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---
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. -->
# FalCoder 🦅👩💻
**Falcon-7b** fine-tuned on the **CodeAlpaca 20k instructions dataset** by using the method **QLoRA** with [PEFT](https://github.com/huggingface/peft) library.
## Model description 🧠
[Falcon 7B](https://huggingface.co/tiiuae/falcon-7b)
## 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 ⚙
TBA
### Training results 🗒️
| 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 👩💻
```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
```
@misc {manuel_romero_2023,
author = { {Manuel Romero} },
title = { falcoder-7b (Revision e061237) },
year = 2023,
url = { https://huggingface.co/mrm8488/falcoder-7b },
doi = { 10.57967/hf/0789 },
publisher = { Hugging Face }
}
``` |