemetry/test_manager.py

@@ -0,0 +1,278 @@
+"""Tests for TelemetryManager class and utilities"""
+
+# pylint: disable=redefined-outer-name,protected-access
+
+import os
+from unittest.mock import patch
+
+import pytest
+import yaml
+
+from axolotl.telemetry.manager import TelemetryManager
+
+
+@pytest.fixture
+def mock_whitelist(tmp_path):
+    """Create a temporary whitelist file for testing"""
+    whitelist_content = {
+        "organizations": ["meta-llama", "mistralai"],
+    }
+    whitelist_file = tmp_path / "whitelist.yaml"
+    with open(whitelist_file, "w", encoding="utf-8") as f:
+        yaml.dump(whitelist_content, f)
+
+    return str(whitelist_file)
+
+
+@pytest.fixture
+def telemetry_manager_class():
+    """Reset the TelemetryManager singleton between tests"""
+    original_instance = TelemetryManager._instance
+    original_initialized = TelemetryManager._initialized
+    TelemetryManager._instance = None
+    TelemetryManager._initialized = False
+    yield TelemetryManager
+    TelemetryManager._instance = original_instance
+    TelemetryManager._initialized = original_initialized
+
+
+@pytest.fixture
+def manager(telemetry_manager_class, mock_whitelist):
+    """Create a TelemetryManager instance with mocked dependencies"""
+    with (
+        patch("posthog.capture"),
+        patch("posthog.flush"),
+        patch("time.sleep"),
+        patch("axolotl.telemetry.manager.WHITELIST_PATH", mock_whitelist),
+        patch.dict(os.environ, {"RANK": "0"}),
+    ):
+        manager = telemetry_manager_class()
+        # Manually enable for most tests
+        manager.enabled = True
+        return manager
+
+
+def test_singleton_instance(telemetry_manager_class):
+    """Test that TelemetryManager is a singleton"""
+    with (
+        patch("posthog.capture"),
+        patch("time.sleep"),
+        patch.dict(os.environ, {"RANK": "0"}),
+    ):
+        first = telemetry_manager_class()
+        second = telemetry_manager_class()
+        assert first is second
+        assert telemetry_manager_class.get_instance() is first
+
+
+def test_telemetry_disabled_by_default(telemetry_manager_class):
+    """Test that telemetry is disabled by default (opt-in)"""
+    with (
+        patch.dict(os.environ, {"RANK": "0"}, clear=True),
+        patch("time.sleep"),
+        patch("logging.Logger.info"),
+    ):
+        manager = telemetry_manager_class()
+        assert not manager.enabled
+
+
+def test_telemetry_enabled_with_explicit_opt_in(telemetry_manager_class):
+    """Test that telemetry is enabled when AXOLOTL_DO_NOT_TRACK=0"""
+    with (
+        patch.dict(os.environ, {"AXOLOTL_DO_NOT_TRACK": "0", "RANK": "0"}),
+        patch("time.sleep"),
+    ):
+        manager = telemetry_manager_class()
+        assert manager.enabled
+
+
+def test_telemetry_disabled_with_axolotl_do_not_track(telemetry_manager_class):
+    """Test that telemetry is disabled when AXOLOTL_DO_NOT_TRACK=1"""
+    with (
+        patch.dict(os.environ, {"AXOLOTL_DO_NOT_TRACK": "1", "RANK": "0"}),
+        patch("time.sleep"),
+    ):
+        manager = telemetry_manager_class()
+        assert not manager.enabled
+
+
+def test_telemetry_disabled_with_do_not_track(telemetry_manager_class):
+    """Test that telemetry is disabled when DO_NOT_TRACK=1"""
+    with (
+        patch.dict(
+            os.environ, {"AXOLOTL_DO_NOT_TRACK": "0", "DO_NOT_TRACK": "1", "RANK": "0"}
+        ),
+        patch("time.sleep"),
+    ):
+        manager = telemetry_manager_class()
+        assert not manager.enabled
+
+
+def test_telemetry_disabled_for_non_main_process(telemetry_manager_class):
+    """Test that telemetry is disabled for non-main processes"""
+    with (
+        patch.dict(os.environ, {"AXOLOTL_DO_NOT_TRACK": "0", "RANK": "1"}),
+        patch("time.sleep"),
+    ):
+        manager = telemetry_manager_class()
+        assert not manager.enabled
+
+
+def test_opt_in_info_displayed(telemetry_manager_class):
+    """Test that opt-in info is displayed when telemetry is not configured"""
+    with (
