add output for train loss in assertian err

cicd/multigpu.sh

@@ -1,10 +1,13 @@
 #!/bin/bash
 set -e
 
+# only run one test at a time so as not to OOM the GPU
+pytest -v  --durations=10 -n2 /workspace/axolotl/tests/e2e/multigpu/ --ignore=/workspace/axolotl/tests/e2e/multigpu/solo/
+pytest -v  --durations=10 -n1 /workspace/axolotl/tests/e2e/multigpu/solo/
+
 # Only run two tests at a time to avoid OOM on GPU (with coverage collection)
 pytest -v -n2 \
-  --ignore=/workspace/axolotl/tests/e2e/multigpu/solo/ \
+  --ignore=/workspace/axolotl/tests/e2e/multigpu/solo/
-  --ignore=/workspace/axolotl/tests/e2e/multigpu/patched/ \
   /workspace/axolotl/tests/e2e/multigpu/ \
   --cov=axolotl \
   --cov-report=xml:multigpu-coverage.xml
@@ -14,11 +17,6 @@ pytest -v  --durations=10 -n1 /workspace/axolotl/tests/e2e/multigpu/solo/ \
   --cov-append \
   --cov-report=xml:multigpu-coverage.xml
 
-pytest -v  --durations=10 -n1 /workspace/axolotl/tests/e2e/multigpu/patched/ \
-  --cov=axolotl \
-  --cov-append \
-  --cov-report=xml:multigpu-coverage.xml
-
 # Upload coverage to Codecov
 if [ -f multigpu-coverage.xml ]; then
   codecov -f multigpu-coverage.xml -F multigpu,docker-tests,pytorch-${PYTORCH_VERSION}

docs/config.qmd

@@ -55,46 +55,20 @@ overrides_of_model_config:
 overrides_of_model_kwargs:
   # use_cache: False
 
+# optional overrides to the bnb 4bit quantization configuration
+# https://huggingface.co/docs/transformers/main/main_classes/quantization#transformers.BitsAndBytesConfig
+bnb_config_kwargs:
+  # These are default values
+  llm_int8_has_fp16_weight: false
+  bnb_4bit_quant_type: nf4
+  bnb_4bit_use_double_quant: true
 
 
-# Quantization configuration.
-quantization:
-  backend: bnb | hqq | gptq
-  bits: 8
-  # optional overrides to the bnb 4bit quantization configuration
-  # https://huggingface.co/docs/transformers/main/main_classes/quantization#transformers.BitsAndBytesConfig
-  bnb_config_kwargs:
-    # These are default values
-    llm_int8_has_fp16_weight: false
-    bnb_4bit_quant_type: nf4
-    bnb_4bit_use_double_quant: true
-
-  # If using hqq config, additional config paramters are needed. See: https://huggingface.co/docs/transformers/main/en//quantization/hqq
-  hqq_config:
-    # pick one of the following, depending on if you want to uniformly quantize the whole model or
-    # apply different quantization settings to specific layers in the model:
-
-    # if uniformly quantize the whole model:
-    group_size: 64
-    # if we want to invoke dynamic_config in order to apply specific layers with different quantization settings:
-    - nbits: 4
-      group_size: 64
-      target_modules:
-        - self_attn.k_proj
-        - self_attn.v_proj
-        - self_attn.o_proj
-    - nbits: 3
-      group_size: 32
-      target_modules:
-        - mlp.gate_proj
-        - mlp.up_proj
-        - mlp.down_proj
-
-# (Internal Use Only)
 # Whether you are training a 4-bit GPTQ quantized model
-gptq:
+gptq: true
+
 # This will attempt to quantize the model down to 8 bits and use adam 8 bit optimizer
-load_in_8bit:
+load_in_8bit: true
 # Use bitsandbytes 4 bit
 load_in_4bit:
 

requirements.txt

@@ -6,7 +6,7 @@ triton>=3.0.0
 mamba-ssm==1.2.0.post1
 xformers>=0.0.23.post1
 autoawq==0.2.7.post3
-liger-kernel==0.5.8
+liger-kernel==0.5.6
 # END section
 
 packaging==23.2
@@ -22,7 +22,6 @@ hf_xet==1.0.0
 
 optimum==1.16.2
 hf_transfer
-hqq==0.2.5
 sentencepiece
 gradio==5.23.3
 

src/axolotl/core/trainer_builder.py

@@ -1040,11 +1040,9 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
         if self.cfg.dataset_processes:
             training_args_kwargs["dataset_num_proc"] = self.cfg.dataset_processes
 
-        if self.cfg.trl and self.cfg.trl.beta is not None:
+        if (self.cfg.trl and self.cfg.trl.beta) or self.cfg.rl_beta:
-            training_args_kwargs["beta"] = self.cfg.trl.beta
+            training_args_kwargs["beta"] = self.cfg.trl.beta or self.cfg.rl_beta
-        elif self.cfg.rl_beta is not None:
+        if self.cfg.orpo_alpha:
-            training_args_kwargs["beta"] = self.cfg.rl_beta
-        elif self.cfg.orpo_alpha is not None:
             # trl does some odd mapping of alpha to beta to reuse the beta parameter ???
             training_args_kwargs["beta"] = self.cfg.orpo_alpha
 

src/axolotl/integrations/liger/README.md

@@ -25,7 +25,7 @@ liger_fused_linear_cross_entropy: true
 - deepseek_v2
 - gemma
 - gemma2
-- gemma3
+- gemma3 (partial support, no support for FLCE yet)
 - granite
 - jamba
 - llama

src/axolotl/integrations/liger/__init__.py

@@ -21,6 +21,7 @@ It is designed to be performant, correct, and light-weight.
 import inspect
 import logging
 import sys
+from functools import partial
 
 from axolotl.integrations.base import BasePlugin
 
@@ -54,6 +55,7 @@ class LigerPlugin(BasePlugin):
             )
         from liger_kernel.transformers.cross_entropy import LigerCrossEntropyLoss
         from liger_kernel.transformers.functional import liger_cross_entropy
+        from liger_kernel.transformers.geglu import LigerGEGLUMLP
         from liger_kernel.transformers.layer_norm import LigerLayerNorm
         from liger_kernel.transformers.monkey_patch import MODEL_TYPE_TO_APPLY_LIGER_FN
         from liger_kernel.transformers.rms_norm import LigerRMSNorm
@@ -139,6 +141,38 @@ class LigerPlugin(BasePlugin):
                 modeling_mod.CrossEntropyLoss = LigerCrossEntropyLoss
             if cfg.liger_fused_linear_cross_entropy:
                 modeling_mod.DeepseekV2ForCausalLM.forward = deepseekv2_lce_forward
+        elif cfg.model_config_type in ["gemma3", "gemma3_text"]:
+            from transformers.models.gemma3 import modeling_gemma3
+
+            if cfg.liger_rope:
+                modeling_gemma3.apply_rotary_pos_emb = liger_rotary_pos_emb
+            if cfg.liger_rms_norm:
+
+                def _liger_rms_norm_wrapper(dim, **kwargs):
+                    "Convert 'dim' keyword to 'hidden_size' to pass to LigerRMSNorm"
+                    return LigerRMSNorm(hidden_size=dim, **kwargs)
+
+                modeling_gemma3.Gemma3RMSNorm = partial(
+                    _liger_rms_norm_wrapper,
+                    offset=1.0,
+                    casting_mode="gemma",
+                    init_fn="zeros",
+                    in_place=False,
+                )
+            if cfg.liger_glu_activation:
+                modeling_gemma3.Gemma3MLP = LigerGEGLUMLP
+            if cfg.liger_layer_norm:
+                modeling_gemma3.nn.LayerNorm = LigerLayerNorm
+
+            if cfg.liger_cross_entropy:
+                from transformers.loss.loss_utils import nn
+
+                nn.functional.cross_entropy = liger_cross_entropy
+
+            if cfg.liger_fused_linear_cross_entropy:
+                raise NotImplementedError(
+                    "Fused linear cross entropy is not yet supported for Gemma3."
+                )
         elif cfg.model_config_type == "llama4":
             from axolotl.integrations.liger.models.llama4 import (
                 apply_liger_kernel_to_llama4,

src/axolotl/utils/config/__init__.py

@@ -236,18 +236,6 @@ def normalize_config(cfg):
 
     log_gpu_memory_usage(LOG, "baseline", cfg.device)
 
