Feat: Add example for Mistral (#644)

* Feat: Add example for Mistral

* chore: turn off flash

* chore: add is_mistral_derived_model

* chore: update following PR

README.md

@@ -413,9 +413,10 @@ tokenizer_legacy:
 # this is reported to improve training speed on some models
 resize_token_embeddings_to_32x:
 
-# used to identify if the model is falcon/llama based
+# used to identify which the model is based on
 is_falcon_derived_model:
 is_llama_derived_model:
+is_mistral_derived_model:
 
 # whether you are training a 4-bit GPTQ quantized model
 gptq: true

examples/mistral/config.yml

@@ -0,0 +1,62 @@
+base_model: mistralai/Mistral-7B-v0.1
+base_model_config: mistralai/Mistral-7B-v0.1
+model_type: MistralForCausalLM
+tokenizer_type: LlamaTokenizer
+is_mistral_derived_model: true
+
+load_in_8bit: false
+load_in_4bit: false
+strict: false
+
+datasets:
+  - path: mhenrichsen/alpaca_2k_test
+    type: alpaca
+dataset_prepared_path: last_run_prepared
+val_set_size: 0.01
+output_dir: ./out
+
+sequence_len: 8192
+sample_packing:
+pad_to_sequence_len:
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_run_id:
+wandb_log_model:
+
+gradient_accumulation_steps: 4
+micro_batch_size: 2
+num_epochs: 3
+optimizer: adamw_bnb_8bit
+lr_scheduler: cosine
+learning_rate: 0.0002
+
+train_on_inputs: false
+group_by_length: false
+bf16: true
+fp16: false
+tf32: false
+
+gradient_checkpointing: true
+early_stopping_patience:
+resume_from_checkpoint:
+local_rank:
+logging_steps: 1
+xformers_attention:
+flash_attention: true
+
+warmup_steps: 10
+eval_steps: 20
+eval_table_size: 5
+eval_table_max_new_tokens: 128
+save_steps:
+debug:
+deepspeed:
+weight_decay: 0.0
+fsdp:
+fsdp_config:
+special_tokens:
+  bos_token: "<s>"
+  eos_token: "</s>"
+  unk_token: "<unk>"

src/axolotl/utils/config.py

@@ -82,7 +82,7 @@ def normalize_config(cfg):
     cfg.is_llama_derived_model = (
         (hasattr(model_config, "model_type") and model_config.model_type == "llama")
         or cfg.is_llama_derived_model
-        or "llama" in cfg.base_model
+        or "llama" in cfg.base_model.lower()
         or (cfg.model_type and "llama" in cfg.model_type.lower())
     )
 
@@ -98,10 +98,23 @@ def normalize_config(cfg):
             ]
         )
         or cfg.is_falcon_derived_model
-        or "falcon" in cfg.base_model
+        or "falcon" in cfg.base_model.lower()
         or (cfg.model_type and "rwforcausallm" in cfg.model_type.lower())
     )
 
+    cfg.is_mistral_derived_model = (
+        (
+            hasattr(model_config, "model_type")
+            and model_config.model_type
+            in [
+                "mistral",
+            ]
+        )
+        or cfg.is_mistral_derived_model
+        or "mistral" in cfg.base_model.lower()
+        or (cfg.model_type and "mistral" in cfg.model_type.lower())
+    )
+
     log_gpu_memory_usage(LOG, "baseline", cfg.device)