+        patch.dict(os.environ, {"RANK": "0"}, clear=True),
+        patch("logging.Logger.warning") as mock_warning,
+        patch("time.sleep"),
+    ):
+        telemetry_manager_class()
+        info_displayed = False
+        for call in mock_warning.call_args_list:
+            print(f"call: {call}")
+            if "Telemetry is currently disabled by default" in str(call):
+                info_displayed = True
+                break
+        assert info_displayed
+
+
+def test_is_whitelisted(telemetry_manager_class, mock_whitelist):
+    """Test org whitelist functionality"""
+    with (
+        patch("axolotl.telemetry.manager.WHITELIST_PATH", mock_whitelist),
+        patch.dict(os.environ, {"AXOLOTL_DO_NOT_TRACK": "0"}),
+    ):
+        manager = telemetry_manager_class()
+
+        # Should match organizations from the mock whitelist
+        assert manager._is_whitelisted("meta-llama/llama-7b")
+        assert manager._is_whitelisted("mistralai/mistral-7b-instruct")
+        # Should not match
+        assert not manager._is_whitelisted("unknown/model")
+        # Should handle case insensitively
+        assert manager._is_whitelisted("META-LLAMA/Llama-7B")
+        # Should handle empty input
+        assert not manager._is_whitelisted("")
+
+
+def test_system_info_collection(manager):
+    """Test system information collection"""
+    system_info = manager._get_system_info()
+
+    # Check essential keys
+    assert "os" in system_info
+    assert "python_version" in system_info
+    assert "cpu_count" in system_info
+    assert "memory_total" in system_info
+    assert "accelerator_count" in system_info
+
+
+def test_send_event(telemetry_manager_class):
+    """Test basic event sending"""
+    with (
+        patch("posthog.capture") as mock_capture,
+        patch.dict(os.environ, {"AXOLOTL_DO_NOT_TRACK": "0"}),
+    ):
+        manager = telemetry_manager_class()
+
+        # Test with clean properties (no PII)
+        manager.send_event("test_event", {"key": "value"})
+        assert mock_capture.called
+        assert mock_capture.call_args[1]["event"] == "test_event"
+        assert mock_capture.call_args[1]["properties"] == {"key": "value"}
+        assert mock_capture.call_args[1]["distinct_id"] == manager.run_id
+
+        # Test with default properties (None)
+        mock_capture.reset_mock()
+        manager.send_event("simple_event")
+        assert mock_capture.called
+        assert mock_capture.call_args[1]["properties"] == {}
+
+
+def test_send_system_info(telemetry_manager_class):
+    """Test sending system info"""
+    with (
+        patch("posthog.capture") as mock_capture,
+        patch.dict(os.environ, {"AXOLOTL_DO_NOT_TRACK": "0"}),
+    ):
+        manager = telemetry_manager_class()
+        manager.send_system_info()
+        assert mock_capture.called
+        assert mock_capture.call_args[1]["event"] == "system-info"
+        assert mock_capture.call_args[1]["properties"] == manager.system_info
+
+
+def test_redacted_properties(telemetry_manager_class):
+    """Test path redaction in send_event method"""
+    with (
+        patch("posthog.capture") as mock_capture,
+        patch.dict(os.environ, {"AXOLOTL_DO_NOT_TRACK": "0"}),
+    ):
+        manager = telemetry_manager_class()
+        # Test with properties containing various paths and non-paths
+        test_properties = {
+            "filepath": "/home/user/sensitive/data.txt",
+            "windows_path": "C:\\Users\\name\\Documents\\project\\file.py",
+            "output_dir": "/var/lib/data",
+            "path_to_model": "models/llama/7b",
+            "message": "Training started",  # Should not be redacted
+            "metrics": {"loss": 0.5, "accuracy": 0.95},  # Should not be redacted
+            "base_model": "models/local_model",
+            "nested": {
+                "model_path": "/models/my_model",
+                "root_dir": "/home/user/projects",
+                "stats": {"steps": 1000, "epochs": 3},  # Should not be redacted
+            },
+        }