-    if cfg.quantization:
-        if cfg.quantization.backend in ["bnb"]:
-            if cfg.quantization.bits == 8:
-                cfg.load_in_8bit = True
-            elif cfg.quantization.bits == 4:
-                cfg.load_in_4bit = True
-
-        if cfg.quantization.backend == "gptq":
-            cfg.gptq = True
-        elif cfg.quantization.backend == "hqq":
-            cfg.hqq = True
-
 
 def normalize_cfg_datasets(cfg):
     """

src/axolotl/utils/data/sft.py

@@ -3,7 +3,6 @@
 import functools
 import logging
 import os
-import tempfile
 from pathlib import Path
 from typing import List, Optional, Tuple, Union
 
@@ -118,27 +117,9 @@ def prepare_dataset(cfg, tokenizer, processor=None, preprocess_iterable=None):
             cfg.pretraining_dataset[0]["type"] or "pretrain",
         )
 
-        # when letting accelerator dispatch batches from the main process, we don't need to load the dataset from
+        iter_ds = load_dataset(
-        # other ranks, we just need to present a fake dataset
+            path, streaming=True, split=split, name=name, data_files=data_files
-        if (
+        )
-            cfg.accelerator_config
-            and cfg.accelerator_config.dispatch_batches
-            and not is_local_main_process()
-        ):
-            with tempfile.NamedTemporaryFile(mode="w+", delete=False) as f:
-                f.write("text\n")
-                f.write("lorem ipsum dolor sit amet\n")
-                # rewind the file pointer to the beginning so we can read it again
-                f.seek(0)
-                iter_ds = load_dataset(
-                    "csv", data_files=f.name, split="train", streaming=True
-                )
-        else:
-            if is_local_main_process():
-                iter_ds = load_dataset(
-                    path, streaming=True, split=split, name=name, data_files=data_files
-                )
-
         if skip:
             LOG.info(f"Skipping {skip} samples from the dataset")
             iter_ds = iter_ds.skip(skip)

src/axolotl/utils/models.py

@@ -36,7 +36,6 @@ from transformers import (
     BitsAndBytesConfig,
     Gemma3ForConditionalGeneration,
     GPTQConfig,
-    HqqConfig,
     Llama4ForConditionalGeneration,
     LlavaForConditionalGeneration,
     Mistral3ForConditionalGeneration,
@@ -834,13 +833,6 @@ class ModelLoader:
             del self.model_kwargs["device_map"]
 
     def set_quantization_config(self) -> None:
-        if (
-            (not self.cfg.quantization)
-            and (not self.cfg.load_in_8bit)
-            and (not self.cfg.load_in_4bit)
-            and not self.cfg.gptq
-        ):
-            return
         self.model_kwargs["load_in_8bit"] = self.cfg.load_in_8bit
         self.model_kwargs["load_in_4bit"] = self.cfg.load_in_4bit
 
@@ -862,21 +854,21 @@ class ModelLoader:
             and hasattr(self.model_config, "quantization_config")
             and self.model_config.quantization_config["quant_method"]
             in ["gptq", "awq", "bitsandbytes"]
-            and not self.cfg.hqq
         ):
-            quant_config_class_dict = {
+            if self.model_config.quantization_config["quant_method"] == "gptq":
-                "gptq": GPTQConfig,
+                self.model_kwargs["quantization_config"] = GPTQConfig(
-                "awq": AwqConfig,
+                    **self.model_config.quantization_config
-                "bitsandbytes": BitsAndBytesConfig,
+                )
-            }
+            elif self.model_config.quantization_config["quant_method"] == "awq":
-
+                self.model_kwargs["quantization_config"] = AwqConfig(
-            quant_config_class = quant_config_class_dict[
+                    **self.model_config.quantization_config
-                self.model_config.quantization_config["quant_method"]
+                )
-            ]
+            elif (
-            self.model_kwargs["quantization_config"] = quant_config_class(
+                self.model_config.quantization_config["quant_method"] == "bitsandbytes"
-                **self.model_config.quantization_config
+            ):
-            )
+                self.model_kwargs["quantization_config"] = BitsAndBytesConfig(
-
+                    **self.model_config.quantization_config
+                )
         elif self.cfg.adapter == "qlora" and self.model_kwargs["load_in_4bit"]:
             bnb_config = {
                 "load_in_4bit": True,
@@ -894,8 +886,8 @@ class ModelLoader:
                 # but deepspeed needs this still in bfloat16
                 bnb_config["bnb_4bit_quant_storage"] = torch.float32
 
-            if self.cfg.quantization and self.cfg.quantization.bnb_config_kwargs:
+            if self.cfg.bnb_config_kwargs:
-                bnb_config.update(self.cfg.quantization.bnb_config_kwargs)
+                bnb_config.update(self.cfg.bnb_config_kwargs)
 
             self.model_kwargs["quantization_config"] = BitsAndBytesConfig(
                 **bnb_config,
@@ -911,13 +903,6 @@ class ModelLoader:
                 **bnb_config,
             )
 
-        if self.cfg.hqq:
-            from axolotl.utils.schemas.quant import get_hqq_quant_config_kwargs
-
-            self.model_kwargs["quantization_config"] = HqqConfig(
-                **get_hqq_quant_config_kwargs(self.cfg)
-            )
-
         # no longer needed per https://github.com/huggingface/transformers/pull/26610
         if "quantization_config" in self.model_kwargs or self.cfg.gptq:
             self.model_kwargs.pop("load_in_8bit", None)
@@ -1051,12 +1036,6 @@ class ModelLoader:
                         config=self.model_config,
                     )
             else:
-                if self.cfg.hqq and torch.cuda.device_count() < 2:
-                    # for some reason on single gpu, we need to set device_map to auto/cuda
-                    # otherwise you run into tensors on two devices error during training
-                    # Doesn't affect multi-gpu tho
-
-                    self.model_kwargs["device_map"] = "auto"
                 self.model = self.auto_model_loader.from_pretrained(
                     self.base_model,
                     config=self.model_config,
@@ -1211,7 +1190,7 @@ class ModelLoader:
         if (
             not skip_prepare_model_for_kbit_training
             and self.cfg.adapter in ["lora", "qlora"]
-            and (self.cfg.load_in_8bit or self.cfg.load_in_4bit or self.cfg.hqq)
+            and (self.cfg.load_in_8bit or self.cfg.load_in_4bit)
         ):
             LOG.info("converting PEFT model w/ prepare_model_for_kbit_training")
             self.model = prepare_model_for_kbit_training(
@@ -1481,16 +1460,7 @@ def load_llama_adapter(model, cfg):
 
