|
--- |
|
tags: |
|
- generated_from_trainer |
|
- code |
|
- coding |
|
- llama |
|
model-index: |
|
- name: FalCoder |
|
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 } |
|
} |
|
``` |
