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openchat_openchat-3.6-8b-20240522-2_2bpw_exl2
Zoyd/openchat_openchat-3.6-8b-20240522-2_2bpw_exl2
openchat_openchat-3.6-8b-20240522-2_2bpw_exl2 is a text generation model from Zoyd. Use it when you need the model to write or continue text. It is set up for transformers. The card lists the license as llama3.
Exllamav2 quant (exl2 / 2.2 bpw) made with ExLlamaV2 v0.0.21
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At a glance
- Task
- Text Generation
- Library
- transformers
- License
- llama3
- Model type
- llama
- Access
- Public
- Created
- May 26, 2024
- Updated
- May 26, 2024
- SHA
- d2050a9c
Try a prompt
Base models
Model card
From the Hugging Face model README
Exllamav2 quant (exl2 / 2.2 bpw) made with ExLlamaV2 v0.0.21
Other EXL2 quants:
| Quant | Model Size | lm_head |
|---|---|---|
| <center>2.2</center> | <center>3250 MB</center> | <center>6</center> |
| <center>2.5</center> | <center>3478 MB</center> | <center>6</center> |
| <center>3.0</center> | <center>3895 MB</center> | <center>6</center> |
| <center>3.5</center> | <center>4311 MB</center> | <center>6</center> |
| <center>3.75</center> | <center>4519 MB</center> | <center>6</center> |
| <center>4.0</center> | <center>4726 MB</center> | <center>6</center> |
| <center>4.25</center> | <center>4932 MB</center> | <center>6</center> |
| <center>5.0</center> | <center>5552 MB</center> | <center>6</center> |
| <center>6.0</center> | <center>6497 MB</center> | <center>8</center> |
| <center>6.5</center> | <center>6913 MB</center> | <center>8</center> |
| <center>8.0</center> | <center>8144 MB</center> | <center>8</center> |
To use this model, we highly recommend installing the OpenChat package by following the installation guide in our repository and using the OpenChat OpenAI-compatible API server by running the serving command from the table below. The server is optimized for high-throughput deployment using vLLM and can run on a consumer GPU with 24GB RAM. To enable tensor parallelism, append --tensor-parallel-size N to the serving command.
Once started, the server listens at localhost:18888 for requests and is compatible with the OpenAI ChatCompletion API specifications. Please refer to the example request below for reference. Additionally, you can use the OpenChat Web UI for a user-friendly experience.
If you want to deploy the server as an online service, you can use --api-keys sk-KEY1 sk-KEY2 ... to specify allowed API keys and --disable-log-requests --disable-log-stats --log-file openchat.log for logging only to a file. For security purposes, we recommend using an HTTPS gateway in front of the server.
| Model | Size | Context | Weights | Serving |
|---|---|---|---|---|
| OpenChat-3.6-20240522 | 8B | 8192 | Huggingface | python -m ochat.serving.openai_api_server --model openchat/openchat-3.6-8b-20240522 |
curl http://localhost:18888/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "openchat_3.6",
"messages": [{"role": "user", "content": "You are a large language model named OpenChat. Write a poem to describe yourself"}]
}'
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))
Foundation Model Limitations Despite its advanced capabilities, OpenChat is still bound by the limitations inherent in its foundation models. These limitations may impact the model's performance in areas such as:
- Complex reasoning
- Mathematical and arithmetic tasks
- Programming and coding challenges
Hallucination of Non-existent Information OpenChat may sometimes generate information that does not exist or is not accurate, also known as "hallucination". Users should be aware of this possibility and verify any critical information obtained from the model.
Safety OpenChat may sometimes generate harmful, hate speech, biased responses, or answer unsafe questions. It's crucial to apply additional AI safety measures in use cases that require safe and moderated responses.
<div align="center"> <h2> 💌 Contact </h2> </div>We look forward to hearing from you and collaborating on this exciting project!
Project Lead:
- Guan Wang [imonenext at gmail dot com]
- Alpay Ariyak [aariyak at wpi dot edu]
@article{wang2023openchat,
title={OpenChat: Advancing Open-source Language Models with Mixed-Quality Data},
author={Wang, Guan and Cheng, Sijie and Zhan, Xianyuan and Li, Xiangang and Song, Sen and Liu, Yang},
journal={arXiv preprint arXiv:2309.11235},
year={2023}
}
- Task
- Text Generation
- Library
- transformers
- Type
- llama
- License
- llama3
- Created
- May 26, 2024
- Updated
- May 26, 2024