resume from optimizer checkpoint only

* fix: allow trainer builder to use custom jinja chat template

* chore: use get_chat_template_from_config

Co-authored-by: Chirag Jain <jain.chirag925@gmail.com>

* fix: swap imports

---------

Co-authored-by: Chirag Jain <jain.chirag925@gmail.com>

.gitignore

@@ -1,6 +1,7 @@
 **/axolotl.egg-info
 configs
 last_run_prepared/
+outputs
 .vscode
 _site/
 

cicd/cicd.sh

@@ -5,6 +5,6 @@ python -c "import torch; assert '$PYTORCH_VERSION' in torch.__version__"
 
 pytest -v --durations=10 -n8 --ignore=tests/e2e/ --ignore=tests/patched/ /workspace/axolotl/tests/
 # pytest -v --durations=10 -n8 --dist loadfile /workspace/axolotl/tests/patched/
-pytest -v --durations=10 -n1 --dist loadfile /workspace/axolotl/tests/e2e/patched/
+pytest -v --durations=10 /workspace/axolotl/tests/e2e/patched/
-pytest -v --durations=10 -n1 --dist loadfile /workspace/axolotl/tests/e2e/integrations/
+pytest -v --durations=10 /workspace/axolotl/tests/e2e/integrations/
 pytest -v --durations=10 --ignore=tests/e2e/patched/ --ignore=tests/e2e/multigpu/ --ignore=tests/e2e/integrations/ /workspace/axolotl/tests/e2e/

deepspeed_configs/zero1_torch_compile.json

@@ -0,0 +1,27 @@
+{
+  "zero_optimization": {
+    "stage": 1,
+    "overlap_comm": true
+  },
+  "bf16": {
+    "enabled": "auto"
+  },
+  "fp16": {
+    "enabled": "auto",
+    "auto_cast": false,
+    "loss_scale": 0,
+    "initial_scale_power": 32,
+    "loss_scale_window": 1000,
+    "hysteresis": 2,
+    "min_loss_scale": 1
+  },
+  "compile": {
+    "disable": false,
+    "backend": "inductor"
+  },
+  "gradient_accumulation_steps": "auto",
+  "gradient_clipping": "auto",
+  "train_batch_size": "auto",
+  "train_micro_batch_size_per_gpu": "auto",
+  "wall_clock_breakdown": false
+}

docs/config.qmd

@@ -127,34 +127,40 @@ datasets:
     # - tokenizer_default_fallback_*: where * is the name of the chat template to fallback to if the tokenizer does not have a chat template else default to tokenizer. E.g. tokenizer_default_fallback_chatml.
     # - jinja: Uses a custom jinja template for the chat template. The custom jinja template should be provided in the chat_template_jinja field.
     chat_template: tokenizer_default
-    # Custom jinja template for chat template. This will be only used if `chat_template` is set to `jinja` or empty (in which case chat_template is automatically set to `jinja`).
+
+    # Custom jinja chat template. Used only if `chat_template: jinja` or empty.
     chat_template_jinja:
-    # The key in the data example that contains the messages. Default is "messages".
+
+    # Key containing the messages (default: "messages")
     field_messages: messages
-    # The key in the message turn that contains the role. Default is "role".
+    # Key for role in each message (default: "role")
     message_field_role: role
-    # The key in the message turn that contains the content. Default is "content".
+    # Key for content in each message (default:  "content")
     message_field_content: content
-    # Optional[Dict[str, List]]. Roles mapping for the messages.
+
+    # Optional[Dict[str, List]]. Roles mapping in the messages. The default is:
     roles:
       user: ["human", "user"]
-      assistant: ["gpt", "assistant", "ai"]
+      assistant: ["gpt", "assistant"]
       system: ["system"]
+      tool: ["tool"]
 
-    ## NOTE: Leaving the below empty will default to using the simple legacy tokenization strategy where only last message is trained on.
+    # IMPORTANT: The following fields determine which parts of the conversation to train on.
+    # Priority order: message_field_training > message_field_training_detail > train_on_inputs or role in roles_to_train
+    # See examples at `docs/dataset-formats/conversation.qmd`
+    # Note: If the below 4 fields are empty, defaults to training only on the last message.
 
     # Optional[List[str]]. Roles to train on. The tokens from these roles will be considered for the loss.
-    roles_to_train: ["gpt", "assistant"]
+    roles_to_train: ["assistant"]  # default
     # Optional[str]. Which EOS tokens to train on in the conversation. Possible values are:
     # - all: train on all EOS tokens
-    # - turn: train on the EOS token at the end of each trainable turn
+    # - turn (default): train on the EOS token at the end of each trainable turn
     # - last: train on the last EOS token in the conversation
     train_on_eos: last
     # The key in the message turn that indicates via boolean whether tokens of a turn should be considered for training. Useful to selectively train on certain turns besides the `roles_to_train`.
     message_field_training: training
     # The key in the message turn that contains the training details. Useful to selectively train on certain tokens in a turn.
     # The value of the key is a List[Dict] containing `begin_offset` (start character index in content), `end_offset` (end character index in content), and `train` (boolean whether to train).
-    # See example at `docs/dataset-formats/conversation.qmd`
     message_field_training_detail: train_detail
 
 
@@ -239,6 +245,9 @@ sample_packing_group_size: 100000
 # The number of samples which can be packed into one sequence. Increase if using a large sequence_len with many short samples.
 sample_packing_bin_size: 200
 
+# Use batch flattening for speedups when not using sample_packing
+batch_flattening:
+
 # Passed through to transformers when loading the model when launched without accelerate
 # Use `sequential` when training w/ model parallelism to limit memory
 device_map:
@@ -331,7 +340,8 @@ comet_experiment_config: # Dictionary for additional configuration settings, see
 output_dir: ./completed-model
 
 # Whether to use torch.compile and which backend to use
-torch_compile:  # bool
+# setting to `auto` will enable torch compile when torch>=2.5.1
+torch_compile:  # Optional[Union[Literal["auto"], bool]]
 torch_compile_backend:  # Optional[str]
 
 # Training hyperparameters
@@ -363,6 +373,10 @@ eval_table_size: # Approximate number of predictions sent to wandb depending on
 eval_max_new_tokens: # Total number of tokens generated for predictions sent to wandb. Default is 128
 eval_causal_lm_metrics: # HF evaluate metrics used during evaluation. Default is ["sacrebleu", "comet", "ter", "chrf", "perplexity"]
 
+profiler_steps: # enable the pytorch profiler to capture the first N steps of training to the output_dir.
+                # see https://pytorch.org/blog/understanding-gpu-memory-1/ for more information
+                # snapshots can be visualized @ https://pytorch.org/memory_viz
+
 loss_watchdog_threshold: # High loss value, indicating the learning has broken down (a good estimate is ~2 times the loss at the start of training)
 loss_watchdog_patience: # Number of high-loss steps in a row before the trainer aborts (default: 3)
 

docs/dataset-formats/conversation.qmd

@@ -68,6 +68,8 @@ We recommend checking the below examples for other usecases.
 datasets:
   - path: ...
     type: chat_template
+    roles_to_train:
+    train_on_eos:
 ```
 
 2. Using the `gemma` chat template to override the tokenizer_config.json's chat template on OpenAI messages format, training on all assistant messages.
@@ -77,7 +79,7 @@ chat_template: gemma # this overwrites the tokenizer's chat_template
 datasets:
   - path: ...
     type: chat_template
-    roles_to_train: ["assistant"]
+    roles_to_train: ["assistant"]  # default value
 ```
 
