Mistral-Depth-UP-Scaled-9B

Mistral-Depth-UP-Scaled-9B

An auto-regressive causal LM created by combining 2x finetuned mistral 7B into one.

Benchmarks

Coming soon.

Usage :

# Load in4Bit

from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
import torch

nf4_config = BitsAndBytesConfig(
   load_in_4bit=True,
   bnb_4bit_quant_type="nf4",
   bnb_4bit_use_double_quant=True,
   bnb_4bit_compute_dtype=torch.bfloat16
)
model = AutoModelForCausalLM.from_pretrained(
    "ayoubkirouane/Mistral-Depth-UP-Scaled-9B",
    device_map='auto',
    quantization_config=nf4_config,
    use_cache=False
)
tokenizer = AutoTokenizer.from_pretrained("ayoubkirouane/Mistral-Depth-UP-Scaled-9B")

tokenizer.pad_token = tokenizer.eos_token
tokenizer.padding_side = "right"

def generate_response(prompt, model , max_new_tokens):
  encoded_input = tokenizer(prompt,  return_tensors="pt", add_special_tokens=True)
  model_inputs = encoded_input.to('cuda')

  generated_ids = model.generate(**model_inputs, max_new_tokens=max_new_tokens, do_sample=True, pad_token_id=tokenizer.eos_token_id)

  decoded_output = tokenizer.batch_decode(generated_ids)

  return decoded_output[0].replace(prompt, "")


generate_response(prompt="What is GANs ?", model=model , max_new_tokens=100)