library_name: peft
base_model: mistralai/Mistral-7B-v0.1
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))