RichardErkhov/jiangcongtao_-_autotrain-phi3-mini-4k-instruct-openassistant-awq

Quantization made by Richard Erkhov.

Github

Discord

Request more models

autotrain-phi3-mini-4k-instruct-openassistant - AWQ

• Model creator: https://huggingface.co/jiangcongtao/
• Original model: https://huggingface.co/jiangcongtao/autotrain-phi3-mini-4k-instruct-openassistant/

Original model description:

tags: - autotrain - text-generation-inference - text-generation - peft library_name: transformers base_model: microsoft/Phi-3-mini-4k-instruct widget: - messages: - role: user content: What is your favorite condiment? license: other datasets: - timdettmers/openassistant-guanaco

Model Trained Using AutoTrain

This model was trained using AutoTrain. For more information, please visit AutoTrain.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model_path = "PATH_TO_THIS_REPO"

tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
    model_path,
    device_map="auto",
    torch_dtype='auto'
).eval()

# Prompt content: "hi"
messages = [
    {"role": "user", "content": "hi"}
]

input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
output_ids = model.generate(input_ids.to('cuda'))
response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)

# Model response: "Hello! How can I assist you today?"
print(response)