 3. Using the tokenizer_config.json's chat template or `chatml` as fallback if the former's chat template does not exist, on OpenAI messages format, training on all assistant messages.
@@ -87,7 +89,6 @@ chat_template: tokenizer_default_fallback_chatml # this overwrites the tokenizer
 datasets:
   - path: ...
     type: chat_template
-    roles_to_train: ["assistant"]
 ```
 
 4. Using a custom jinja template on OpenAI messages format, training on all assistant messages.
@@ -99,7 +100,6 @@ chat_template_jinja: "{{ bos_token }}{% for message in messages %}{% if (message
 datasets:
   - path: ...
     type: chat_template
-    roles_to_train: ["assistant"]
 ```
 
 5. (Advanced) Using fine-grained control over tokens and turns to train in a conversation

examples/cerebras/btlm-ft.yml

@@ -1,6 +1,10 @@
 base_model: cerebras/btlm-3b-8k-base
+# optionally might have model_type or tokenizer_type
 model_type: AutoModelForCausalLM
 tokenizer_type: GPT2Tokenizer
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
+
 trust_remote_code: true
 tokenizer_use_fast: true
 tokenizer_legacy: true

examples/cerebras/qlora.yml

@@ -1,4 +1,7 @@
 base_model: cerebras/Cerebras-GPT-1.3B
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
+
 load_in_8bit: false
 load_in_4bit: true
 strict: false

examples/code-llama/13b/lora.yml

@@ -1,6 +1,9 @@
 base_model: codellama/CodeLlama-13b-hf
+# optionally might have model_type or tokenizer_type
 model_type: LlamaForCausalLM
 tokenizer_type: CodeLlamaTokenizer
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
 
 load_in_8bit: true
 load_in_4bit: false

examples/code-llama/13b/qlora.yml

@@ -1,6 +1,9 @@
 base_model: codellama/CodeLlama-13b-hf
+# optionally might have model_type or tokenizer_type
 model_type: LlamaForCausalLM
 tokenizer_type: CodeLlamaTokenizer
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
 
 load_in_8bit: false
 load_in_4bit: true

examples/code-llama/34b/lora.yml

@@ -1,6 +1,9 @@
 base_model: codellama/CodeLlama-34b-hf
+# optionally might have model_type or tokenizer_type
 model_type: LlamaForCausalLM
 tokenizer_type: CodeLlamaTokenizer
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
 
 load_in_8bit: true
 load_in_4bit: false

examples/code-llama/34b/qlora.yml

@@ -1,6 +1,9 @@
 base_model: codellama/CodeLlama-34b-hf
+# optionally might have model_type or tokenizer_type
 model_type: LlamaForCausalLM
 tokenizer_type: CodeLlamaTokenizer
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
 
 load_in_8bit: false
 load_in_4bit: true

examples/code-llama/7b/lora.yml

@@ -1,6 +1,9 @@
 base_model: codellama/CodeLlama-7b-hf
+# optionally might have model_type or tokenizer_type
 model_type: LlamaForCausalLM
 tokenizer_type: CodeLlamaTokenizer
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
 
 load_in_8bit: true
 load_in_4bit: false

examples/code-llama/7b/qlora.yml

@@ -1,6 +1,9 @@
 base_model: codellama/CodeLlama-7b-hf
+# optionally might have model_type or tokenizer_type
 model_type: LlamaForCausalLM
 tokenizer_type: CodeLlamaTokenizer
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
 
 load_in_8bit: false
 load_in_4bit: true

examples/dbrx/16bit-lora.yaml

@@ -1,4 +1,7 @@
 base_model: LnL-AI/dbrx-base-converted-v2
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
+
 trust_remote_code: true
 
 load_in_8bit: false

examples/dbrx/8bit-lora.yaml

@@ -1,4 +1,7 @@
 base_model: LnL-AI/dbrx-base-converted-v2
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
+
 trust_remote_code: true
 
 load_in_8bit: true

examples/dbrx/fft-ds-zero3.yaml

@@ -1,4 +1,7 @@
 base_model: LnL-AI/dbrx-base-converted-v2
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
+
 trust_remote_code: true
 
 load_in_8bit: false

examples/deepseek-v2/fft-fsdp-16b.yaml

@@ -1,4 +1,6 @@
 base_model: deepseek-ai/DeepSeek-V2-Lite
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
 trust_remote_code: true
 
 load_in_8bit: false

examples/deepseek-v2/qlora-fsdp-2_5.yaml

@@ -1,4 +1,7 @@
 base_model: axolotl-quants/DeepSeek-V2.5-bnb-nf4-bf16
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
+
 trust_remote_code: true
 
 load_in_8bit: false

examples/falcon/config-7b-lora.yml

@@ -1,7 +1,12 @@
 base_model: tiiuae/falcon-7b
-trust_remote_code: true
+# optionally might have model_type or tokenizer_type
 model_type: AutoModelForCausalLM
 tokenizer_type: AutoTokenizer
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
+
+# required by falcon custom model code: https://huggingface.co/tiiuae/falcon-7b/tree/main
+trust_remote_code: true
 
 load_in_8bit: true
 load_in_4bit: false

examples/falcon/config-7b-qlora.yml

@@ -1,10 +1,15 @@
 # 1b: tiiuae/falcon-rw-1b
 # 40b: tiiuae/falcon-40b
 base_model: tiiuae/falcon-7b
-# required by falcon custom model code: https://huggingface.co/tiiuae/falcon-7b/tree/main
+# optionally might have model_type or tokenizer_type
-trust_remote_code: true
 model_type: AutoModelForCausalLM
 tokenizer_type: AutoTokenizer
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
+
+# required by falcon custom model code: https://huggingface.co/tiiuae/falcon-7b/tree/main
+trust_remote_code: true
+
 
 load_in_8bit: false
 # enable 4bit for QLoRA

examples/falcon/config-7b.yml

@@ -1,7 +1,12 @@
 base_model: tiiuae/falcon-7b
-trust_remote_code: true
+# optionally might have model_type or tokenizer_type
 model_type: AutoModelForCausalLM
 tokenizer_type: AutoTokenizer
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
+
+# required by falcon custom model code: https://huggingface.co/tiiuae/falcon-7b/tree/main
+trust_remote_code: true
 
 load_in_8bit: false
 load_in_4bit: false

examples/gemma/qlora.yml

@@ -1,7 +1,10 @@
 # use google/gemma-7b if you have access
 base_model: mhenrichsen/gemma-7b
+# optionally might have model_type or tokenizer_type
 model_type: AutoModelForCausalLM
 tokenizer_type: AutoTokenizer
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
 
 load_in_8bit: false
 load_in_4bit: true

examples/gemma2/qlora.yml

@@ -1,6 +1,9 @@
 base_model: google/gemma-2-9b
+# optionally might have model_type or tokenizer_type
 model_type: AutoModelForCausalLM
 tokenizer_type: AutoTokenizer
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
 
 load_in_8bit: false
 load_in_4bit: true

examples/gemma2/reward-model.yaml

@@ -1,6 +1,9 @@
 base_model: google/gemma-2-2b
+# optionally might have model_type or tokenizer_type
 model_type: AutoModelForSequenceClassification
 tokenizer_type: AutoTokenizer
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
 
 load_in_8bit: false
 load_in_4bit: false

examples/gptj/qlora.yml

@@ -1,4 +1,7 @@
 base_model: EleutherAI/gpt-j-6b
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
+
 load_in_8bit: false
 load_in_4bit: true
 strict: false

examples/jamba/qlora.yaml

@@ -1,4 +1,7 @@
 base_model: ai21labs/Jamba-v0.1
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
+
 trust_remote_code: true
 