 
 def find_all_linear_names(model):
-    from hqq.core.peft import HQQLinearLoRA
+    cls = (bnb.nn.Linear4bit, bnb.nn.Linear8bitLt, torch.nn.Linear)
-    from hqq.core.quantize import HQQLinear
-
-    cls = (
-        bnb.nn.Linear4bit,
-        bnb.nn.Linear8bitLt,
-        torch.nn.Linear,
-        HQQLinear,
-        HQQLinearLoRA,
-    )
     lora_module_names = set()
     for name, module in model.named_modules():
         if (

src/axolotl/utils/schedulers.py

@@ -40,7 +40,7 @@ class RexLR(LRScheduler):
         self.max_lr = max_lr
         self.total_steps = total_steps
         self.num_warmup_steps = num_warmup_steps
-        self.last_step = max(last_step - 1, 0)
+        self.last_step = last_step - 1
 
         # Ensure each parameter group has an "initial_lr" key to avoid issues when resuming.
         for group in optimizer.param_groups:

src/axolotl/utils/schemas/config.py

@@ -660,7 +660,6 @@ class AxolotlInputConfig(
             data.get("val_set_size") == 0
             and (data.get("eval_steps") or data.get("eval_strategy"))
             and not data.get("test_datasets")
-            and data.get("eval_strategy") != "no"
         ):
             raise ValueError(
                 "eval_steps and eval_strategy are not supported with val_set_size == 0"

src/axolotl/utils/schemas/peft.py

@@ -1,8 +1,8 @@
 """Pydantic models for PEFT-related configuration"""
 
-from pydantic import BaseModel, Field, field_validator, model_validator
+from typing import Any
 
-from axolotl.utils.schemas.quant import QuantizationConfig
+from pydantic import BaseModel, Field, field_validator, model_validator
 
 
 class LoftQConfig(BaseModel):
@@ -23,11 +23,8 @@ class PeftConfig(BaseModel):
 class LoraConfig(BaseModel):
     """Peft / LoRA configuration subset"""
 
-    quantization: QuantizationConfig | None = None
+    load_in_8bit: bool | None = Field(default=False)
-    load_in_4bit: bool | None = None  # for internal use
+    load_in_4bit: bool | None = Field(default=False)
-    load_in_8bit: bool | None = None  # for internal use
-    hqq: bool | None = None  # for internal use
-    gptq: bool | None = None  # for internal use
 
     adapter: str | None = None
     lora_model_dir: str | None = None
@@ -53,6 +50,8 @@ class LoraConfig(BaseModel):
         },
     )
     lora_on_cpu: bool | None = None
+    gptq: bool | None = None
+    bnb_config_kwargs: dict[str, Any] | None = None
 
     loraplus_lr_ratio: float | None = Field(
         default=None,
@@ -75,11 +74,11 @@ class LoraConfig(BaseModel):
         if (
             not data.get("adapter")
             and not data.get("inference")
-            and (data.get("quantization"))
+            and (data.get("load_in_8bit") or data.get("load_in_4bit"))
         ):
             raise ValueError(
-                "Quantization is not supported without setting an adapter."
+                "load_in_8bit and load_in_4bit are not supported without setting an adapter for training."
-                "If you want to full finetune, please turn off Quantization."
+                "If you want to full finetune, please turn off load_in_8bit and load_in_4bit."
             )
         return data
 
@@ -87,26 +86,25 @@ class LoraConfig(BaseModel):
     def validate_qlora(self):
         if self.adapter == "qlora":
             if self.merge_lora:
-                if self.quantization.bits == 8 or self.load_in_8bit:
+                # can't merge qlora if loaded in 8bit or 4bit
+                if self.load_in_8bit:
                     raise ValueError("Can't merge qlora if loaded in 8bit")
 
-                if self.quantization.backend == "gptq":
+                if self.gptq:
-                    raise ValueError("Can't merge qlora if using gptq")
+                    raise ValueError("Can't merge qlora if gptq")
 
-                if self.quantization.bits == 4 or self.load_in_4bit:
+                if self.load_in_4bit:
                     raise ValueError("Can't merge qlora if loaded in 4bit")
 
             else:
-                if self.quantization:
+                if self.load_in_8bit:
-                    if self.quantization.bits == 8 or self.load_in_8bit:
+                    raise ValueError("Can't load qlora in 8bit")
-                        raise ValueError("Can't load qlora in 8bit")
 
-                    if self.quantization.backend == "gptq":
+                if self.gptq:
-                        raise ValueError("Can't load qlora if using gptq")
+                    raise ValueError("Can't load qlora if gptq")
-
-                    if not self.quantization.bits == 4 or self.load_in_4bit:
-                        raise ValueError("Require quantization.bits <= 4 for qlora")
 
+                if not self.load_in_4bit:
+                    raise ValueError("Require cfg.load_in_4bit to be True for qlora")
         return self
 
     @field_validator("loraplus_lr_embedding")
@@ -123,24 +121,6 @@ class LoraConfig(BaseModel):
             data["lora_dropout"] = 0.0
         return data
 
-    @model_validator(mode="before")
-    @classmethod
-    def validate_hqq(cls, data):
-        if (
-            data.get("quantization")
-            and data.get("quantization").get("backend") == "hqq"
-        ):
-            if not data.get("quantization").get("hqq_config"):
-                raise ValueError(
-                    "If using HQQ, must set `hqq_config` under `quantization`"
-                )
-
-            if data.get("load_in_4bit") or data.get("load_in_8bit"):
-                raise ValueError(
-                    "If using HQQ quantization, please remove load_in_4bit or load_in_8bit"
-                )
-        return data
-
 
 class ReLoRAConfig(BaseModel):
     """ReLoRA configuration subset"""

