FalCoder 🦅👩‍💻

Falcon-7b fine-tuned on the CodeAlpaca 20k instructions dataset by using the method QLoRA with PEFT library.

Model description 🧠

Falcon 7B

Training and evaluation data 📚

CodeAlpaca_20K: contains 20K instruction-following data used for fine-tuning the Code Alpaca model.

Training hyperparameters ⚙

TBA

Training results 🗒️

Example of usage 👩‍💻

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 }
}