Coding Models 👩💻
Collection
Collection to track the LLMs I fine-tuned for code generation
•
4 items
•
Updated
•
1
LlaMa 2 Coder 🦙👩💻
LlaMa-2 7b fine-tuned on the CodeAlpaca 20k instructions dataset by using the method QLoRA with PEFT library.
Model description 🧠
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: contains 20K instruction-following data used for fine-tuning the Code Alpaca model.
Training hyperparameters ⚙
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 👩💻
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 }
}
- Downloads last month
- 1,824
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social
visibility and check back later, or deploy to Inference Endpoints (dedicated)
instead.