+
+        manager.send_event("test_event", test_properties)
+
+        # Verify the call was made
+        assert mock_capture.called
+
+        # Get the sanitized properties that were sent
+        sanitized = mock_capture.call_args[1]["properties"]
+
+        # Check that path-like and base_model keys were redacted
+        assert sanitized["filepath"] == "[REDACTED]"
+        assert sanitized["windows_path"] == "[REDACTED]"
+        assert sanitized["path_to_model"] == "[REDACTED]"
+        assert sanitized["base_model"] == "[REDACTED]"
+
+        # Check that non-path values were preserved
+        assert sanitized["message"] == "Training started"
+        assert sanitized["metrics"] == {"loss": 0.5, "accuracy": 0.95}
+
+        # Check nested structure handling
+        assert sanitized["nested"]["model_path"] == "[REDACTED]"
+        assert sanitized["nested"]["root_dir"] == "[REDACTED]"
+        assert sanitized["nested"]["stats"] == {"steps": 1000, "epochs": 3}
+
+
+def test_disable_telemetry(manager):
+    """Test that disabled telemetry doesn't send events"""
+    with patch("posthog.capture") as mock_capture:
+        manager.enabled = False
+        manager.send_event("test_event")
+        assert not mock_capture.called
+
+
+def test_exception_handling_during_send(manager):
+    """Test that exceptions in PostHog are handled gracefully"""
+    with (
+        patch("posthog.capture", side_effect=Exception("Test error")),
+        patch("logging.Logger.warning") as mock_warning,
+    ):
+        manager.send_event("test_event")
+        warning_logged = False
+        for call in mock_warning.call_args_list:
+            if "Failed to send telemetry event" in str(call):
+                warning_logged = True
+                break
+        assert warning_logged
+
+
+def test_shutdown(manager):
+    """Test shutdown behavior"""
+    with patch("posthog.flush") as mock_flush:
+        manager.shutdown()
+        assert mock_flush.called

tests/telemetry/test_runtime_metrics.py

@@ -0,0 +1,357 @@
+"""Tests for runtime metrics telemetry module"""
+
+# pylint: disable=redefined-outer-name
+
+from unittest.mock import MagicMock, patch
+
+import pytest
+
+from axolotl.telemetry.runtime_metrics import RuntimeMetrics, RuntimeMetricsTracker
+
+
+@pytest.fixture
+def mock_time():
+    """Mock time.time() to have predictable values in tests"""
+    with patch("time.time") as mock_time:
+        # Start with time 1000.0 and increment by 10 seconds on each call
+        times = [1000.0 + i * 10 for i in range(10)]
+        mock_time.side_effect = times
+        yield mock_time
+
+
+@pytest.fixture
+def mock_telemetry_manager():
+    """Create a mock TelemetryManager"""
+    with patch(
+        "axolotl.telemetry.runtime_metrics.TelemetryManager"
+    ) as mock_manager_class:
+        mock_manager = MagicMock()
+        mock_manager.enabled = True
+        mock_manager_class.get_instance.return_value = mock_manager
+        yield mock_manager
+
+
+@pytest.fixture
+def mock_psutil():
+    """Mock psutil for memory information"""
+    with patch("axolotl.telemetry.runtime_metrics.psutil") as mock_psutil:
+        mock_process = MagicMock()
+        mock_memory_info = MagicMock()
+        # Set initial memory to 1GB
+        mock_memory_info.rss = 1024 * 1024 * 1024
+        mock_process.memory_info.return_value = mock_memory_info
+        mock_psutil.Process.return_value = mock_process
+        yield mock_psutil
+
+
+@pytest.fixture
+def mock_torch():
+    """Mock torch.cuda functions"""
+    with patch("axolotl.telemetry.runtime_metrics.torch") as mock_torch:
+        mock_torch.cuda.is_available.return_value = True
+        mock_torch.cuda.device_count.return_value = 2
+
+        # Mock memory allocated per device (1GB for device 0, 2GB for device 1)
+        mock_torch.cuda.memory_allocated.side_effect = (
+            lambda device: (device + 1) * 1024 * 1024 * 1024
+        )
+
+        yield mock_torch
+
+
+class TestRuntimeMetrics:
+    """Tests for RuntimeMetrics class."""