src/axolotl/utils/schemas/quant.py

@@ -1,93 +0,0 @@
-""" "
-Takes care of quantization configuration
-"""
-
-from typing import Annotated, Any, Literal
-
-from annotated_types import MinLen
-from pydantic import BaseModel, Field, model_validator
-
-
-class HQQConfig(BaseModel):
-    """HQQ configuration subset"""
-
-    nbits: Literal[8, 4, 3, 2, 1] | None = Field(
-        default=None,
-        json_schema_extra={
-            "description": "Number of bits for HQQ quantization. 8, 4, 3, 2, or 1."
-        },
-    )
-
-    group_size: int = Field(default=64)
-    target_modules: list[str] | str | None = Field(
-        default=None,
-        json_schema_extra={
-            "description": "Target modules for HQQ quantization. If not specified, the whole model will be quantized."
-        },
-    )
-
-
-class QuantizationConfig(BaseModel):
-    """Over all Quantization configuration subset"""
-
-    # We will use this class as base future refactoring of all quantization configs
-    backend: Literal["bnb", "hqq", "gptq"] | None = None
-    bits: Literal[8, 4, 3, 2, 1] | None = None
-    bnb_config_kwargs: dict[str, Any] | None = None
-    hqq_config: HQQConfig | Annotated[list[HQQConfig], MinLen(1)] | None = None
-
-    @model_validator(mode="before")
-    @classmethod
-    def check_hqq_config(cls, data):
-        if data.get("backend") == "hqq" and not data.get("hqq_config"):
-            raise ValueError("If using HQQ, must set `group_size` under `hqq_config`")
-
-        if data.get("hqq_config") and len(data.get("hqq_config")) > 1:
-            for hqq_config in data.get("hqq_config"):
-                if hqq_config.get("target_modules") is None:
-                    raise ValueError(
-                        "For list of hqq configs, `target_modules` must be specified for each"
-                    )
-
-        return data
-
-
-def get_hqq_quant_config_kwargs(cfg):
-
-    # If no target module is specified, then target the whole model
-    if not isinstance(cfg.quantization.hqq_config, list):
-        cfg.quantization.hqq_config = [cfg.quantization.hqq_config]
-
-    if (
-        len(cfg.quantization.hqq_config) == 1
-        and cfg.quantization.hqq_config[0].target_modules is None
-    ):
-
-        nbits = (
-            cfg.quantization.hqq_config[0].nbits
-            if cfg.quantization.hqq_config[0].nbits is not None
-            else cfg.quantization.bits
-        )
-
-        return {
-            "nbits": nbits,
-            "group_size": cfg.quantization.hqq_config[0].group_size,
-        }
-
-    hqq_quant_config_kwargs = {"dynamic_config": {}}
-    for hqq_config in cfg.quantization.hqq_config:
-        nbits = (
-            hqq_config.nbits if hqq_config.nbits is not None else cfg.quantization.bits
-        )
-
-        target_modules = hqq_config.target_modules
-        if not isinstance(target_modules, list):
-            target_modules = [target_modules]
-
-        for module in target_modules:
-            hqq_quant_config_kwargs["dynamic_config"][module] = {
-                "nbits": nbits,
-                "group_size": hqq_config.group_size,
-            }
-
-    return hqq_quant_config_kwargs

tests/conftest.py

@@ -193,14 +193,6 @@ def download_tiny_shakespeare_dataset():
     snapshot_download_w_retry("winglian/tiny-shakespeare", repo_type="dataset")
 
 
-@pytest.fixture(scope="session", autouse=True)
-def download_evolkit_kd_sample_dataset():
-    # download the dataset
-    snapshot_download_w_retry(
-        "axolotl-ai-co/evolkit-logprobs-pipeline-75k-v2-sample", repo_type="dataset"
-    )
-
-
 @pytest.fixture(scope="session", autouse=True)
 def download_deepseek_model_fixture():
     snapshot_download_w_retry("axolotl-ai-co/DeepSeek-V3-11M", repo_type="model")
@@ -216,16 +208,6 @@ def download_huggyllama_model_fixture():
     )
 
 
-@pytest.fixture(scope="session", autouse=True)
-def download_llama33_70b_model_fixture():
-    # download the tokenizer only
-    snapshot_download_w_retry(
-        "axolotl-ai-co/Llama-3.3-70B-Instruct-tokenizer",
-        repo_type="model",
-        allow_patterns=["*token*", "config.json"],
-    )
-
-
 @pytest.fixture(scope="session", autouse=True)
 def download_llama_1b_model_fixture():
     # download the tokenizer only
@@ -333,14 +315,6 @@ def download_llama2_model_fixture():
     )
 
 
-@pytest.fixture(scope="session", autouse=True)
-def download_llama32_1b_model_fixture():
-    snapshot_download_w_retry(
-        "osllmai-community/Llama-3.2-1B",
-        repo_type="model",
-    )
-
-
 @pytest.fixture
 @enable_hf_offline
 def tokenizer_huggyllama(
@@ -522,6 +496,12 @@ def dataset_fozziethebeat_alpaca_messages_2k_dpo_test_rev_ea82cff(
     return datasets.load_from_disk(ds_path)["train"]
 
 
+@pytest.fixture(scope="session", autouse=True)
+def download_tiny_llama_7m_model():
+    # download the model
+    return snapshot_download_w_retry("axolotl-ai-internal/llama-7m", repo_type="model")
+
+
 # # pylint: disable=redefined-outer-name,unused-argument
 # def test_load_fixtures(
 #     download_smollm2_135m_model,

tests/e2e/integrations/test_kd.py

@@ -90,7 +90,7 @@ class TestKnowledgeDistillation:
         train(cfg=cfg, dataset_meta=dataset_meta)
         assert (Path(temp_dir) / "model.safetensors").exists()
         check_tensorboard(
-            temp_dir + "/runs", "train/loss", 1.0, "Train Loss is too high"
+            temp_dir + "/runs", "train/loss", 1.0, "Train loss (%s) is too high"
         )
 
     @pytest.mark.parametrize(
@@ -121,5 +121,5 @@ class TestKnowledgeDistillation:
         train(cfg=cfg, dataset_meta=dataset_meta)
         assert (Path(temp_dir) / "adapter_model.safetensors").exists()
         check_tensorboard(
-            temp_dir + "/runs", "train/loss", 1.0, "Train Loss is too high"
+            temp_dir + "/runs", "train/loss", 1.0, "Train loss (%s) is too high"
         )

tests/e2e/multigpu/solo/__init__.py

@@ -1,2 +0,0 @@
-# Tests under this directory should get run "solo" on their own as they
-# seem to cause issues when run in the same batch as other tests.

tests/e2e/multigpu/solo/test_flex.py

@@ -49,9 +49,8 @@ class TestPackedFlex:
                 },
                 "datasets": [
                     {
-                        "path": "tatsu-lab/alpaca",
+                        "path": "vicgalle/alpaca-gpt4",
                         "type": "alpaca",
-                        "split": "train[:10%]",
                     },
                 ],
                 "num_epochs": 1,
@@ -90,5 +89,5 @@ class TestPackedFlex:
         )
 
         check_tensorboard(
-            temp_dir + "/runs", "train/train_loss", 2.0, "Train Loss is too high"
+            temp_dir + "/runs", "train/train_loss", 2.0, "Train loss (%s) is too high"
         )

tests/e2e/multigpu/test_eval.py

@@ -30,10 +30,8 @@ class TestMultiGPUEval:
         cfg = DictDefault(
             {
                 "base_model": "HuggingFaceTB/SmolLM2-135M",
-                "quantization": {
+                "load_in_8bit": False,
-                    "backend": "bnb",
+                "load_in_4bit": True,
-                    "bits": 4,
-                },
                 "strict": False,
                 "sequence_len": 2048,
                 "adapter": "qlora",
@@ -101,10 +99,8 @@ class TestMultiGPUEval:
         cfg = DictDefault(
             {
                 "base_model": "HuggingFaceTB/SmolLM2-135M",
-                "quantization": {
+                "load_in_8bit": False,
-                    "backend": "bnb",
+                "load_in_4bit": True,
-                    "bits": 4,
-                },
                 "strict": False,
                 "sequence_len": 2048,
                 "adapter": "qlora",

tests/e2e/multigpu/test_gemma3.py

@@ -96,5 +96,5 @@ class TestMultiGPUGemma3:
         )
 
         check_tensorboard(
-            temp_dir + "/runs", "train/train_loss", 1.8, "Train Loss is too high"
+            temp_dir + "/runs", "train/train_loss", 1.8, "Train loss (%s) is too high"
         )

tests/e2e/multigpu/test_llama.py

@@ -43,7 +43,7 @@ class TestMultiGPULlama:
         # pylint: disable=duplicate-code
         cfg = DictDefault(
             {
-                "base_model": "HuggingFaceTB/SmolLM2-135M",
+                "base_model": "axolotl-ai-internal/llama-7m",
                 "sequence_len": 2048,
                 "adapter": "lora",
                 "lora_r": 8,
@@ -94,7 +94,7 @@ class TestMultiGPULlama:
         )
 
         check_tensorboard(
-            temp_dir + "/runs", "train/train_loss", 2.3, "Train Loss is too high"
+            temp_dir + "/runs", "train/train_loss", 2.3, "Train loss (%s) is too high"
         )
 
