Create README.md

README.md

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+---
+license: apache-2.0
+datasets:
+- mlabonne/Evol-Instruct-Python-1k
+pipeline_tag: text-generation
+---
+# 🦙💻 EvolCodeLlama-7b
+
+📝 [Article](https://medium.com/@mlabonne/a-beginners-guide-to-llm-fine-tuning-4bae7d4da672)
+
+<center><img src="https://i.imgur.com/5m7OJQU.png" width="300"></center>
+
+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).
+
+## 🔧 Training
+
+It was trained on an RTX 3090 in 1h 11m 44s with the following configuration file:
+
+```yaml
+base_model: codellama/CodeLlama-7b-hf
+base_model_config: codellama/CodeLlama-7b-hf
+model_type: LlamaForCausalLM
+tokenizer_type: LlamaTokenizer
+is_llama_derived_model: true
+hub_model_id: EvolCodeLlama-7b
+
+load_in_8bit: false
+load_in_4bit: true
+strict: false
+
+datasets:
+  - path: mlabonne/Evol-Instruct-Python-1k
+    type: alpaca
+dataset_prepared_path: last_run_prepared
+val_set_size: 0.02
+output_dir: ./qlora-out
+
+adapter: qlora
+lora_model_dir:
+
+sequence_len: 2048
+sample_packing: true
+
+lora_r: 32
+lora_alpha: 16
+lora_dropout: 0.05
+lora_target_modules:
+lora_target_linear: true
+lora_fan_in_fan_out:
+
+wandb_project: axolotl
+wandb_entity:
+wandb_watch:
+wandb_run_id:
+wandb_log_model:
+
+gradient_accumulation_steps: 1
+micro_batch_size: 10
+num_epochs: 3
+optimizer: paged_adamw_32bit
+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: 100
+eval_steps: 0.01
+save_strategy: epoch
+save_steps:
+debug:
+deepspeed:
+weight_decay: 0.0
+fsdp:
+fsdp_config:
+special_tokens:
+  bos_token: "<s>"
+  eos_token: "</s>"
+  unk_token: "<unk>"
+```
+
+Here are the loss curves:
+
+![](https://i.imgur.com/zrBq01N.png)
+
+It is mainly designed for educational purposes, not for inference.
+
+## 💻 Usage
+
+``` python
+# pip install transformers accelerate
+
+from transformers import AutoTokenizer
+import transformers
+import torch
+
+model = "mlabonne/EvolCodeLlama-7b"
+prompt = "Your prompt"
+
+tokenizer = AutoTokenizer.from_pretrained(model)
+pipeline = transformers.pipeline(
+    "text-generation",
+    model=model,
+    torch_dtype=torch.float16,
+    device_map="auto",
+)
+
+sequences = pipeline(
+    f'{prompt}',
+    do_sample=True,
+    top_k=10,
+    num_return_sequences=1,
+    eos_token_id=tokenizer.eos_token_id,
+    max_length=200,
+)
+for seq in sequences:
+    print(f"Result: {seq['generated_text']}")
+```