 load_in_8bit: false

examples/jamba/qlora_deepspeed.yaml

@@ -1,4 +1,6 @@
 base_model: ai21labs/Jamba-v0.1
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
 trust_remote_code: true
 
 load_in_8bit: false

examples/jamba/qlora_fsdp_large.yaml

@@ -1,5 +1,8 @@
 base_model: ai21labs/AI21-Jamba-1.5-Large
+# optionally might have model_type or tokenizer_type
 tokenizer_type: AutoTokenizer
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
 
 load_in_4bit: true
 strict: false

examples/jeopardy-bot/config.yml

@@ -1,6 +1,10 @@
 base_model: huggyllama/llama-7b
+# optionally might have model_type or tokenizer_type
 model_type: LlamaForCausalLM
 tokenizer_type: LlamaTokenizer
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
+
 load_in_8bit: false
 datasets:
   - path: openaccess-ai-collective/jeopardy

examples/llama-2/fft_optimized.yml

@@ -1,6 +1,9 @@
 base_model: NousResearch/Llama-2-7b-hf
+# optionally might have model_type or tokenizer_type
 model_type: LlamaForCausalLM
 tokenizer_type: LlamaTokenizer
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
 
 load_in_8bit: false
 load_in_4bit: false

examples/llama-2/gptq-lora.yml

@@ -1,8 +1,13 @@
 base_model: TheBloke/Llama-2-7B-GPTQ
-gptq: true
+# optionally might have model_type or tokenizer_type
-gptq_disable_exllama: true
 model_type: AutoModelForCausalLM
 tokenizer_type: LlamaTokenizer
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
+
+gptq: true
+gptq_disable_exllama: true
+
 tokenizer_use_fast: true
 tokenizer_legacy: true
 load_in_8bit: false

examples/llama-2/lisa.yml

@@ -1,6 +1,9 @@
 base_model: NousResearch/Llama-2-7b-hf
+# optionally might have model_type or tokenizer_type
 model_type: LlamaForCausalLM
 tokenizer_type: LlamaTokenizer
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
 
 load_in_8bit: false
 load_in_4bit: false

examples/llama-2/loftq.yml

@@ -1,6 +1,9 @@
 base_model: NousResearch/Llama-2-7b-hf
+# optionally might have model_type or tokenizer_type
 model_type: LlamaForCausalLM
 tokenizer_type: LlamaTokenizer
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
 
 load_in_8bit: false
 load_in_4bit: false

examples/llama-2/lora.yml

@@ -1,6 +1,9 @@
 base_model: NousResearch/Llama-2-7b-hf
+# optionally might have model_type or tokenizer_type
 model_type: LlamaForCausalLM
 tokenizer_type: LlamaTokenizer
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
 
 load_in_8bit: true
 load_in_4bit: false

examples/llama-2/qlora-fsdp.yml

@@ -1,6 +1,9 @@
 base_model: NousResearch/Llama-2-7b-hf
+# optionally might have model_type or tokenizer_type
 model_type: LlamaForCausalLM
 tokenizer_type: LlamaTokenizer
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
 
 load_in_8bit: false
 load_in_4bit: true

examples/llama-2/qlora.yml

@@ -1,6 +1,9 @@
 base_model: NousResearch/Llama-2-7b-hf
+# optionally might have model_type or tokenizer_type
 model_type: LlamaForCausalLM
 tokenizer_type: LlamaTokenizer
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
 
 load_in_8bit: false
 load_in_4bit: true

examples/llama-3-vision/lora-11b.yaml

@@ -1,5 +1,9 @@
 base_model: alpindale/Llama-3.2-11B-Vision-Instruct
+# optionally might have model_type or tokenizer_type or processor_type
 processor_type: AutoProcessor
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
+
 strict: false
 
 # these 3 lines are needed for now to handle vision chat templates w images

examples/llama-3/fft-8b-liger-fsdp.yaml

@@ -1,4 +1,6 @@
 base_model: NousResearch/Meta-Llama-3.1-8B
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
 
 plugins:
   - axolotl.integrations.liger.LigerPlugin

examples/llama-3/fft-8b.yaml

@@ -1,4 +1,6 @@
 base_model: NousResearch/Meta-Llama-3.1-8B
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
 
 load_in_8bit: false
 load_in_4bit: false

examples/llama-3/instruct-dpo-lora-8b.yml

@@ -1,6 +1,9 @@
 base_model: meta-llama/Meta-Llama-3-8B-Instruct
+# optionally might have model_type or tokenizer_type
 model_type: LlamaForCausalLM
 tokenizer_type: AutoTokenizer
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
 
 load_in_8bit: true
 load_in_4bit: false

examples/llama-3/instruct-lora-8b.yml

@@ -1,6 +1,9 @@
 base_model: NousResearch/Meta-Llama-3-8B-Instruct
+# optionally might have model_type or tokenizer_type
 model_type: LlamaForCausalLM
 tokenizer_type: AutoTokenizer
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
 
 load_in_8bit: true
 load_in_4bit: false

examples/llama-3/lora-1b-deduplicate-dpo.yml

@@ -1,6 +1,9 @@
 base_model: meta-llama/Llama-3.2-1B
+# optionally might have model_type or tokenizer_type
 model_type: LlamaForCausalLM
 tokenizer_type: AutoTokenizer
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
 
 load_in_8bit: true
 load_in_4bit: false

examples/llama-3/lora-1b-deduplicate-sft.yml

@@ -1,6 +1,9 @@
 base_model: meta-llama/Llama-3.2-1B
+# optionally might have model_type or tokenizer_type
 model_type: LlamaForCausalLM
 tokenizer_type: AutoTokenizer
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
 
 load_in_8bit: true
 load_in_4bit: false

examples/llama-3/lora-1b.yml

@@ -1,4 +1,6 @@
 base_model: NousResearch/Llama-3.2-1B
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
 
 load_in_8bit: false
 load_in_4bit: false

examples/llama-3/lora-8b.yml

@@ -1,6 +1,9 @@
 base_model: NousResearch/Meta-Llama-3-8B
+# optionally might have model_type or tokenizer_type
 model_type: LlamaForCausalLM
 tokenizer_type: AutoTokenizer
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
 
 load_in_8bit: true
 load_in_4bit: false

examples/llama-3/qlora-1b-kto.yaml

@@ -1,4 +1,6 @@
 base_model: meta-llama/Llama-3.2-1B
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
 
 load_in_8bit: false
 load_in_4bit: true

examples/llama-3/qlora-1b.yml

@@ -1,4 +1,6 @@
 base_model: NousResearch/Llama-3.2-1B
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
 
 load_in_8bit: false
 load_in_4bit: true

examples/llama-3/qlora-fsdp-405b.yaml

@@ -1,5 +1,8 @@
 base_model: hugging-quants/Meta-Llama-3.1-405B-BNB-NF4-BF16
+# optionally might have model_type or tokenizer_type
 tokenizer_type: AutoTokenizer
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
 
 load_in_4bit: true
 strict: false

examples/llama-3/qlora-fsdp-70b.yaml

@@ -1,6 +1,9 @@
 base_model: casperhansen/llama-3-70b-fp16
+# optionally might have model_type or tokenizer_type
 model_type: LlamaForCausalLM
 tokenizer_type: AutoTokenizer  # PreTrainedTokenizerFast
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
 
 load_in_8bit: false
 load_in_4bit: true

examples/llama-3/qlora.yml

@@ -1,6 +1,9 @@
 base_model: NousResearch/Meta-Llama-3-8B
+# optionally might have model_type or tokenizer_type
 model_type: AutoModelForCausalLM
 tokenizer_type: AutoTokenizer
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
 
