metadata
tags:
- generated_from_trainer
model-index:
- name: PomeranIAn
results: []
license: apache-2.0
language:
- code
thumbnail: >-
https://huggingface.co/mrm8488/pomeranian/resolve/main/pomeranian-removebg-preview.png
FalCoder
Falcon-7b fine-tuned on the CodeAlpaca 20k instructions dataset by using the method QLoRA with PEFT library.
Model description
Dataset
Intended uses & limitations
TBA
Training and evaluation data
TBA
Training hyperparameters
TBA
Training results
TBA
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))