--- 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](https://github.com/unslothai/unsloth#installation-instructions---conda) **Now Follow the CODE** ```markdown ``` 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 ) ``` # 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](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [](https://github.com/unslothai/unsloth)