 load_in_8bit: false
 load_in_4bit: true

examples/mamba/config.yml

@@ -1,7 +1,10 @@
 base_model: state-spaces/mamba-2.8b
+# optionally might have model_type or tokenizer_type or tokenizer_config
 model_type: MambaLMHeadModel
 tokenizer_type: AutoTokenizer
 tokenizer_config: EleutherAI/gpt-neox-20b
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
 
 load_in_8bit: false
 load_in_4bit: false

examples/mistral/bigstral-ds-zero3.yaml

@@ -1,6 +1,10 @@
 base_model: mistral-community/Mixtral-8x22B-v0.1
+# optionally might have model_type or tokenizer_type
 model_type: AutoModelForCausalLM
 tokenizer_type: LlamaTokenizer
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
+
 trust_remote_code: true
 
 load_in_8bit: false

examples/mistral/config.yml

@@ -1,6 +1,9 @@
 base_model: mistralai/Mistral-7B-v0.1
+# optionally might have model_type or tokenizer_type
 model_type: MistralForCausalLM
 tokenizer_type: LlamaTokenizer
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
 
 load_in_8bit: false
 load_in_4bit: false

examples/mistral/lora-mps.yml

@@ -1,6 +1,9 @@
 base_model: mistralai/Mistral-7B-v0.1
+# optionally might have model_type or tokenizer_type
 model_type: MistralForCausalLM
 tokenizer_type: LlamaTokenizer
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
 
 load_in_8bit: false
 load_in_4bit: false

examples/mistral/lora.yml

@@ -1,6 +1,9 @@
 base_model: mistralai/Mistral-7B-v0.1
+# optionally might have model_type or tokenizer_type
 model_type: MistralForCausalLM
 tokenizer_type: LlamaTokenizer
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
 
 load_in_8bit: true
 load_in_4bit: false

examples/mistral/mistral-dpo-qlora.yml

@@ -4,8 +4,11 @@
 #face problems with the special tokens.
 
 base_model: mistralai/Mistral-7B-Instruct-v0.2
+# optionally might have model_type or tokenizer_type
 model_type: MistralForCausalLM
 tokenizer_type: LlamaTokenizer
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
 
 load_in_8bit: false
 load_in_4bit: true

examples/mistral/mistral-qlora-fsdp.yml

@@ -1,6 +1,10 @@
 base_model: mistralai/Mixtral-8x7B-v0.1
+# optionally might have model_type or tokenizer_type
 model_type: AutoModelForCausalLM
 tokenizer_type: LlamaTokenizer
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
+
 trust_remote_code: true
 
 load_in_8bit: false

examples/mistral/mistral-qlora-orpo.yml

@@ -1,6 +1,9 @@
 base_model: mistralai/Mistral-7B-v0.1
+# optionally might have model_type or tokenizer_type
 model_type: MistralForCausalLM
 tokenizer_type: LlamaTokenizer
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
 
 load_in_8bit: false
 load_in_4bit: true

examples/mistral/mixtral-8x22b-qlora-fsdp.yml

@@ -1,6 +1,9 @@
 base_model: mistral-community/Mixtral-8x22B-v0.1
+# optionally might have model_type or tokenizer_type
 model_type: AutoModelForCausalLM
 tokenizer_type: LlamaTokenizer
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
 
 load_in_8bit: false
 load_in_4bit: true

examples/mistral/mixtral-qlora-fsdp.yml

@@ -1,6 +1,10 @@
 base_model: mistralai/Mixtral-8x7B-v0.1
+# optionally might have model_type or tokenizer_type
 model_type: AutoModelForCausalLM
 tokenizer_type: LlamaTokenizer
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
+
 trust_remote_code: true
 
 load_in_8bit: false

examples/mistral/mixtral.yml

@@ -1,6 +1,10 @@
 base_model: mistralai/Mixtral-8x7B-v0.1
+# optionally might have model_type or tokenizer_type
 model_type: AutoModelForCausalLM
 tokenizer_type: LlamaTokenizer
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
+
 trust_remote_code: true
 
 load_in_8bit: false

examples/mistral/mixtral_22.yml

@@ -1,6 +1,10 @@
 base_model: mistral-community/Mixtral-8x22B-v0.1
+# optionally might have model_type or tokenizer_type
 model_type: AutoModelForCausalLM
 tokenizer_type: LlamaTokenizer
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
+
 trust_remote_code: true
 
 load_in_8bit: false

examples/mistral/qlora.yml

@@ -1,6 +1,9 @@
 base_model: mistralai/Mistral-7B-v0.1
+# optionally might have model_type or tokenizer_type
 model_type: MistralForCausalLM
 tokenizer_type: LlamaTokenizer
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
 
 load_in_8bit: false
 load_in_4bit: true

examples/mpt-7b/config.yml

@@ -1,5 +1,9 @@
 base_model: mosaicml/mpt-7b
+# optionally might have model_type or tokenizer_type
 tokenizer_type: AutoTokenizer
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
+
 trust_remote_code: true  # required for mpt as their model class is not merged into transformers yet
 load_in_8bit: false
 datasets:

examples/openllama-3b/config.yml

@@ -1,6 +1,10 @@
 base_model: openlm-research/open_llama_3b_v2
+# optionally might have model_type or tokenizer_type
 model_type: LlamaForCausalLM
 tokenizer_type: LlamaTokenizer
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
+
 load_in_8bit: false
 load_in_4bit: false
 strict: false

examples/openllama-3b/lora.yml

@@ -1,6 +1,10 @@
 base_model: openlm-research/open_llama_3b_v2
+# optionally might have model_type or tokenizer_type
 model_type: LlamaForCausalLM
 tokenizer_type: LlamaTokenizer
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
+
 load_in_8bit: true
 load_in_4bit: false
 strict: false

examples/openllama-3b/qlora.yml

@@ -1,6 +1,10 @@
 base_model: openlm-research/open_llama_3b_v2
+# optionally might have model_type or tokenizer_type
 model_type: LlamaForCausalLM
 tokenizer_type: LlamaTokenizer
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
+
 load_in_8bit: false
 load_in_4bit: true
 strict: false

examples/phi/lora-3.5.yaml

@@ -1,6 +1,9 @@
 base_model: microsoft/Phi-3.5-mini-instruct
+# optionally might have model_type or tokenizer_type
 model_type: AutoModelForCausalLM
 tokenizer_type: AutoTokenizer
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
 
 load_in_8bit: true
 load_in_4bit: false

examples/phi/phi-ft.yml

@@ -1,6 +1,9 @@
 base_model: microsoft/phi-1_5
+# optionally might have model_type or tokenizer_type
 model_type: AutoModelForCausalLM
 tokenizer_type: AutoTokenizer
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
 
 load_in_8bit: false
 load_in_4bit: false

examples/phi/phi-qlora.yml

@@ -1,6 +1,9 @@
 base_model: microsoft/phi-1_5
+# optionally might have model_type or tokenizer_type
 model_type: AutoModelForCausalLM
 tokenizer_type: AutoTokenizer
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
 
 load_in_8bit: false
 load_in_4bit: true

examples/phi/phi2-ft.yml

@@ -1,6 +1,9 @@
 base_model: microsoft/phi-2
+# optionally might have model_type or tokenizer_type
 model_type: AutoModelForCausalLM
 tokenizer_type: AutoTokenizer
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
 
 load_in_8bit: false
 load_in_4bit: false

examples/phi/phi3-ft-fsdp.yml

@@ -1,6 +1,9 @@
 base_model: microsoft/Phi-3-mini-4k-instruct
+# optionally might have model_type or tokenizer_type
 model_type: AutoModelForCausalLM
 tokenizer_type: AutoTokenizer
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
 
