metadata

license: other
language:
  - en
pipeline_tag: text-generation
inference: false
tags:
  - transformers
  - gguf
  - imatrix
  - openchat-3.6-8b-20240522

Quantizations of https://huggingface.co/openchat/openchat-3.6-8b-20240522

From original readme

Conversation templates

💡 Default Mode: Best for coding, chat and general tasks

GPT4 Correct User: Hello<|end_of_turn|>GPT4 Correct Assistant: Hi<|end_of_turn|>GPT4 Correct User: How are you today?<|end_of_turn|>GPT4 Correct Assistant:

⚠️ Notice: Remember to set <|end_of_turn|> as end of generation token.

The default template is also available as the integrated tokenizer.chat_template, which can be used instead of manually specifying the template:

messages = [
    {"role": "user", "content": "Hello"},
    {"role": "assistant", "content": "Hi"},
    {"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)

Inference using Transformers

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "openchat/openchat-3.6-8b-20240522"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")

messages = [
    {"role": "user", "content": "Explain how large language models work in detail."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)

outputs = model.generate(input_ids,
    do_sample=True,
    temperature=0.5,
    max_new_tokens=1024
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))