| --- |
| tags: |
| - generated_from_trainer |
| - code |
| - coding |
| - llama |
| model-index: |
| - name: Llama-2-coder-7b |
| results: [] |
| license: apache-2.0 |
| language: |
| - code |
| thumbnail: https://huggingface.co/mrm8488/llama-2-coder-7b/resolve/main/llama2-coder-logo-removebg-preview.png |
| datasets: |
| - HuggingFaceH4/CodeAlpaca_20K |
| pipeline_tag: text-generation |
| --- |
| |
| <div style="text-align:center;width:250px;height:250px;"> |
| <img src="https://huggingface.co/mrm8488/llama-2-coder-7b/resolve/main/llama2-coder-logo-removebg-preview.png" alt="llama-2 coder logo""> |
| </div> |
| |
|
|
| # LlaMa 2 Coder 🦙👩💻 |
| **LlaMa-2 7b** fine-tuned on the **CodeAlpaca 20k instructions dataset** by using the method **QLoRA** with [PEFT](https://github.com/huggingface/peft) library. |
|
|
| ## Model description 🧠 |
|
|
| [Llama-2](https://huggingface.co/meta-llama/Llama-2-7b) |
|
|
| Llama 2 is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. |
| Meta developed and publicly released the Llama 2 family of large language models (LLMs), a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama-2-Chat, are optimized for dialogue use cases. Llama-2-Chat models outperform open-source chat models on most benchmarks we tested, and in our human evaluations for helpfulness and safety, are on par with some popular closed-source models like ChatGPT and PaLM. |
|
|
|
|
| ## Training and evaluation data 📚 |
|
|
| [CodeAlpaca_20K](https://huggingface.co/datasets/HuggingFaceH4/CodeAlpaca_20K): contains 20K instruction-following data used for fine-tuning the Code Alpaca model. |
|
|
|
|
| ### Training hyperparameters ⚙ |
|
|
| ```py |
| optim="paged_adamw_32bit", |
| num_train_epochs = 2, |
| eval_steps=50, |
| save_steps=50, |
| evaluation_strategy="steps", |
| save_strategy="steps", |
| save_total_limit=2, |
| seed=66, |
| load_best_model_at_end=True, |
| logging_steps=1, |
| learning_rate=2e-4, |
| fp16=True, |
| bf16=False, |
| max_grad_norm=0.3, |
| warmup_ratio=0.03, |
| group_by_length=True, |
| lr_scheduler_type="constant" |
| ``` |
|
|
| ### Training results 🗒️ |
|
|
|
|
| | Step | Training Loss | Validation Loss | |
| |------|----------|----------| |
| | 50 | 0.624400 | 0.600070 | |
| | 100 | 0.634100 | 0.592757 | |
| | 150 | 0.545800 | 0.586652 | |
| | 200 | 0.572500 | 0.577525 | |
| | 250 | 0.528000 | 0.590118 | |
|
|
|
|
| ### Eval results 📊 |
|
|
| WIP |
|
|
|
|
| ### Example of usage 👩💻 |
| ```py |
| import torch |
| from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig |
| |
| model_id = "mrm8488/llama-2-coder-7b" |
| |
| tokenizer = AutoTokenizer.from_pretrained(model_id) |
| |
| model = AutoModelForCausalLM.from_pretrained(model_id).to("cuda") |
| |
| def create_prompt(instruction): |
| system = "You are a coding assistant that will help the user to resolve the following instruction:" |
| instruction = "### Instruction: " + instruction |
| return system + "\n" + instruction + "\n\n" + "### Solution:" + "\n" |
| |
| def generate( |
| instruction, |
| max_new_tokens=128, |
| temperature=0.1, |
| top_p=0.75, |
| top_k=40, |
| num_beams=4, |
| **kwargs, |
| ): |
| prompt = create_prompt(instruction) |
| print(prompt) |
| inputs = tokenizer(prompt, return_tensors="pt") |
| input_ids = inputs["input_ids"].to("cuda") |
| attention_mask = inputs["attention_mask"].to("cuda") |
| generation_config = GenerationConfig( |
| temperature=temperature, |
| top_p=top_p, |
| top_k=top_k, |
| num_beams=num_beams, |
| **kwargs, |
| ) |
| with torch.no_grad(): |
| generation_output = model.generate( |
| input_ids=input_ids, |
| attention_mask=attention_mask, |
| generation_config=generation_config, |
| return_dict_in_generate=True, |
| output_scores=True, |
| max_new_tokens=max_new_tokens, |
| early_stopping=True |
| ) |
| s = generation_output.sequences[0] |
| output = tokenizer.decode(s) |
| return output.split("### Solution:")[1].lstrip("\n") |
| |
| instruction = """ |
| Edit the following XML code to add a navigation bar to the top of a web page |
| <html> |
| <head> |
| <title>CliBrAIn</title> |
| </head> |
| """ |
| print(generate(instruction)) |
| ``` |
|
|
| ### Citation |
|
|
| ``` |
| @misc {manuel_romero_2023, |
| author = { {Manuel Romero} }, |
| title = { llama-2-coder-7b (Revision d30d193) }, |
| year = 2023, |
| url = { https://huggingface.co/mrm8488/llama-2-coder-7b }, |
| doi = { 10.57967/hf/0931 }, |
| publisher = { Hugging Face } |
| } |
| ``` |
