Instructions to use DipeshChaudhary/ShareGPTChatBot-Counselchat1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DipeshChaudhary/ShareGPTChatBot-Counselchat1 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("DipeshChaudhary/ShareGPTChatBot-Counselchat1", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Unsloth Studio new
How to use DipeshChaudhary/ShareGPTChatBot-Counselchat1 with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for DipeshChaudhary/ShareGPTChatBot-Counselchat1 to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for DipeshChaudhary/ShareGPTChatBot-Counselchat1 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for DipeshChaudhary/ShareGPTChatBot-Counselchat1 to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="DipeshChaudhary/ShareGPTChatBot-Counselchat1", max_seq_length=2048, )
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
STEP 2: Now Follow the CODES
LOAD THE MODEL
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.
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Model tree for DipeshChaudhary/ShareGPTChatBot-Counselchat1
Base model
unsloth/llama-3-8b-Instruct-bnb-4bit