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--- |
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license: apache-2.0 |
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base_model: mistralai/Mistral-7B-v0.1 |
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datasets: |
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- yahma/alpaca-cleaned |
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tags: |
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- sft |
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--- |
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# 🦙 Mistralpaca-7B |
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Mistral-7B model supervised fine-tuned on the [vicgalle/alpaca-gpt4](https://huggingface.co/datasets/vicgalle/alpaca-gpt4) dataset. |
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## 🧩 Configuration |
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```yaml |
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base_model: mistralai/Mistral-7B-v0.1 |
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model_type: MistralForCausalLM |
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tokenizer_type: LlamaTokenizer |
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is_mistral_derived_model: true |
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load_in_8bit: false |
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load_in_4bit: true |
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strict: false |
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datasets: |
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- path: vicgalle/alpaca-gpt4 |
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type: alpaca |
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dataset_prepared_path: |
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val_set_size: 0.01 |
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output_dir: ./out |
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sequence_len: 2048 |
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sample_packing: true |
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pad_to_sequence_len: true |
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adapter: qlora |
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lora_model_dir: |
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lora_r: 32 |
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lora_alpha: 64 |
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lora_dropout: 0.05 |
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lora_target_linear: true |
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wandb_project: axolotl |
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wandb_entity: |
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wandb_watch: |
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wandb_name: |
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wandb_log_model: |
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gradient_accumulation_steps: 3 |
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micro_batch_size: 4 |
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num_epochs: 3 |
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optimizer: adamw_bnb_8bit |
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lr_scheduler: cosine |
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learning_rate: 0.0002 |
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train_on_inputs: false |
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group_by_length: false |
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bf16: auto |
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fp16: |
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tf32: false |
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gradient_checkpointing: true |
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early_stopping_patience: |
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resume_from_checkpoint: |
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local_rank: |
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logging_steps: 1 |
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xformers_attention: |
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flash_attention: true |
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warmup_steps: 10 |
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evals_per_epoch: 4 |
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eval_table_size: |
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eval_table_max_new_tokens: 128 |
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saves_per_epoch: 1 |
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debug: |
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deepspeed: |
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weight_decay: 0.1 |
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fsdp: |
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fsdp_config: |
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special_tokens: |
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bos_token: <s> |
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eos_token: </s> |
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unk_token: <unk> |
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``` |
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## 💻 Usage |
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```python |
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!pip install -qU transformers accelerate |
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from transformers import AutoTokenizer |
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import transformers |
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import torch |
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model = "mlabonne/Mistralpaca-7B" |
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messages = [{"role": "user", "content": "What is a large language model?"}] |
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tokenizer = AutoTokenizer.from_pretrained(model) |
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prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) |
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pipeline = transformers.pipeline( |
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"text-generation", |
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model=model, |
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torch_dtype=torch.float16, |
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device_map="auto", |
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) |
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outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) |
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print(outputs[0]["generated_text"]) |
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``` |
