metadata
license: cc-by-nc-4.0
language:
- en
tags:
- text-generation
datasets:
- stanford_alpaca
pipeline_tag: text-generation
LLM Generation models trained by Jina AI, Finetuner team.
This repo contains the full weights (16bit) for Falcon-7b fit on the Code Alpaca dataset.
Reproduction
This version of the weights was trained with the following hyperparameters:
- Epochs: 6
- Batch size: 128
- Micro batch size: 8
- Learning rate: 3e-4
- Lora r: 8
- Lora target modules: query_key_value
You can reproduce using this repository:
https://github.com/jina-ai/jerboa
Make sure you install requirements and finetune using this command using the following command:
python finetune.py \
--base-model tiiuae/falcon-7b --lora-target-modules query_key_value \
--data-path sahil2801/CodeAlpaca-20k --output-dir ./lora-alpaca-code \
--batch-size 128 --micro-batch-size 8 --eval-limit 45 \
--eval-file code_eval.jsonl --wandb-project jerboa --wandb-log-model \
--wandb-watch gradients --num-epochs 6
Inference
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
TOKENIZER_SOURCE = 'tiiuae/falcon-7b'
BASE_MODEL = 'jinaai/falcon-7b-code-alpaca'
DEVICE = "cuda"
PROMPT = """
Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
### Instruction:
Write a for loop in python
### Input:
### Response:
"""
model = AutoModelForCausalLM.from_pretrained(
pretrained_model_name_or_path=BASE_MODEL,
torch_dtype=torch.float16,
trust_remote_code=True,
device_map='auto',
)
model.eval()
tokenizer = AutoTokenizer.from_pretrained(
TOKENIZER_SOURCE,
trust_remote_code=True,
padding_side='left',
)
tokenizer.pad_token = tokenizer.eos_token
inputs = tokenizer(PROMPT, return_tensors="pt")
input_ids = inputs["input_ids"].to(DEVICE)
input_attention_mask = inputs["attention_mask"].to(DEVICE)
with torch.no_grad():
generation_output = model.generate(
input_ids=input_ids,
attention_mask=input_attention_mask,
return_dict_in_generate=True,
max_new_tokens=32,
eos_token_id=tokenizer.eos_token_id,
)
generation_output = generation_output.sequences[0]
output = tokenizer.decode(generation_output, skip_special_tokens=True)
print(output)
Contact
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