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Model Details

Model Description

Developed by: Rais Kazi
Model type: Fine-Tuned
License: Apache
Finetuned from model [optional]: Intel/neural-chat-7b-v3-1

Sample Code to call this model

import torch from peft import PeftModel, PeftConfig from transformers import AutoModelForCausalLM, AutoTokenizer from transformers import BitsAndBytesConfig

peft_model_id = "meetrais/finetuned-neural-chat-7b-v3-1" 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, max_new_tokens=30) print(tokenizer.decode(outputs[0], skip_special_tokens=True))

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