+
+    def test_initialization(self):
+        """Test RuntimeMetrics initialization."""
+        metrics = RuntimeMetrics(start_time=1000.0)
+
+        assert metrics.start_time == 1000.0
+        assert metrics.epoch_start_times == {}
+        assert metrics.epoch_end_times == {}
+        assert metrics.peak_gpu_memory == {}
+        assert metrics.total_steps == 0
+        assert metrics.current_epoch == 0
+        assert metrics.current_step == 0
+        assert metrics.peak_cpu_memory == 0
+
+    def test_elapsed_time(self, mock_time):
+        """Test elapsed_time property."""
+        metrics = RuntimeMetrics(start_time=1000.0)
+
+        # Mock time.time() to return 1050.0
+        mock_time.side_effect = [1050.0]
+
+        assert metrics.elapsed_time == 50.0
+
+    def test_epoch_time(self):
+        """Test epoch_time method."""
+        metrics = RuntimeMetrics(start_time=1000.0)
+
+        # No epoch data
+        assert metrics.epoch_time(0) is None
+
+        # Add epoch start but no end
+        metrics.epoch_start_times[0] = 1000.0
+        assert metrics.epoch_time(0) is None
+
+        # Add epoch end
+        metrics.epoch_end_times[0] = 1060.0
+        assert metrics.epoch_time(0) == 60.0
+
+    def test_average_epoch_time(self):
+        """Test average_epoch_time method."""
+        metrics = RuntimeMetrics(start_time=1000.0)
+
+        # No completed epochs
+        assert metrics.average_epoch_time() is None
+
+        # Add one completed epoch
+        metrics.epoch_start_times[0] = 1000.0
+        metrics.epoch_end_times[0] = 1060.0
+        assert metrics.average_epoch_time() == 60.0
+
+        # Add second completed epoch
+        metrics.epoch_start_times[1] = 1060.0
+        metrics.epoch_end_times[1] = 1140.0  # 80 seconds
+        assert metrics.average_epoch_time() == 70.0  # Average of 60 and 80
+
+        # Add incomplete epoch (should not affect average)
+        metrics.epoch_start_times[2] = 1140.0
+        assert metrics.average_epoch_time() == 70.0
+
+    def test_steps_per_second(self, mock_time):
+        """Test steps_per_second method."""
+        metrics = RuntimeMetrics(start_time=1000.0)
+
+        # No steps - first call to time.time()
+        mock_time.side_effect = None
+        mock_time.return_value = 1050.0
+        assert metrics.steps_per_second() is None
+
+        # Add steps - second call to time.time()
+        metrics.total_steps = 100
+        mock_time.return_value = 1050.0  # Keep same time for consistent result
+        assert metrics.steps_per_second() == 2.0  # 100 steps / 50 seconds
+
+    def test_to_dict_basic(self, mock_time):
+        """Test to_dict method with basic metrics."""
