mistral-finetune-gaban-samsay

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Direct Use

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig

base_model_id = "mistralai/Mistral-7B-v0.1"
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_use_double_quant=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16
)

base_model = AutoModelForCausalLM.from_pretrained(
    base_model_id,  # Mistral, same as before
    quantization_config=bnb_config,  # Same quantization config as before
    device_map="auto",
    trust_remote_code=True,
    use_auth_token=True
)

eval_tokenizer = AutoTokenizer.from_pretrained(base_model_id, add_bos_token=True, trust_remote_code=True)

from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM

ft_model = PeftModel.from_pretrained(base_model, "ctrltokyo/mistral-finetune-gaban-samsay")

# Inference

eval_prompt = """The following is a script for an episode of Kitchen Nightmares:
[Gordon] Goddamn it, this restaurant is in the toilet!
"""
model_input = eval_tokenizer(eval_prompt, return_tensors="pt").to("cuda")

ft_model.eval()
with torch.no_grad():
    print(eval_tokenizer.decode(ft_model.generate(**model_input, max_new_tokens=150, repetition_penalty=1.5)[0], skip_special_tokens=True))

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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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Framework versions

• PEFT 0.7.2.dev0