 load_in_8bit: false
 load_in_4bit: false

examples/phi/phi3-ft.yml

@@ -1,7 +1,11 @@
 base_model: microsoft/Phi-3-mini-4k-instruct
+# optionally might have model_type or tokenizer_type
 trust_remote_code: true
 model_type: AutoModelForCausalLM
 tokenizer_type: AutoTokenizer
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
+
 chat_template: phi_3
 
 load_in_8bit: false

examples/pythia-12b/config.yml

@@ -1,7 +1,11 @@
 base_model: EleutherAI/pythia-12b-deduped
 base_model_ignore_patterns: pytorch*  # prefer safetensors
+# optionally might have model_type or tokenizer_type
 model_type: GPTNeoXForCausalLM
 tokenizer_type: AutoTokenizer
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
+
 load_in_8bit: false
 load_in_4bit: false
 gptq: false

examples/pythia/lora.yml

@@ -1,4 +1,7 @@
 base_model: EleutherAI/pythia-1.4b-deduped
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
+
 load_in_8bit: true
 datasets:
   - path: teknium/GPT4-LLM-Cleaned

examples/qwen/lora.yml

@@ -1,6 +1,9 @@
 base_model: Qwen/Qwen-7B
+# optionally might have model_type or tokenizer_type
 model_type: AutoModelForCausalLM
 tokenizer_type: AutoTokenizer
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
 
 trust_remote_code: true
 

examples/qwen/qlora.yml

@@ -1,6 +1,9 @@
 base_model: Qwen/Qwen-7B
+# optionally might have model_type or tokenizer_type
 model_type: AutoModelForCausalLM
 tokenizer_type: AutoTokenizer
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
 
 trust_remote_code: true
 

examples/qwen/qwen2-moe-lora.yaml

@@ -1,4 +1,7 @@
 base_model: Qwen/Qwen1.5-MoE-A2.7B
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
+
 trust_remote_code: true
 
 load_in_8bit: false

examples/qwen/qwen2-moe-qlora.yaml

@@ -1,4 +1,7 @@
 base_model: Qwen/Qwen1.5-MoE-A2.7B
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
+
 trust_remote_code: true
 
 load_in_8bit: false

examples/qwen2/dpo.yaml

@@ -1,4 +1,6 @@
 base_model: Qwen/Qwen2.5-0.5B
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
 
 strict: false
 

examples/qwen2/qlora-fsdp.yaml

@@ -1,4 +1,7 @@
 base_model: Qwen/Qwen2-7B
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
+
 trust_remote_code: true
 
 load_in_8bit: false

examples/redpajama/config-3b.yml

@@ -1,6 +1,10 @@
 base_model: togethercomputer/RedPajama-INCITE-Chat-3B-v1
+# optionally might have model_type or tokenizer_type
 model_type: GPTNeoXForCausalLM
 tokenizer_type: AutoTokenizer
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
+
 trust_remote_code:
 load_in_8bit: false
 datasets:

examples/replit-3b/config-lora.yml

@@ -1,4 +1,7 @@
 base_model: replit/replit-code-v1-3b
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
+
 trust_remote_code: true
 load_in_8bit: false
 datasets:

examples/stablelm-2/1.6b/fft.yml

@@ -1,6 +1,10 @@
 base_model: stabilityai/stablelm-2-1_6b
+# optionally might have model_type or tokenizer_type
 model_type: AutoModelForCausalLM
 tokenizer_type: AutoTokenizer
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
+
 trust_remote_code: true
 
 load_in_8bit: false

examples/stablelm-2/1.6b/lora.yml

@@ -1,6 +1,10 @@
 base_model: stabilityai/stablelm-2-1_6b
+# optionally might have model_type or tokenizer_type
 model_type: AutoModelForCausalLM
 tokenizer_type: AutoTokenizer
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
+
 trust_remote_code: true
 
 load_in_8bit: true

examples/starcoder2/qlora.yml

@@ -1,4 +1,6 @@
 base_model: bigcode/starcoder2-3b
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
 
 load_in_8bit: false
 load_in_4bit: true

examples/tiny-llama/lora-mps.yml

@@ -1,6 +1,9 @@
 base_model: TinyLlama/TinyLlama_v1.1
+# optionally might have model_type or tokenizer_type
 model_type: LlamaForCausalLM
 tokenizer_type: LlamaTokenizer
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
 
 load_in_8bit: true
 load_in_4bit: false

examples/tiny-llama/lora.yml

@@ -1,5 +1,8 @@
 base_model: TinyLlama/TinyLlama_v1.1
+# optionally might have model_type or tokenizer_type
 tokenizer_type: AutoTokenizer
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
 
 load_in_8bit: true
 load_in_4bit: false

examples/tiny-llama/pretrain.yml

@@ -1,7 +1,9 @@
 base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
-
+# optionally might have model_type or tokenizer_type
 model_type: LlamaForCausalLM
 tokenizer_type: LlamaTokenizer
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
 
 load_in_8bit: false
 load_in_4bit: false

examples/tiny-llama/qlora.yml

@@ -1,6 +1,9 @@
 base_model: TinyLlama/TinyLlama_v1.1
+# optionally might have model_type or tokenizer_type
 model_type: LlamaForCausalLM
 tokenizer_type: LlamaTokenizer
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
 
 load_in_8bit: false
 load_in_4bit: true

examples/xgen-7b/xgen-7b-8k-qlora.yml

@@ -1,9 +1,14 @@
 # An example finetuning Saleforce's XGen-7b model with 8k context using qlora
 # on Tim Dettmer's Guanaco dataset.
 base_model: Salesforce/xgen-7b-8k-base
-trust_remote_code: true
+# optionally might have model_type or tokenizer_type
 model_type: AutoModelForCausalLM
 tokenizer_type: AutoTokenizer
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
+
+trust_remote_code: true
+
 load_in_8bit: false
 # enable 4bit for QLoRA
 load_in_4bit: true

examples/yi-34B-chat/qlora.yml

@@ -1,6 +1,9 @@
 base_model: 01-ai/Yi-34B-Chat
+# optionally might have model_type or tokenizer_type
 model_type: LlamaForCausalLM
 tokenizer_type: LlamaTokenizer
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
 
 load_in_8bit: false
 load_in_4bit: true

requirements.txt

@@ -7,26 +7,31 @@ mamba-ssm==1.2.0.post1
 flash-attn==2.7.0.post2
 xformers>=0.0.23.post1
 autoawq==0.2.7.post3
-liger-kernel==0.4.2
+liger-kernel==0.5.2
 # END section
 
 packaging==23.2
+
 peft==0.14.0
-transformers>=4.46.3
+transformers==4.47.1
 tokenizers>=0.20.1
-accelerate==1.2.0
+accelerate==1.2.1
 datasets==3.1.0
 deepspeed==0.16.1
+trl==0.12.1
+
+optimum==1.16.2
+hf_transfer
+sentencepiece
+gradio==3.50.2
+
 pydantic==2.6.3
 addict
 fire
 PyYAML>=6.0
 requests
-sentencepiece
 wandb
 einops
-optimum==1.16.2
-hf_transfer
 colorama
 numba
 numpy>=1.24.4,<=2.0.1
@@ -36,7 +41,6 @@ scipy
 scikit-learn==1.4.2
 nvidia-ml-py==12.560.30
 art
-gradio==3.50.2
 tensorboard
 python-dotenv==1.0.1
 