+        metrics = RuntimeMetrics(start_time=1000.0)
+        metrics.total_steps = 100
+        metrics.peak_cpu_memory = 2 * 1024 * 1024 * 1024  # 2GB
+
+        # Mock elapsed_time
+        mock_time.side_effect = None
+        mock_time.return_value = 1050.0
+
+        result = metrics.to_dict()
+
+        assert result["total_time_seconds"] == 50.0
+        assert result["total_steps"] == 100
+        assert result["steps_per_second"] == 2.0
+        assert result["epochs_completed"] == 0
+        assert result["peak_cpu_memory_bytes"] == 2 * 1024 * 1024 * 1024
+        assert "epoch_times" not in result
+        assert "gpu_memory" not in result
+
+    def test_to_dict_with_epochs(self, mock_time):
+        """Test to_dict method with epoch data."""
+        metrics = RuntimeMetrics(start_time=1000.0)
+        metrics.total_steps = 100
+
+        # Add epoch data
+        metrics.epoch_start_times[0] = 1000.0
+        metrics.epoch_end_times[0] = 1060.0
+        metrics.epoch_start_times[1] = 1060.0
+        metrics.epoch_end_times[1] = 1140.0
+
+        # Mock elapsed_time
+        mock_time.side_effect = None
+        mock_time.return_value = 1150.0
+
+        result = metrics.to_dict()
+
+        assert "epoch_times" in result
+        assert result["epoch_times"]["epoch_0_seconds"] == 60.0
+        assert result["epoch_times"]["epoch_1_seconds"] == 80.0
+        assert result["average_epoch_time_seconds"] == 70.0
+
+    def test_to_dict_with_gpu_memory(self, mock_time):
+        """Test to_dict method with GPU memory data."""
+        metrics = RuntimeMetrics(start_time=1000.0)
+        metrics.peak_gpu_memory = {
+            0: 1 * 1024 * 1024 * 1024,  # 1GB
+            1: 2 * 1024 * 1024 * 1024,  # 2GB
+        }
+
+        # Mock elapsed_time
+        mock_time.side_effect = [1050.0]
+
+        result = metrics.to_dict()
+
+        assert "gpu_memory" in result
+        assert result["gpu_memory"]["gpu_0_peak_memory_bytes"] == 1 * 1024 * 1024 * 1024
+        assert result["gpu_memory"]["gpu_1_peak_memory_bytes"] == 2 * 1024 * 1024 * 1024
+
+
+class TestRuntimeMetricsTracker:
+    """Tests for RuntimeMetricsTracker class."""
+
+    # pylint: disable=unused-argument
+    def test_initialization(self, mock_time, mock_telemetry_manager):
+        """Test RuntimeMetricsTracker initialization."""
+        tracker = RuntimeMetricsTracker()
+
+        assert isinstance(tracker.metrics, RuntimeMetrics)
+        assert tracker.metrics.start_time == 1000.0  # First value from mock_time
+
+    # pylint: disable=unused-argument
+    def test_start_epoch(
+        self, mock_time, mock_psutil, mock_torch, mock_telemetry_manager
+    ):
+        """Test start_epoch method."""
+        tracker = RuntimeMetricsTracker()
+
+        # Reset mock_time to control next value
+        mock_time.side_effect = [1010.0]
+
+        tracker.start_epoch(0)
+
+        assert tracker.metrics.current_epoch == 0
+        assert tracker.metrics.epoch_start_times[0] == 1010.0
+
+        # Verify memory metrics were updated
+        assert tracker.metrics.peak_cpu_memory == 1 * 1024 * 1024 * 1024
+        assert 0 in tracker.metrics.peak_gpu_memory
+        assert 1 in tracker.metrics.peak_gpu_memory
+
+    # pylint: disable=unused-argument
+    def test_end_epoch(self, mock_time, mock_telemetry_manager):
+        """Test end_epoch method."""
