finetuned_mistral_7b

Model Details

Model Description

This is quantized model of mistral-7B.

• Developed by: Rais Kazi

Model Sources [optional]

https://github.com/meetrais/LLM-Fine-Tuning/blob/main/finetune_mistral_7b.py https://github.com/meetrais/LLM-Fine-Tuning/blob/main/call_finetune_mistral_7b.py

Training procedure

The following bitsandbytes quantization config was used during training:

• quant_method: QuantizationMethod.BITS_AND_BYTES
• load_in_8bit: False
• load_in_4bit: True
• llm_int8_threshold: 6.0
• llm_int8_skip_modules: None
• llm_int8_enable_fp32_cpu_offload: False
• llm_int8_has_fp16_weight: False
• bnb_4bit_quant_type: nf4
• bnb_4bit_use_double_quant: True
• bnb_4bit_compute_dtype: bfloat16

Framework versions

• PEFT 0.6.2.dev0

Code to call this mnodel

import torch

from peft import PeftModel, PeftConfig

from transformers import AutoModelForCausalLM, AutoTokenizer

from transformers import BitsAndBytesConfig

peft_model_id = "meetrais/finetuned_mistral_7b"

config = PeftConfig.from_pretrained(peft_model_id)

bnb_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_use_double_quant=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.bfloat16 )

model = AutoModelForCausalLM.from_pretrained(peft_model_id, quantization_config=bnb_config, device_map='auto')

tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)

if tokenizer.pad_token is None: tokenizer.add_special_tokens({'pad_token': '[PAD]'})

text = "Capital of USA is"

device = "cuda:0"

inputs = tokenizer(text, return_tensors="pt").to(device)

outputs = model.generate(**inputs, pad_token_id= tokenizer.eos_token_id, max_new_tokens=20)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))