metadata

language:
  - en
license: apache-2.0
tags:
  - text-generation-inference
  - transformers
  - unsloth
  - llama
  - trl
base_model: unsloth/llama-3-8b-Instruct-bnb-4bit

To Use This Model

STEP 1:

Installs Unsloth, Xformers (Flash Attention) and all other packages! according to your environments and GPU
To install Unsloth on your own computer, follow the installation instructions on our Github page : LINK IS HERE

Now Follow the CODE

from unsloth import FastLanguageModel

  import torch
  max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!
  dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
  load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.
  from transformers import AutoTokenizer

  model, tokenizer = FastLanguageModel.from_pretrained(
  model_name="DipeshChaudhary/ShareGPTChatBot-Counselchat1",  # Your fine-tuned model
  max_seq_length=max_seq_length,
  dtype=dtype,
  load_in_4bit=load_in_4bit,
  )

We now use the Llama-3 format for conversation style finetunes. We use Open Assistant conversations in ShareGPT style.

We use our get_chat_template function to get the correct chat template. They support zephyr, chatml, mistral, llama, alpaca, vicuna, vicuna_old and their own optimized unsloth template

  from unsloth.chat_templates import get_chat_template
  tokenizer = get_chat_template(
  tokenizer,
  chat_template = "llama-3", # Supports zephyr, chatml, mistral, llama, alpaca, vicuna, vicuna_old, unsloth
  mapping = {"role" : "from", "content" : "value", "user" : "human", "assistant" : "gpt"}, # ShareGPT style
  )

FOR ACTUAL INFERENCE

  FastLanguageModel.for_inference(model) # Enable native 2x faster inference

  messages = [
      {"from": "human", "value": "I'm worry about my exam."},
  ]
  inputs = tokenizer.apply_chat_template(
      messages,
      tokenize = True,
      add_generation_prompt = True, # Must add for generation
      return_tensors = "pt",
  ).to("cuda")

  from transformers import TextStreamer
  text_streamer = TextStreamer(tokenizer)
  x= model.generate(input_ids = inputs, streamer = text_streamer, max_new_tokens = 128, use_cache = True)

Uploaded model

Developed by: DipeshChaudhary
License: apache-2.0
Finetuned from model : unsloth/llama-3-8b-Instruct-bnb-4bit

This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.