@@ -45,7 +49,6 @@ s3fs>=2024.5.0
 gcsfs>=2024.5.0
 # adlfs
 
-trl==0.12.1
 zstandard==0.22.0
 fastcore
 
@@ -55,5 +58,7 @@ langdetect==1.0.9
 immutabledict==4.2.0
 antlr4-python3-runtime==4.13.2
 
-torchao==0.5.0
+torchao==0.7.0
 schedulefree==1.3.0
+
+axolotl-contribs-lgpl==0.0.2

scripts/unsloth_install.py

@@ -32,5 +32,5 @@ else:
     raise RuntimeError(f"Torch = {v} too new!")
 x = x.format(cuda.replace(".", ""), "-ampere" if is_ampere else "")
 print(
-    f'pip install unsloth-zoo==2024.11.7 && pip install --no-deps "unsloth[{x}]==2024.11.9"'
+    f'pip install unsloth-zoo==2024.12.1 && pip install --no-deps "unsloth[{x}]==2024.12.4"'
 )

src/axolotl/__init__.py

@@ -1,3 +1,7 @@
 """Axolotl - Train and fine-tune large language models"""
 
+import pkgutil
+
+__path__ = pkgutil.extend_path(__path__, __name__)  # Make this a namespace package
+
 __version__ = "0.6.0"

src/axolotl/cli/evaluate.py

@@ -0,0 +1,52 @@
+"""
+CLI to run training on a model
+"""
+import logging
+from pathlib import Path
+from typing import Union
+
+import fire
+from dotenv import load_dotenv
+from transformers.hf_argparser import HfArgumentParser
+
+from axolotl.cli import (
+    check_accelerate_default_config,
+    check_user_token,
+    load_cfg,
+    load_datasets,
+    load_rl_datasets,
+    print_axolotl_text_art,
+)
+from axolotl.common.cli import TrainerCliArgs
+from axolotl.evaluate import evaluate
+
+LOG = logging.getLogger("axolotl.cli.evaluate")
+
+
+def do_evaluate(cfg, cli_args) -> None:
+    # pylint: disable=duplicate-code
+    print_axolotl_text_art()
+    check_accelerate_default_config()
+    check_user_token()
+
+    if cfg.rl:  # and cfg.rl != "orpo":
+        dataset_meta = load_rl_datasets(cfg=cfg, cli_args=cli_args)
+    else:
+        dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
+
+    evaluate(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta)
+
+
+def do_cli(config: Union[Path, str] = Path("examples/"), **kwargs) -> None:
+    # pylint: disable=duplicate-code
+    parsed_cfg = load_cfg(config, **kwargs)
+    parser = HfArgumentParser(TrainerCliArgs)
+    parsed_cli_args, _ = parser.parse_args_into_dataclasses(
+        return_remaining_strings=True
+    )
+    do_evaluate(parsed_cfg, parsed_cli_args)
+
+
+if __name__ == "__main__":
+    load_dotenv()
+    fire.Fire(do_cli)

src/axolotl/cli/main.py

@@ -12,7 +12,8 @@ from axolotl.cli.utils import (
     build_command,
     fetch_from_github,
 )
-from axolotl.common.cli import PreprocessCliArgs, TrainerCliArgs
+from axolotl.common.cli import EvaluateCliArgs, PreprocessCliArgs, TrainerCliArgs
+from axolotl.utils import set_pytorch_cuda_alloc_conf
 from axolotl.utils.config.models.input.v0_4_1 import AxolotlInputConfig
 
 
@@ -48,6 +49,9 @@ def train(config: str, accelerate: bool, **kwargs):
     """Train or fine-tune a model."""
     kwargs = {k: v for k, v in kwargs.items() if v is not None}
 
+    # Enable expandable segments for cuda allocation to improve VRAM usage
+    set_pytorch_cuda_alloc_conf()
+
     if accelerate:
         base_cmd = ["accelerate", "launch", "-m", "axolotl.cli.train"]
         if config:
@@ -65,6 +69,31 @@ def train(config: str, accelerate: bool, **kwargs):
 @click.option(
     "--accelerate/--no-accelerate",
     default=True,
+    help="Use accelerate launch for multi-GPU training",
+)
+@add_options_from_dataclass(EvaluateCliArgs)
+@add_options_from_config(AxolotlInputConfig)
+def evaluate(config: str, accelerate: bool, **kwargs):
+    """Evaluate a model."""
+    kwargs = {k: v for k, v in kwargs.items() if v is not None}
+
+    if accelerate:
+        base_cmd = ["accelerate", "launch", "-m", "axolotl.cli.evaluate"]
+        if config:
+            base_cmd.append(config)
+        cmd = build_command(base_cmd, kwargs)
+        subprocess.run(cmd, check=True)  # nosec B603
+    else:
+        from axolotl.cli.evaluate import do_cli
+
+        do_cli(config=config, **kwargs)
+
+
+@cli.command()
+@click.argument("config", type=click.Path(exists=True, path_type=str))
+@click.option(
+    "--accelerate/--no-accelerate",
+    default=False,
     help="Use accelerate launch for multi-GPU inference",
 )
 @click.option(
@@ -95,7 +124,7 @@ def inference(
     if lora_model_dir:
         kwargs["lora_model_dir"] = lora_model_dir
     if base_model:
-        kwargs["output_dir"] = base_model
+        kwargs["base_model"] = base_model
 
     if accelerate:
         base_cmd = ["accelerate", "launch", "-m", "axolotl.cli.inference"]

src/axolotl/common/cli.py

@@ -15,6 +15,19 @@ configure_logging()
 LOG = logging.getLogger("axolotl.common.cli")
 
 
+@dataclass
+class PreprocessCliArgs:
+    """
+    dataclass representing arguments for preprocessing only
+    """
+
+    debug: bool = field(default=False)
+    debug_text_only: bool = field(default=False)
+    debug_num_examples: int = field(default=1)
+    prompter: Optional[str] = field(default=None)
+    download: Optional[bool] = field(default=True)
+
+
 @dataclass
 class TrainerCliArgs:
     """
@@ -31,16 +44,14 @@ class TrainerCliArgs:
 
 
 @dataclass
-class PreprocessCliArgs:
+class EvaluateCliArgs:
     """
-    dataclass representing arguments for preprocessing only
+    dataclass representing the various evaluation arguments
     """
 
     debug: bool = field(default=False)
     debug_text_only: bool = field(default=False)
-    debug_num_examples: int = field(default=1)
+    debug_num_examples: int = field(default=0)
-    prompter: Optional[str] = field(default=None)
-    download: Optional[bool] = field(default=True)
 
 
 def load_model_and_tokenizer(
@@ -50,7 +61,9 @@ def load_model_and_tokenizer(
 ):
     LOG.info(f"loading tokenizer... {cfg.tokenizer_config or cfg.base_model_config}")
     tokenizer = load_tokenizer(cfg)
+
     LOG.info("loading model and (optionally) peft_config...")
-    model, _ = load_model(cfg, tokenizer, inference=cli_args.inference)
+    inference = getattr(cli_args, "inference", False)
+    model, _ = load_model(cfg, tokenizer, inference=inference)
 
