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