     @pytest.mark.parametrize(
@@ -105,7 +105,7 @@ class TestMultiGPULlama:
         # pylint: disable=duplicate-code
         cfg = DictDefault(
             {
-                "base_model": "HuggingFaceTB/SmolLM2-135M",
+                "base_model": "axolotl-ai-internal/llama-7m",
                 "sequence_len": 2048,
                 "sample_packing": True,
                 "eval_sample_packing": False,
@@ -159,22 +159,19 @@ class TestMultiGPULlama:
         )
 
         check_tensorboard(
-            temp_dir + "/runs", "train/train_loss", 2.3, "Train Loss is too high"
+            temp_dir + "/runs", "train/train_loss", 2.3, "Train loss (%s) is too high"
         )
 
     def test_dpo_lora_ddp(self, temp_dir):
         # pylint: disable=duplicate-code
         cfg = DictDefault(
             {
-                "base_model": "HuggingFaceTB/SmolLM2-135M",
+                "base_model": "axolotl-ai-internal/llama-7m",
                 "sequence_len": 2048,
                 "sample_packing": False,
                 "eval_sample_packing": False,
                 "pad_to_sequence_len": True,
-                "quantization": {
+                "load_in_8bit": True,
-                    "backend": "bnb",
-                    "bits": 8,
-                },
                 "adapter": "lora",
                 "lora_r": 8,
                 "lora_alpha": 16,
@@ -247,15 +244,12 @@ class TestMultiGPULlama:
         # pylint: disable=duplicate-code
         cfg = DictDefault(
             {
-                "base_model": "HuggingFaceTB/SmolLM2-135M",
+                "base_model": "axolotl-ai-internal/llama-7m",
                 "sequence_len": 2048,
                 "sample_packing": False,
                 "eval_sample_packing": False,
                 "pad_to_sequence_len": True,
-                "quantization": {
+                "load_in_4bit": True,
-                    "backend": "bnb",
-                    "bits": 4,
-                },
                 "adapter": "qlora",
                 "lora_r": 8,
                 "lora_alpha": 16,
@@ -332,7 +326,7 @@ class TestMultiGPULlama:
         # pylint: disable=duplicate-code
         cfg = DictDefault(
             {
-                "base_model": "HuggingFaceTB/SmolLM2-135M",
+                "base_model": "axolotl-ai-internal/llama-7m",
                 "sequence_len": 2048,
                 "val_set_size": 0.01,
                 "special_tokens": {
@@ -391,7 +385,7 @@ class TestMultiGPULlama:
         )
 
         check_tensorboard(
-            temp_dir + "/runs", "train/train_loss", 2.3, "Train Loss is too high"
+            temp_dir + "/runs", "train/train_loss", 2.3, "Train loss (%s) is too high"
         )
 
     @pytest.mark.parametrize(
@@ -402,7 +396,7 @@ class TestMultiGPULlama:
         # pylint: disable=duplicate-code
         cfg = DictDefault(
             {
-                "base_model": "HuggingFaceTB/SmolLM2-135M",
+                "base_model": "axolotl-ai-internal/llama-7m",
                 "sample_packing": True,
                 "pad_to_sequence_len": True,
                 "sequence_len": 1024,
@@ -463,7 +457,7 @@ class TestMultiGPULlama:
         )
 
         check_tensorboard(
-            temp_dir + "/runs", "train/train_loss", 2.3, "Train Loss is too high"
+            temp_dir + "/runs", "train/train_loss", 2.3, "Train loss (%s) is too high"
         )
 
     @require_torch_2_6_0
@@ -481,7 +475,7 @@ class TestMultiGPULlama:
         # pylint: disable=duplicate-code
         cfg = DictDefault(
             {
-                "base_model": "HuggingFaceTB/SmolLM2-135M",
+                "base_model": "axolotl-ai-internal/llama-7m",
                 "sample_packing": True,
                 "pad_to_sequence_len": True,
                 "sequence_len": 2048,
@@ -544,7 +538,7 @@ class TestMultiGPULlama:
         )
 
         check_tensorboard(
-            temp_dir + "/runs", "train/train_loss", 2.1, "Train Loss is too high"
+            temp_dir + "/runs", "train/train_loss", 2.1, "Train loss (%s) is too high"
         )
 
     def test_fsdp_qlora_prequant_packed(self, temp_dir):
@@ -554,10 +548,7 @@ class TestMultiGPULlama:
                 "base_model": "axolotl-ai-co/SmolLM2-135M-bnb-nf4-bf16",
                 "adapter": "qlora",
                 "mean_resizing_embeddings": True,
-                "quantization": {
+                "load_in_4bit": True,
-                    "backend": "bnb",
-                    "bits": 4,
-                },
                 "lora_r": 8,
                 "lora_alpha": 16,
                 "lora_dropout": 0.05,
@@ -627,7 +618,7 @@ class TestMultiGPULlama:
         )
 
         check_tensorboard(
-            temp_dir + "/runs", "train/train_loss", 2.3, "Train Loss is too high"
+            temp_dir + "/runs", "train/train_loss", 2.3, "Train loss (%s) is too high"
         )
 
     @pytest.mark.parametrize(
@@ -657,16 +648,13 @@ class TestMultiGPULlama:
                 "lora_alpha": 16,
                 "lora_dropout": 0.05,
                 "lora_target_linear": True,
-                "quantization": {
+                "load_in_4bit": True,
-                    "backend": "bnb",
-                    "bits": 4,
-                },
             }
         else:
             adapter = {}
         cfg = DictDefault(
             {
-                "base_model": "HuggingFaceTB/SmolLM2-135M",
+                "base_model": "axolotl-ai-internal/llama-7m",
                 "sample_packing": True,
                 "pad_to_sequence_len": True,
                 "sequence_len": 1024,
@@ -714,7 +702,7 @@ class TestMultiGPULlama:
         )
 
         check_tensorboard(
-            temp_dir + "/runs", "train/train_loss", 2.3, "Train Loss is too high"
+            temp_dir + "/runs", "train/train_loss", 2.3, "Train loss (%s) is too high"
         )
 
     @pytest.mark.parametrize(
@@ -734,16 +722,13 @@ class TestMultiGPULlama:
                 "lora_alpha": 16,
                 "lora_dropout": 0.05,
                 "lora_target_linear": True,
-                "quantization": {
+                "load_in_4bit": True,
-                    "backend": "bnb",
-                    "bits": 4,
-                },
             }
         else:
             adapter = {}
         cfg = DictDefault(
             {
-                "base_model": "HuggingFaceTB/SmolLM2-135M",
+                "base_model": "axolotl-ai-internal/llama-7m",
                 "sample_packing": True,
                 "pad_to_sequence_len": True,
                 "sequence_len": 1024,
@@ -791,7 +776,7 @@ class TestMultiGPULlama:
         )
 
         check_tensorboard(
-            temp_dir + "/runs", "train/train_loss", 2.3, "Train Loss is too high"
+            temp_dir + "/runs", "train/train_loss", 2.3, "Train loss (%s) is too high"
         )
 