     return model, tokenizer

src/axolotl/core/tokenizer_utils.py

@@ -1,272 +0,0 @@
-"""
-helper functions for fixing the embeddings/tokenizer
-"""
-
-# Copyright 2023-present Daniel Han-Chen & the Unsloth team. All rights reserved.
-# GNU LESSER GENERAL PUBLIC LICENSE
-# Version 3, 29 June 2007
-#
-# Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
-# Everyone is permitted to copy and distribute verbatim copies
-# of this license document, but changing it is not allowed.
-
-import gc
-import itertools
-import logging
-from collections import Counter
-
-import datasets
-import numpy as np
-import torch
-
-LOG = logging.getLogger("axolotl.core.tokenizer_utils")
-
-
-@torch.inference_mode()
-def fix_untrained_tokens(  # pylint: disable=too-many-return-statements
-    model, tokenizer, train_dataset, ignored_tokenizer_names=None, eps=1e-16
-):
-    """
-    Llama-3 for eg has untrained vectors in the base model.
-    These include <|eot_id|>, <|start_header_id|>, <|end_header_id|>
-    We reset them to the mean of the rest of the tokens
-    """
-    # Code licensed under LGPL
-    embedding_matrix = model.get_input_embeddings().weight
-    lm_head_matrix = model.get_output_embeddings().weight
-    chat_template = getattr(tokenizer, "chat_template", None)
-    tokenizer = tokenizer.tokenizer if hasattr(tokenizer, "tokenizer") else tokenizer
-
-    # Ignore some model checks for now
-    if not ignored_tokenizer_names:
-        ignored_tokenizer_names = []
-    if (
-        model.config._name_or_path  # pylint: disable=protected-access
-        in ignored_tokenizer_names
-    ):
-        return
-
-    # Sometimes the sizes can be different like in vision models
-    # Ie <image> is in input, but not in output
-    min_size = min(embedding_matrix.shape[1], lm_head_matrix.shape[1])
-    embedding_matrix = embedding_matrix[:, :min_size]
-    lm_head_matrix = lm_head_matrix[:, :min_size]
-
-    # Get untrained tokens
-    indicator_untrained1 = torch.amax(embedding_matrix, axis=1) <= eps
-    # Check lm_head as well
-
-    # Does NOT work for Llama 3.1!!
-    indicator_untrained2 = torch.amax(lm_head_matrix, axis=1) <= eps
-
-    # We instead check for repeated vectors
-    lm_head_where = torch.where(indicator_untrained1)[0]
-    lm_head_bad = lm_head_matrix[lm_head_where]
-    lm_head_bad = lm_head_bad.cpu().float().numpy().round(3)
-    counter = Counter()
-    for row in lm_head_bad:
-        counter[hash(row.data.tobytes())] += 1
-    counter = Counter({k: c for k, c in counter.items() if c >= 2})
-
-    lm_head_where = lm_head_where.cpu().numpy()
-    final_bad_lm_head = []
-    for j, row in enumerate(lm_head_bad):
-        if hash(row.data.tobytes()) in counter:
-            final_bad_lm_head.append(lm_head_where[j])
-    indicator_untrained2 = indicator_untrained2 | torch.zeros_like(indicator_untrained2)
-    indicator_untrained2[final_bad_lm_head] = True
-
-    # Combine both checks
-    indicator_untrained = indicator_untrained1 & indicator_untrained2
-
-    # Remove pad token possibility
-    if hasattr(tokenizer, "pad_token_id"):
-        pad_token_id = tokenizer.pad_token_id
-        if pad_token_id is not None and pad_token_id < indicator_untrained.shape[0]:
-            indicator_untrained[pad_token_id] = False
-
-    where_untrained = torch.where(indicator_untrained)[0]
-    n_untrained = where_untrained.shape[0]
-    n_trained = embedding_matrix.shape[0] - n_untrained
-
-    # Get set and actual tokens
-    where_untrained = where_untrained.tolist()
-    if len(where_untrained) == 0:
-        return
-
-    # Remove untrained indices where it's longer
-    where_untrained_set = frozenset(where_untrained)
-    actual_bad_tokens = tokenizer.convert_ids_to_tokens(where_untrained)
-    # Remove None items in actual_bad_tokens
-    actual_bad_tokens = [x for x in actual_bad_tokens if x is not None]
-
-    # Check if tokenizer and training datasets have bad tokens
-    if_bad_first = False
-    if_bad_second = False
-    # Check tokenizer's chat template for any untrained tokens
-    if chat_template is not None:
-        if_bad_first = any(x in chat_template for x in actual_bad_tokens)
-
-    if isinstance(train_dataset, datasets.IterableDataset):
-        # Skip the check, since the code below assumes
-        # an indexable dataset
-        return
-
-    # Check the first 250, last 250 input_ids
-    size_dataset = len(train_dataset)
-    size = min(size_dataset, 250)
-    for j in range(size):
-        input_ids = train_dataset[j]
-        if "input_ids" in input_ids:
-            input_ids = input_ids["input_ids"]
-            if_bad = any(item in where_untrained_set for item in input_ids)
-            if if_bad:
-                if_bad_second = True
-                break
-
-    # Check last 250
-    if not if_bad_second:
-        left = max(size_dataset - 250, 0)
-        for j in range(left, size_dataset):
-            input_ids = train_dataset[j]
-            if "input_ids" in input_ids:
-                input_ids = input_ids["input_ids"]
-                if_bad = any(item in where_untrained_set for item in input_ids)
-                if if_bad:
-                    if_bad_second = True
-                    break
-
-    # Check if bad tokens exists!
-    if not if_bad_first and not if_bad_second:
-        return
-
-    # Check if lm_head / embed_token are trainable!
-    bad_not_trainable = False
-    if not embedding_matrix.requires_grad:
-        bad_not_trainable = True
-    if not lm_head_matrix.requires_grad:
-        bad_not_trainable = True
-
-    if bad_not_trainable:  # pylint: disable=too-many-nested-blocks
-        final_bad_items = []
-
-        # Re-check the first 250, last 250 input_ids
-        size_dataset = len(train_dataset)
-        size = min(size_dataset, 250)
-        for j in range(size):
-            input_ids = train_dataset[j]
-            if "input_ids" in input_ids:
-                input_ids = input_ids["input_ids"]
-                for item in input_ids:
-                    if item in where_untrained_set:
-                        final_bad_items.append(item)
-
-        # Re-check last 250
-        left = max(size_dataset - 250, 0)
-        for j in range(left, size_dataset):
-            input_ids = train_dataset[j]
-            if "input_ids" in input_ids:
-                input_ids = input_ids["input_ids"]
-                for item in input_ids:
-                    if item in where_untrained_set:
-                        final_bad_items.append(item)
-
-        # If no bad tokens, possibly chat template itself has issues?
-        if len(final_bad_items) == 0:
-            # Recheck 2000 and last 2000 items
-            size_dataset = len(train_dataset)
-            size = min(size_dataset, 2000)
-            for j in range(size):
-                input_ids = train_dataset[j]
-                if "input_ids" in input_ids:
-                    input_ids = input_ids["input_ids"]
-                    for item in input_ids:
-                        if item in where_untrained_set:
-                            final_bad_items.append(item)
-
-            # Re-check last 2000
-            left = max(size_dataset - 2000, 0)
-            for j in range(left, size_dataset):
-                input_ids = train_dataset[j]
-                if "input_ids" in input_ids:
-                    input_ids = input_ids["input_ids"]
-                    for item in input_ids:
-                        if item in where_untrained_set:
-                            final_bad_items.append(item)
-
-            # Most likely false signal!
-            if len(final_bad_items) == 0:
-                return
-
-        raise ValueError(
-            f"Untrained tokens of [{list(set(final_bad_items))}] found, but embed_tokens & lm_head not trainable, causing NaNs. "
-        )
-
-    # Count all the possible bad tokens
-    final_counts = np.zeros(
-        max(len(tokenizer), embedding_matrix.shape[0]), dtype=np.int64
-    )
-
-    def mapping(examples):
-        input_ids = examples["input_ids"]
-        counter = np.fromiter(itertools.chain.from_iterable(input_ids), dtype=np.int32)
-        np.add.at(final_counts, counter, 1)
-
-    train_dataset.map(mapping, batched=True, desc="Counting untrained tokens")
-
-    # Get counts for untrained tokens
-    counts_untrained = final_counts[where_untrained]
-    # Identify untrained tokens seen in train_dataset
-    indices_seen_in_train = np.where(counts_untrained > 0)[0]
-    tokens_to_update = [where_untrained[i] for i in indices_seen_in_train]
-
-    if len(tokens_to_update) == 0:
-        LOG.info(
-            "No untrained tokens found in train_dataset. No embeddings were modified."
-        )
-        return
-
-    # Log the token IDs that are being rescaled
-    LOG.info(
-        f"Rescaling embeddings for tokens seen in train_dataset: {tokens_to_update}"
-    )
-
-    # Get sum of all items
-    sum_embedding = torch.sum(embedding_matrix, dtype=torch.float32, axis=0)
-    sum_lm_head = torch.sum(lm_head_matrix, dtype=torch.float32, axis=0)
-
-    # Remove bad tokens
-    sum_embedding -= torch.sum(
-        embedding_matrix[where_untrained], dtype=torch.float32, axis=0
-    )
-    sum_lm_head -= torch.sum(
-        lm_head_matrix[where_untrained], dtype=torch.float32, axis=0
-    )
-
-    # Find correct average by dividing by sum of trained tokens
-    mean_embedding = sum_embedding / n_trained
-    mean_lm_head = sum_lm_head / n_trained
-
-    # Compute scaling for tokens to update
-    scaling = counts_untrained[indices_seen_in_train] / max(final_counts.max(), 1)
-    scaling = torch.tensor(scaling, device=mean_embedding.device).unsqueeze(1)
-
-    # Prepare mean embeddings for tokens to update
-    mean_embedding_repeated = (
-        mean_embedding.unsqueeze(0).repeat(len(tokens_to_update), 1) * scaling
-    )
-    mean_lm_head_repeated = (
-        mean_lm_head.unsqueeze(0).repeat(len(tokens_to_update), 1) * scaling
-    )
-
-    # Update embeddings only for tokens seen in train_dataset
-    embedding_matrix[tokens_to_update] = mean_embedding_repeated.to(
-        embedding_matrix.dtype
-    )
-    lm_head_matrix[tokens_to_update] = mean_lm_head_repeated.to(lm_head_matrix.dtype)
-
-    # Clean up
-    for _ in range(3):
-        gc.collect()
-        torch.cuda.empty_cache()
-    return