+        tracker = RuntimeMetricsTracker()
+
+        # Start epoch 0
+        mock_time.side_effect = [1010.0]
+        tracker.start_epoch(0)
+
+        # End epoch 0
+        mock_time.side_effect = [1060.0]
+        tracker.end_epoch(0)
+
+        assert 0 in tracker.metrics.epoch_end_times
+        assert tracker.metrics.epoch_end_times[0] == 1060.0
+
+    # pylint: disable=unused-argument
+    def test_update_step(
+        self, mock_time, mock_psutil, mock_torch, mock_telemetry_manager
+    ):
+        """Test update_step method."""
+        tracker = RuntimeMetricsTracker()
+
+        # Update step to a non-multiple of 100
+        tracker.update_step(42)
+
+        assert tracker.metrics.current_step == 42
+        assert tracker.metrics.total_steps == 1
+
+        # Memory metrics should not be updated for non-multiple of 100
+        assert tracker.metrics.peak_cpu_memory == 0
+
+        # Update step to a multiple of 100
+        tracker.update_step(100)
+
+        assert tracker.metrics.current_step == 100
+        assert tracker.metrics.total_steps == 2
+
+        # Memory metrics should be updated for multiple of 100
+        assert tracker.metrics.peak_cpu_memory == 1 * 1024 * 1024 * 1024
+
+    # pylint: disable=unused-argument
+    def test_update_memory_metrics(
+        self, mock_psutil, mock_torch, mock_telemetry_manager
+    ):
+        """Test update_memory_metrics method."""
+        tracker = RuntimeMetricsTracker()
+
+        # Initial memory state
+        assert tracker.metrics.peak_cpu_memory == 0
+        assert tracker.metrics.peak_gpu_memory == {}
+
+        # Update memory metrics
+        tracker.update_memory_metrics()
+
+        # Verify CPU memory
+        assert tracker.metrics.peak_cpu_memory == 1 * 1024 * 1024 * 1024
+
+        # Verify GPU memory
+        assert tracker.metrics.peak_gpu_memory[0] == 1 * 1024 * 1024 * 1024
+        assert tracker.metrics.peak_gpu_memory[1] == 2 * 1024 * 1024 * 1024
+
+        # Change mocked memory values to be lower
+        mock_process = mock_psutil.Process.return_value
+        mock_memory_info = mock_process.memory_info.return_value
+        mock_memory_info.rss = 0.5 * 1024 * 1024 * 1024  # 0.5GB
+
+        mock_torch.cuda.memory_allocated.side_effect = (
+            lambda device: (device + 0.5) * 1024 * 1024 * 1024
+        )
+
+        # Update memory metrics again
+        tracker.update_memory_metrics()
+
+        # Peak values should not decrease
+        assert tracker.metrics.peak_cpu_memory == 1 * 1024 * 1024 * 1024
+        assert tracker.metrics.peak_gpu_memory[0] == 1 * 1024 * 1024 * 1024
+        assert tracker.metrics.peak_gpu_memory[1] == 2 * 1024 * 1024 * 1024
+
+        # Change mocked memory values to be higher
+        mock_memory_info.rss = 2 * 1024 * 1024 * 1024  # 2GB
+
+        mock_torch.cuda.memory_allocated.side_effect = (
+            lambda device: (device + 2) * 1024 * 1024 * 1024
+        )
+
+        # Update memory metrics again
+        tracker.update_memory_metrics()
+
+        # Peak values should increase
+        assert tracker.metrics.peak_cpu_memory == 2 * 1024 * 1024 * 1024
+        assert tracker.metrics.peak_gpu_memory[0] == 2 * 1024 * 1024 * 1024
+        assert tracker.metrics.peak_gpu_memory[1] == 3 * 1024 * 1024 * 1024
+
+    # pylint: disable=unused-argument
+    def test_get_memory_metrics(self, mock_psutil, mock_torch, mock_telemetry_manager):
+        """Test get_memory_metrics method."""