     @pytest.mark.parametrize(
@@ -811,16 +796,13 @@ class TestMultiGPULlama:
                 "lora_alpha": 16,
                 "lora_dropout": 0.05,
                 "lora_target_linear": True,
-                "quantization": {
+                "load_in_4bit": True,
-                    "backend": "bnb",
-                    "bits": 4,
-                },
             }
         else:
             adapter = {}
         cfg = DictDefault(
             {
-                "base_model": "HuggingFaceTB/SmolLM2-135M",
+                "base_model": "axolotl-ai-internal/llama-7m",
                 "sample_packing": True,
                 "pad_to_sequence_len": True,
                 "sequence_len": 1024,
@@ -868,7 +850,7 @@ class TestMultiGPULlama:
         )
 
         check_tensorboard(
-            temp_dir + "/runs", "train/train_loss", 2.3, "Train Loss is too high"
+            temp_dir + "/runs", "train/train_loss", 2.3, "Train loss (%s) is too high"
         )
 
     @pytest.mark.skip(
@@ -878,7 +860,7 @@ class TestMultiGPULlama:
         # pylint: disable=duplicate-code
         cfg = DictDefault(
             {
-                "base_model": "HuggingFaceTB/SmolLM2-135M",
+                "base_model": "axolotl-ai-internal/llama-7m",
                 "fix_untrained_tokens": True,
                 "sequence_len": 512,
                 "val_set_size": 0.0,
@@ -935,5 +917,5 @@ class TestMultiGPULlama:
         )
 
         check_tensorboard(
-            temp_dir + "/runs", "train/train_loss", 4.0, "Train Loss is too high"
+            temp_dir + "/runs", "train/train_loss", 4.0, "Train loss (%s) is too high"
         )

tests/e2e/multigpu/test_qwen2.py

@@ -28,10 +28,7 @@ class TestMultiGPUQwen2:
         cfg = DictDefault(
             {
                 "base_model": base_model,
-                "quantization": {
+                "load_in_4bit": True,
-                    "backend": "bnb",
-                    "bits": 4,
-                },
                 "rl": "dpo",
                 "chat_template": "chatml",
                 "sequence_len": 2048,

tests/e2e/multigpu/test_ray.py

@@ -80,7 +80,7 @@ class TestMultiGPURay:
         )
 
         check_tensorboard(
-            temp_dir + "/runs", "train/train_loss", 2.3, "Train Loss is too high"
+            temp_dir + "/runs", "train/train_loss", 2.3, "Train loss (%s) is too high"
         )
 
     @require_torch_lt_2_6_0
@@ -138,5 +138,5 @@ class TestMultiGPURay:
         )
 
         check_tensorboard(
-            temp_dir + "/runs", "train/train_loss", 2.3, "Train Loss is too high"
+            temp_dir + "/runs", "train/train_loss", 2.3, "Train loss (%s) is too high"
         )

tests/e2e/multigpu/test_sp.py

@@ -10,7 +10,7 @@ from transformers.testing_utils import get_torch_dist_unique_port
 
 from axolotl.utils.dict import DictDefault
 
-from ...utils import check_tensorboard
+from ..utils import check_tensorboard
 
 os.environ["WANDB_DISABLED"] = "true"
 
@@ -93,7 +93,7 @@ class TestSequenceParallelism:
         )
 
         check_tensorboard(
-            temp_dir + "/runs", "train/train_loss", 2.6, "Train Loss is too high"
+            temp_dir + "/runs", "train/train_loss", 2.6, "Train loss (%s) is too high"
         )
 
     @pytest.mark.parametrize(

tests/e2e/patched/test_fa_xentropy.py

@@ -86,5 +86,5 @@ class TestFAXentropyLlama:
         check_model_output_exists(temp_dir, cfg)
 
         check_tensorboard(
-            temp_dir + "/runs", "train/train_loss", 1.5, "Train Loss is too high"
+            temp_dir + "/runs", "train/train_loss", 1.5, "Train loss (%s) is too high"
         )

tests/e2e/patched/test_falcon_samplepack.py

@@ -32,10 +32,7 @@ class TestFalconPatched(unittest.TestCase):
                 "flash_attention": True,
                 "sample_packing": True,
                 "sequence_len": 2048,
-                "quantization": {
+                "load_in_4bit": True,
-                    "backend": "bnb",
-                    "bits": 4,
-                },
                 "adapter": "qlora",
                 "lora_r": 16,
                 "lora_alpha": 32,

tests/e2e/patched/test_lora_llama_multipack.py

@@ -89,9 +89,6 @@ class TestLoraLlama(unittest.TestCase):
                 "sequence_len": 1024,
                 "sample_packing": True,
                 "flash_attention": True,
-                "quantization": {
-                    "backend": "gptq",
-                },
                 "load_in_8bit": True,
                 "adapter": "lora",
                 "gptq": True,

tests/e2e/patched/test_mixtral_samplepack.py

@@ -33,10 +33,7 @@ class TestMixtral(unittest.TestCase):
                 "flash_attention": True,
                 "sample_packing": True,
                 "sequence_len": 2048,
-                "quantization": {
+                "load_in_4bit": True,
-                    "backend": "bnb",
-                    "bits": 4,
-                },
                 "adapter": "qlora",
                 "lora_r": 16,
                 "lora_alpha": 32,

tests/e2e/patched/test_resume.py

@@ -46,9 +46,8 @@ class TestResumeLlama:
                 },
                 "datasets": [
                     {
-                        "path": "tatsu-lab/alpaca",
+                        "path": "vicgalle/alpaca-gpt4",
                         "type": "alpaca",
-                        "split": "train[:10%]",
                     },
                 ],
                 "num_epochs": 2,

tests/e2e/patched/test_unsloth_qlora.py

@@ -80,7 +80,7 @@ class TestUnslothQLoRA:
         check_model_output_exists(temp_dir, cfg)
 
         check_tensorboard(
-            temp_dir + "/runs", "train/train_loss", 2.0, "Train Loss is too high"
+            temp_dir + "/runs", "train/train_loss", 2.0, "Train loss (%s) is too high"
         )
 
     def test_unsloth_llama_qlora_unpacked(self, temp_dir):
@@ -130,7 +130,7 @@ class TestUnslothQLoRA:
         check_model_output_exists(temp_dir, cfg)
 
         check_tensorboard(
-            temp_dir + "/runs", "train/train_loss", 2.0, "Train Loss is too high"
+            temp_dir + "/runs", "train/train_loss", 2.0, "Train loss (%s) is too high"
         )
 