src/axolotl/core/trainer_builder.py

@@ -28,6 +28,7 @@ from torch import nn
 from torch.optim.lr_scheduler import OneCycleLR
 from torch.utils.data import BatchSampler, DataLoader, RandomSampler, SequentialSampler
 from transformers import (
+    DataCollatorWithFlattening,
     EarlyStoppingCallback,
     Trainer,
     TrainerCallback,
@@ -55,6 +56,7 @@ from axolotl.monkeypatch.relora import ReLoRACallback, ReLoRAScheduler
 from axolotl.utils import is_comet_available, is_mlflow_available
 from axolotl.utils.callbacks import (
     EvalFirstStepCallback,
+    GCCallback,
     GPUStatsCallback,
     LossWatchDogCallback,
     SaveAxolotlConfigtoWandBCallback,
@@ -65,7 +67,8 @@ from axolotl.utils.callbacks import (
     log_prediction_callback_factory,
 )
 from axolotl.utils.callbacks.lisa import lisa_callback_factory
-from axolotl.utils.chat_templates import get_chat_template
+from axolotl.utils.callbacks.profiler import PytorchProfilerCallback
+from axolotl.utils.chat_templates import get_chat_template_from_config
 from axolotl.utils.collators import (
     BatchSamplerDataCollatorForSeq2Seq,
     DataCollatorForSeq2Seq,
@@ -421,6 +424,11 @@ class SchedulerMixin(Trainer):
 
         return self.lr_scheduler
 
+    def _load_optimizer_and_scheduler(self, checkpoint):
+        if not checkpoint and self.args.optimizer_checkpoint is not None:
+            checkpoint = self.args.optimizer_checkpoint
+        return super()._load_optimizer_and_scheduler(checkpoint)
+
 
 class AxolotlTrainer(SchedulerMixin, Trainer):
     """
@@ -1363,6 +1371,13 @@ class TrainerBuilderBase(abc.ABC):
             plugin_manager.add_callbacks_pre_trainer(cfg=self.cfg, model=self.model)
         )
 
+        if self.cfg.profiler_steps:
+            callbacks.append(
+                PytorchProfilerCallback(
+                    steps_to_profile=self.cfg.profiler_steps,
+                )
+            )
+
         if self.cfg.use_wandb:
             callbacks.append(
                 SaveAxolotlConfigtoWandBCallback(self.cfg.axolotl_config_path)
@@ -1443,6 +1458,8 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
         if self.cfg.loss_watchdog_threshold is not None:
             callbacks.append(LossWatchDogCallback(self.cfg))
 
+        if self.cfg.gc_steps:
+            callbacks.append(GCCallback(gc_steps=self.cfg.gc_steps))
         callbacks.append(SaveModelCallback())
 
         return callbacks
@@ -1752,6 +1769,10 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
         ] = self.cfg.loraplus_lr_embedding
         training_arguments_kwargs["embedding_lr"] = self.cfg.embedding_lr
         training_arguments_kwargs["embedding_lr_scale"] = self.cfg.embedding_lr_scale
+        if self.cfg.optimizer_checkpoint:
+            training_arguments_kwargs[
+                "optimizer_checkpoint"
+            ] = self.cfg.optimizer_checkpoint
 
         if self.cfg.lr_scheduler in ["one_cycle", "log_sweep"]:
             training_arguments_kwargs["lr_scheduler_type"] = "cosine"
@@ -1822,8 +1843,8 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
         training_arguments_kwargs["model_type"] = self.cfg.model_config_type
         training_arguments_kwargs["pretraining"] = bool(self.cfg.pretraining_dataset)
         if self.cfg.chat_template:
-            training_arguments_kwargs["chat_template"] = get_chat_template(
+            training_arguments_kwargs["chat_template"] = get_chat_template_from_config(
-                self.cfg.chat_template,
+                cfg=self.cfg,
                 tokenizer=self.tokenizer,
             )
 
@@ -1981,9 +2002,11 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
                 V2BatchSamplerDataCollatorForSeq2Seq,
                 BatchSamplerDataCollatorForSeq2Seq,
                 DataCollatorForSeq2Seq,
+                DataCollatorWithFlattening,
                 RewardDataCollatorWithPadding,
             ]
         ]
+        collator_args = [self.tokenizer]
         if self.cfg.reward_model:
             collator = RewardDataCollatorWithPadding
             if "max_length" in kwargs:
@@ -2003,12 +2026,18 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
                 collator = MultiModalChatDataCollator
                 kwargs["processor"] = self.processor
                 kwargs["chat_template"] = training_args.chat_template
+            elif self.cfg.batch_flattening:
+                collator = DataCollatorWithFlattening
+                collator_args.pop(0)
+                kwargs.pop("pad_to_multiple_of", None)
+                kwargs.pop("padding", None)
             else:
                 collator = DataCollatorForSeq2Seq
 
+        kwargs["return_tensors"] = "pt"
+
         return collator(
-            self.tokenizer,
+            *collator_args,
-            return_tensors="pt",
             **kwargs,
         )