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--- |
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license: apache-2.0 |
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datasets: |
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- mlabonne/Evol-Instruct-Python-1k |
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pipeline_tag: text-generation |
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--- |
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# 🦙💻 EvolCodeLlama-7b |
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📝 [Article](https://medium.com/@mlabonne/a-beginners-guide-to-llm-fine-tuning-4bae7d4da672) |
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<center><img src="https://i.imgur.com/5m7OJQU.png" width="300"></center> |
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This is a [`codellama/CodeLlama-7b-hf`](https://huggingface.co/codellama/CodeLlama-7b-hf) model fine-tuned using QLoRA (4-bit precision) on the [`mlabonne/Evol-Instruct-Python-1k`](https://huggingface.co/datasets/mlabonne/Evol-Instruct-Python-1k). |
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## 🔧 Training |
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It was trained on an RTX 3090 in 1h 11m 44s with the following configuration file: |
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```yaml |
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base_model: codellama/CodeLlama-7b-hf |
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base_model_config: codellama/CodeLlama-7b-hf |
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model_type: LlamaForCausalLM |
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tokenizer_type: LlamaTokenizer |
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is_llama_derived_model: true |
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hub_model_id: EvolCodeLlama-7b |
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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: mlabonne/Evol-Instruct-Python-1k |
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type: alpaca |
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dataset_prepared_path: last_run_prepared |
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val_set_size: 0.02 |
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output_dir: ./qlora-out |
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adapter: qlora |
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lora_model_dir: |
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sequence_len: 2048 |
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sample_packing: true |
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lora_r: 32 |
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lora_alpha: 16 |
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lora_dropout: 0.05 |
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lora_target_modules: |
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lora_target_linear: true |
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lora_fan_in_fan_out: |
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wandb_project: axolotl |
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wandb_entity: |
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wandb_watch: |
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wandb_run_id: |
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wandb_log_model: |
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gradient_accumulation_steps: 1 |
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micro_batch_size: 10 |
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num_epochs: 3 |
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optimizer: paged_adamw_32bit |
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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: true |
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fp16: false |
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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: 100 |
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eval_steps: 0.01 |
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save_strategy: epoch |
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save_steps: |
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debug: |
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deepspeed: |
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weight_decay: 0.0 |
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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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Here are the loss curves: |
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It is mainly designed for educational purposes, not for inference. |
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[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) |
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## 💻 Usage |
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``` python |
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# pip install 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/EvolCodeLlama-7b" |
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prompt = "Your prompt" |
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tokenizer = AutoTokenizer.from_pretrained(model) |
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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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sequences = pipeline( |
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f'{prompt}', |
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do_sample=True, |
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top_k=10, |
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num_return_sequences=1, |
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eos_token_id=tokenizer.eos_token_id, |
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max_length=200, |
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) |
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for seq in sequences: |
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print(f"Result: {seq['generated_text']}") |
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``` |