     @pytest.mark.parametrize(
@@ -185,5 +185,5 @@ class TestUnslothQLoRA:
         check_model_output_exists(temp_dir, cfg)
 
         check_tensorboard(
-            temp_dir + "/runs", "train/train_loss", 2.0, "Train Loss is too high"
+            temp_dir + "/runs", "train/train_loss", 2.0, "Train loss (%s) is too high"
         )

tests/e2e/solo/test_flex.py

@@ -41,9 +41,8 @@ class TestPackedFlex(unittest.TestCase):
                 },
                 "datasets": [
                     {
-                        "path": "tatsu-lab/alpaca",
+                        "path": "vicgalle/alpaca-gpt4",
                         "type": "alpaca",
-                        "split": "train[:10%]",
                     },
                 ],
                 "num_epochs": 1,
@@ -70,5 +69,5 @@ class TestPackedFlex(unittest.TestCase):
         train(cfg=cfg, dataset_meta=dataset_meta)
 
         check_tensorboard(
-            temp_dir + "/runs", "train/train_loss", 2.0, "Train Loss is too high"
+            temp_dir + "/runs", "train/train_loss", 2.0, "Train loss (%s) is too high"
         )

tests/e2e/solo/test_relora_llama.py

@@ -34,10 +34,7 @@ class TestReLoraLlama(unittest.TestCase):
                 "sample_packing": True,
                 "pad_to_sequence_len": True,
                 "flash_attention": True,
-                "quantization": {
+                "load_in_8bit": True,
-                    "backend": "bnb",
-                    "bits": 8,
-                },
                 "adapter": "lora",
                 "lora_r": 8,
                 "lora_alpha": 16,

tests/e2e/test_llama_pretrain.py

@@ -84,5 +84,5 @@ class TestPretrainLlama:
             temp_dir + "/runs",
             "train/train_loss",
             loss_threshold,
-            "Train Loss is too high",
+            "Train Loss (%s) is too high",
         )

tests/e2e/test_mixtral.py

@@ -35,10 +35,7 @@ class TestMixtral(unittest.TestCase):
                 "tokenizer_config": "LoneStriker/Mixtral-8x7B-v0.1-HF",
                 "flash_attention": True,
                 "sequence_len": 1024,
-                "quantization": {
+                "load_in_4bit": True,
-                    "backend": "bnb",
-                    "bits": 4,
-                },
                 "adapter": "qlora",
                 "lora_r": 4,
                 "lora_alpha": 8,
@@ -94,10 +91,7 @@ class TestMixtral(unittest.TestCase):
                 "tokenizer_config": "LoneStriker/Mixtral-8x7B-v0.1-HF",
                 "flash_attention": False,
                 "sequence_len": 1024,
-                "quantization": {
+                "load_in_4bit": True,
-                    "backend": "bnb",
-                    "bits": 4,
-                },
                 "adapter": "qlora",
                 "lora_r": 4,
                 "lora_alpha": 8,

tests/e2e/test_packing_loss.py

@@ -40,9 +40,8 @@ class TestPackedLlama(unittest.TestCase):
                 },
                 "datasets": [
                     {
-                        "path": "tatsu-lab/alpaca",
+                        "path": "vicgalle/alpaca-gpt4",
                         "type": "alpaca",
-                        "split": "train[:10%]",
                     },
                 ],
                 "num_epochs": 1,
@@ -69,5 +68,5 @@ class TestPackedLlama(unittest.TestCase):
         train(cfg=cfg, dataset_meta=dataset_meta)
 
         check_tensorboard(
-            temp_dir + "/runs", "train/train_loss", 2.0, "Train Loss is too high"
+            temp_dir + "/runs", "train/train_loss", 2.0, "Train loss (%s) is too high"
         )

tests/e2e/test_quantization.py

@@ -1,141 +0,0 @@
-"""
-E2E tests for training with quantized model
-"""
-
-import logging
-import os
-import unittest
-
-from transformers.utils import is_torch_bf16_gpu_available
-
-from axolotl.cli.args import TrainerCliArgs
-from axolotl.common.datasets import load_datasets
-from axolotl.train import train
-from axolotl.utils.config import normalize_config, validate_config
-from axolotl.utils.dict import DictDefault
-
-from .utils import check_tensorboard, with_temp_dir
-
-LOG = logging.getLogger("axolotl.tests.e2e")
-os.environ["WANDB_DISABLED"] = "true"
-
-
-class TestHQQ(unittest.TestCase):
-    """
-    Test cases for training of HQQ-quantized llama models"""
-
-    @with_temp_dir
-    def test_hqq_lora(self, temp_dir):
-        # pylint: disable=duplicate-code
-        cfg = DictDefault(
-            {
-                "base_model": "HuggingFaceTB/SmolLM2-135M",
-                "sequence_len": 1024,
-                "sample_packing": True,
-                "flash_attention": True,
-                "use_hqq": True,
-                "hqq_config": [
-                    {
-                        "nbits": 8,
-                        "group_size": 64,
-                    }
-                ],
-                "adapter": "lora",
-                "lora_r": 16,
-                "lora_alpha": 32,
-                "lora_dropout": 0.05,
-                "lora_target_linear": True,
-                "val_set_size": 0.0,
-                "special_tokens": {
-                    "pad_token": "<|endoftext|>",
-                },
-                "datasets": [
-                    {
-                        "path": "vicgalle/alpaca-gpt4",
-                        "type": "alpaca",
-                    },
-                ],
-                "num_epochs": 1,
-                "micro_batch_size": 2,
-                "gradient_accumulation_steps": 2,
-                "output_dir": temp_dir,
-                "learning_rate": 0.00001,
-                "optimizer": "adamw_torch_fused",
-                "lr_scheduler": "cosine",
-                "max_steps": 5,
-                "use_tensorboard": True,
-            }
-        )
-        if is_torch_bf16_gpu_available():
-            cfg.bf16 = True
-        else:
-            cfg.fp16 = True
-
-        cfg = validate_config(cfg)
-        normalize_config(cfg)
-        cli_args = TrainerCliArgs()
-        dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
-
-        train(cfg=cfg, dataset_meta=dataset_meta)
-
-        check_tensorboard(
-            temp_dir + "/runs", "train/train_loss", 2.0, "Train Loss is too high"
-        )
-
-    @with_temp_dir
-    def test_hqq_qlora(self, temp_dir):
-        # pylint: disable=duplicate-code
-        cfg = DictDefault(
-            {
-                "base_model": "HuggingFaceTB/SmolLM2-135M",
-                "sequence_len": 1024,
-                "sample_packing": True,
-                "flash_attention": True,
-                "use_hqq": True,
-                "hqq_config": [
-                    {
-                        "nbits": 4,
-                        "group_size": 64,
-                    }
-                ],
-                "adapter": "qlora",
-                "lora_r": 16,
-                "lora_alpha": 32,
-                "lora_dropout": 0.05,
-                "lora_target_linear": True,
-                "val_set_size": 0.0,
-                "special_tokens": {
-                    "pad_token": "<|endoftext|>",
-                },
-                "datasets": [
-                    {
-                        "path": "vicgalle/alpaca-gpt4",
-                        "type": "alpaca",
-                    },
-                ],
-                "num_epochs": 1,
-                "micro_batch_size": 2,
-                "gradient_accumulation_steps": 2,
-                "output_dir": temp_dir,
-                "learning_rate": 0.00001,
-                "optimizer": "adamw_torch_fused",
-                "lr_scheduler": "cosine",
-                "max_steps": 5,
-                "use_tensorboard": True,
-            }
-        )
-        if is_torch_bf16_gpu_available():
-            cfg.bf16 = True
-        else:
-            cfg.fp16 = True
-
-        cfg = validate_config(cfg)
-        normalize_config(cfg)
-        cli_args = TrainerCliArgs()
-        dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
-
-        train(cfg=cfg, dataset_meta=dataset_meta)
-
-        check_tensorboard(
-            temp_dir + "/runs", "train/train_loss", 2.0, "Train Loss is too high"
-        )