+        tracker = RuntimeMetricsTracker()
+
+        # Set peak memory values
+        tracker.metrics.peak_cpu_memory = 2 * 1024 * 1024 * 1024
+        tracker.metrics.peak_gpu_memory = {
+            0: 3 * 1024 * 1024 * 1024,
+            1: 4 * 1024 * 1024 * 1024,
+        }
+
+        # Get memory metrics
+        memory_metrics = tracker.get_memory_metrics()
+
+        # Verify CPU memory
+        assert (
+            memory_metrics["cpu_memory_bytes"] == 1 * 1024 * 1024 * 1024
+        )  # Current value from mock
+        assert (
+            memory_metrics["peak_cpu_memory_bytes"] == 2 * 1024 * 1024 * 1024
+        )  # Peak value we set
+
+        # Verify GPU memory
+        assert (
+            memory_metrics["gpu_0_memory_bytes"] == 1 * 1024 * 1024 * 1024
+        )  # Current value from mock
+        assert (
+            memory_metrics["gpu_0_peak_memory_bytes"] == 3 * 1024 * 1024 * 1024
+        )  # Peak value we set
+        assert (
+            memory_metrics["gpu_1_memory_bytes"] == 2 * 1024 * 1024 * 1024
+        )  # Current value from mock
+        assert (
+            memory_metrics["gpu_1_peak_memory_bytes"] == 4 * 1024 * 1024 * 1024
+        )  # Peak value we set

tests/test_tokenizers.py

@@ -1,4 +1,8 @@
-"""Test cases for tokenizer loading."""
+"""
+Test cases for the tokenizer loading
+"""
+
+import unittest
 
 import pytest
 
@@ -9,7 +13,9 @@ from tests.hf_offline_utils import enable_hf_offline
 
 
 class TestTokenizers:
-    """Test class for the load_tokenizer fn"""
+    """
+    test class for the load_tokenizer fn
+    """
 
     @enable_hf_offline
     def test_default_use_fast(self):
@@ -149,50 +155,6 @@ class TestTokenizers:
         ):
             load_tokenizer(cfg)
 
-    def test_mistral_tokenizer_auto_detection(self):
-        """Test that Mistral models are auto-detected and use MistralTokenizerWrapper"""
-        cfg = DictDefault(
-            {
-                "base_model": "adamo1139/Mistral-Small-24B-Instruct-2501-ungated",
-                "tokenizer_config": "adamo1139/Mistral-Small-24B-Instruct-2501-ungated",
-            }
-        )
-        tokenizer = load_tokenizer(cfg)
-        assert tokenizer.__class__.__name__ == "MistralTokenizerWrapper"
 
-    def test_mixtral_tokenizer_auto_detection(self):
-        """Test that Mixtral models are auto-detected and use MistralTokenizerWrapper"""
-        cfg = DictDefault(
-            {
-                "base_model": "model-hub/Mixtral-8x7B-v0.1",
-                "tokenizer_config": "model-hub/Mixtral-8x7B-v0.1",
-            }
-        )
-        tokenizer = load_tokenizer(cfg)
-        assert tokenizer.__class__.__name__ == "MistralTokenizerWrapper"
-
-    def test_mistral_tokenizer_basic_functionality(self):
-        """Test basic encode/decode functionality of MistralTokenizerWrapper"""
-        cfg = DictDefault(
-            {
-                "base_model": "adamo1139/Mistral-Small-24B-Instruct-2501-ungated",
-                "tokenizer_config": "adamo1139/Mistral-Small-24B-Instruct-2501-ungated",
-            }
-        )
-        tokenizer = load_tokenizer(cfg)
-
-        # Test basic encoding
-        text = "Hello, world!"
-        tokens = tokenizer.encode(text)
-        assert isinstance(tokens, list)
-        assert len(tokens) > 0
-
-        # Test basic decoding
-        decoded = tokenizer.decode(tokens)
-        assert isinstance(decoded, str)
-
-        # Test token properties are accessible
-        assert hasattr(tokenizer, "eos_token_id")
-        assert hasattr(tokenizer, "bos_token_id")
-        assert isinstance(tokenizer.eos_token_id, int)
-        assert isinstance(tokenizer.bos_token_id, int)
+if __name__ == "__main__":
+    unittest.main()