|
--- |
|
tags: |
|
- generated_from_trainer |
|
- code |
|
- coding |
|
model-index: |
|
- name: FalCoder |
|
results: [] |
|
license: apache-2.0 |
|
language: |
|
- code |
|
thumbnail: https://huggingface.co/mrm8488/falcoder-7b/resolve/main/falcoder.png |
|
datasets: |
|
- HuggingFaceH4/CodeAlpaca_20K |
|
pipeline_tag: text-generation |
|
--- |
|
|
|
<div style="text-align:center;width:250px;height:250px;"> |
|
<img src="https://huggingface.co/mrm8488/falcoder-7b/resolve/main/falcoder.png" alt="falcoder logo""> |
|
</div> |
|
|
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
|
should probably proofread and complete it, then remove this comment. --> |
|
|
|
# LlaMa 2 Coderπ¦π©βπ» |
|
**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 π§ |
|
|
|
[Llama-2](https://huggingface.co/tiiuae/falcon-7b) |
|
|
|
|
|
## Training and evaluation data π |
|
|
|
[CodeAlpaca_20K](https://huggingface.co/datasets/HuggingFaceH4/CodeAlpaca_20K): contains 20K instruction-following data used for fine-tuning the Code Alpaca model. |
|
|
|
|
|
### Training hyperparameters β |
|
|
|
TBA |
|
|
|
### Training results ποΈ |
|
|
|
| Step | Training Loss | Validation Loss | |
|
|------|---------------|-----------------| |
|
| 100 | 0.798500 | 0.767996 | |
|
| 200 | 0.725900 | 0.749880 | |
|
| 300 | 0.669100 | 0.748029 | |
|
| 400 | 0.687300 | 0.742342 | |
|
| 500 | 0.579900 | 0.736735 | |
|
|
|
|
|
|
|
### Example of usage π©βπ» |
|
```py |
|
import torch |
|
from transformers import AutoModelForCausalLM, AutoTokenizer, AutoTokenizer |
|
|
|
model_id = "mrm8488/falcoder-7b" |
|
|
|
tokenizer = AutoTokenizer.from_pretrained(model_id) |
|
|
|
model = AutoModelForCausalLM.from_pretrained(model_id).to("cuda") |
|
|
|
def generate( |
|
instruction, |
|
max_new_tokens=128, |
|
temperature=0.1, |
|
top_p=0.75, |
|
top_k=40, |
|
num_beams=4, |
|
**kwargs |
|
): |
|
prompt = instruction + "\n### Solution:\n" |
|
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 = "Design a class for representing a person in Python." |
|
print(generate(instruction)) |
|
``` |
|
|
|
### Citation |
|
|
|
|