tests/e2e/test_reward_model_smollm2.py

@@ -73,6 +73,6 @@ class TestRewardModelLoraSmolLM2(unittest.TestCase):
 
         train(cfg=cfg, dataset_meta=dataset_meta)
         check_tensorboard(
-            temp_dir + "/runs", "train/train_loss", 2.5, "Train Loss is too high"
+            temp_dir + "/runs", "train/train_loss", 2.5, "Train loss (%s) is too high"
         )
         check_model_output_exists(temp_dir, cfg)

tests/patched/test_validation.py

@@ -74,11 +74,7 @@ class TestValidation(BaseValidation):
                 "deepspeed": "deepspeed_configs/zero3_bf16.json",
                 "gradient_checkpointing": True,
                 "gradient_checkpointing_kwargs": {"use_reentrant": False},
-                "quantization": {
+                "load_in_4bit": True,
-                    "backend": "bnb",
-                    "bits": 4,
-                },
-                # "load_in_4bit": True
                 "adapter": "qlora",
             }
             | minimal_cfg
@@ -97,10 +93,7 @@ class TestValidation(BaseValidation):
                 "deepspeed": "",
                 "gradient_checkpointing": True,
                 "gradient_checkpointing_kwargs": {"use_reentrant": False},
-                "quantization": {
+                "load_in_4bit": True,
-                    "backend": "bnb",
-                    "bits": 4,
-                },
                 "adapter": "qlora",
             }
             | minimal_cfg
@@ -114,10 +107,7 @@ class TestValidation(BaseValidation):
                 "deepspeed": None,
                 "gradient_checkpointing": True,
                 "gradient_checkpointing_kwargs": {"use_reentrant": False},
-                "quantization": {
+                "load_in_4bit": True,
-                    "backend": "bnb",
-                    "bits": 4,
-                },
                 "adapter": "qlora",
             }
             | minimal_cfg
@@ -316,10 +306,7 @@ class TestValidation(BaseValidation):
         cfg = (
             DictDefault(  # pylint: disable=unsupported-binary-operation
                 {
-                    "quantization": {
+                    "load_in_8bit": True,
-                        "backend": "bnb",
-                        "bits": 8,
-                    },
                 }
             )
             | base_cfg
@@ -331,9 +318,7 @@ class TestValidation(BaseValidation):
         cfg = (
             DictDefault(  # pylint: disable=unsupported-binary-operation
                 {
-                    "quantization": {
+                    "gptq": True,
-                        "backend": "gptq",
-                    },
                 }
             )
             | base_cfg
@@ -345,24 +330,19 @@ class TestValidation(BaseValidation):
         cfg = (
             DictDefault(  # pylint: disable=unsupported-binary-operation
                 {
-                    "quantization": {
+                    "load_in_4bit": False,
-                        "bits": None,
-                    },
                 }
             )
             | base_cfg
         )
 
-        with pytest.raises(ValueError, match=r".*bits <= 4*"):
+        with pytest.raises(ValueError, match=r".*4bit.*"):
             validate_config(cfg)
 
         cfg = (
             DictDefault(  # pylint: disable=unsupported-binary-operation
                 {
-                    "quantization": {
+                    "load_in_4bit": True,
-                        "backend": "bnb",
-                        "bits": 4,
-                    },
                 }
             )
             | base_cfg
@@ -384,10 +364,7 @@ class TestValidation(BaseValidation):
         cfg = (
             DictDefault(  # pylint: disable=unsupported-binary-operation
                 {
-                    "quantization": {
+                    "load_in_8bit": True,
-                        "backend": "bnb",
-                        "bits": 8,
-                    },
                 }
             )
             | base_cfg
@@ -399,10 +376,7 @@ class TestValidation(BaseValidation):
         cfg = (
             DictDefault(  # pylint: disable=unsupported-binary-operation
                 {
-                    "quantization": {
+                    "gptq": True,
-                        "backend": "gptq",
-                        "bits": 4,
-                    },
                 }
             )
             | base_cfg
@@ -414,9 +388,7 @@ class TestValidation(BaseValidation):
         cfg = (
             DictDefault(  # pylint: disable=unsupported-binary-operation
                 {
-                    "quantization": {
+                    "load_in_4bit": True,
-                        "bits": 4,
-                    },
                 }
             )
             | base_cfg
@@ -1004,9 +976,7 @@ class TestValidation(BaseValidation):
         cfg = (
             DictDefault(
                 {
-                    "quantization": {
+                    "load_in_4bit": True,
-                        "bits": None,
-                    },
                 }
             )
             | minimal_cfg
@@ -1014,16 +984,29 @@ class TestValidation(BaseValidation):
 
         with pytest.raises(
             ValueError,
-            match=r"Quantization is not supported without setting an adapter.*",
+            match=r".*load_in_8bit and load_in_4bit are not supported without setting an adapter.*",
         ):
             validate_config(cfg)
 
         cfg = (
             DictDefault(
                 {
-                    "quantization": {
+                    "load_in_8bit": True,
-                        "bits": 4,
+                }
-                    },
+            )
+            | minimal_cfg
+        )
+
+        with pytest.raises(
+            ValueError,
+            match=r".*load_in_8bit and load_in_4bit are not supported without setting an adapter.*",
+        ):
+            validate_config(cfg)
+
+        cfg = (
+            DictDefault(
+                {
+                    "load_in_4bit": True,
                     "adapter": "qlora",
                 }
             )
@@ -1035,9 +1018,7 @@ class TestValidation(BaseValidation):
         cfg = (
             DictDefault(
                 {
-                    "quantization": {
+                    "load_in_8bit": True,
-                        "bits": 8,
-                    },
                     "adapter": "lora",
                 }
             )

tests/test_perplexity.py

@@ -8,7 +8,7 @@ from transformers.models.auto.tokenization_auto import AutoTokenizer
 
 from axolotl.utils.callbacks.perplexity import Perplexity
 
-MODEL_NAME = "HuggingFaceTB/SmolLM2-135M"
+MODEL_NAME = "axolotl-ai-internal/llama-7m"
 
 
 @fixture()
@@ -36,7 +36,7 @@ One day, a little fish named Fin was swimming near the shore. He saw a big crab
 """
     result = metric.compute(model, [sample_text])
     ppl = result["score"]
-    assert round(ppl, 2) == 7.41
+    assert round(ppl, 2) == 75.14
 
 
 def test_perplexity_short(model, metric):
@@ -44,4 +44,4 @@ def test_perplexity_short(model, metric):
     sample_text = "Once upon a time, there was a little car named Beep. Beep loved to go fast and play in the sun."
     result = metric.compute(model, [sample_text])
     ppl = result["score"]
-    assert round(ppl, 2) == 10.33
+    assert round(ppl, 2) == 70.54

tests/utils/test_models.py

@@ -21,10 +21,8 @@ class TestModelsUtils:
                 "base_model": "JackFram/llama-68m",
                 "model_type": "LlamaForCausalLM",
                 "tokenizer_type": "LlamaTokenizer",
-                "quantization": {
+                "load_in_8bit": True,
-                    "backend": "bnb",
+                "load_in_4bit": False,
-                    "bits": 8,
-                },
                 "adapter": "lora",
                 "flash_attention": False,
                 "sample_packing": True,