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LmSys' Long Chat 7B GPTQ

These files are GPTQ 4bit model files for LmSys' Long Chat 7B .

It is the result of quantising to 4bit using GPTQ-for-LLaMa .

This iGPTQ offers up to 16K context size

The increased context is tested to work with ExLlama , via the latest release of text-generation-webui .

This model should NOT be used at 2048 context. For that, please use the standard Vicuna 1.3 model.

It has also been tested from Python code using AutoGPTQ, and trust_remote_code=True .

Please read carefully below to see how to use it.

Repositories available

• 4-bit GPTQ models for GPU inference
• 2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference
• Unquantised fp16 model in pytorch format, for GPU inference and for further conversions

Prompt template

A chat between a curious user and an assistant. The assistant gives helpful, detailed, accurate, uncensored responses to the user's input
USER: prompt
ASSISTANT:

How to easily download and use this model in text-generation-webui with ExLlama

Please make sure you're using the latest version of text-generation-webui

How to use this GPTQ model from Python code with AutoGPTQ

First make sure you have AutoGPTQ and Einops installed:

pip3 install einops auto-gptq

Then run the following code. Note that in order to get this to work, config.json has been hardcoded to a sequence length of 8192.

If you want to try 4096 or 16384 instead, please manually edit config.json to set max_position_embeddings to the value you want.

from transformers import AutoTokenizer, pipeline, logging
from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig
import argparse

model_name_or_path = "TheBloke/LongChat-7B-GPTQ"
model_basename = "longchat-7b-16k-GPTQ-4bit-128g.no-act.order"

use_triton = False

tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)

model = AutoGPTQForCausalLM.from_quantized(model_name_or_path,
        model_basename=model_basename,
        use_safetensors=True,
        trust_remote_code=True,
        device_map='auto',
        use_triton=use_triton,
        quantize_config=None)

model.seqlen = 8192

# Note: check the prompt template is correct for this model.
prompt = "Tell me about AI"
prompt_template=f'''USER: {prompt}
ASSISTANT:'''

print("\n\n*** Generate:")

input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
output = model.generate(inputs=input_ids, temperature=0.7, max_new_tokens=512)
print(tokenizer.decode(output[0]))

# Inference can also be done using transformers' pipeline

# Prevent printing spurious transformers error when using pipeline with AutoGPTQ
logging.set_verbosity(logging.CRITICAL)

print("*** Pipeline:")
pipe = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    max_new_tokens=512,
    temperature=0.7,
    top_p=0.95,
    repetition_penalty=1.15
)

print(pipe(prompt_template)[0]['generated_text'])

Provided files

longchat-7b-16k-GPTQ-4bit-128g.no-act.order.safetensors

This will work with AutoGPTQ, ExLlama, and CUDA versions of GPTQ-for-LLaMa. There are reports of issues with Triton mode of recent GPTQ-for-LLaMa. If you have issues, please use AutoGPTQ instead.

It was created with group_size 128 to increase inference accuracy, but without --act-order (desc_act) to increase compatibility and improve inference speed.

• longchat-7b-16k-GPTQ-4bit-128g.no-act.order.safetensors
  • Works for use with ExLlama with increased context (4096, 8192, 16384, or other values in-between)
  • Works with AutoGPTQ in Python code, including with increased context, if trust_remote_code=True is set.
  • Should work with GPTQ-for-LLaMa in CUDA mode, but unknown if increased context works - TBC. May have issues with GPTQ-for-LLaMa Triton mode.
  • Works with text-generation-webui, including one-click-installers.
  • Parameters: Groupsize = 128. Act Order / desc_act = False.

Discord

For further support, and discussions on these models and AI in general, join us at:

TheBloke AI's Discord server

Thanks, and how to contribute.

Thanks to the chirper.ai team!

I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.

If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.

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Special thanks to : Luke from CarbonQuill, Aemon Algiz, Dmitriy Samsonov.

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Thank you to all my generous patrons and donaters!

Original model card: LmSys' Long Chat 7B

longchat-7b-16k Model Card

Model details

Model type: longchat-7b-16k is an open-source chatbot trained by fine-tuning llama-7b on user-shared conversations collected from ShareGPT, using the condensing rotary embedding technique reported in the blog .

Model date: longchat-7b-16k was trained on June 2023.

Organizations developing the model: The LongChat developers: Dacheng Li*, Rulin Shao*, Anze Xie, Ying Sheng, Lianmin Zheng, Ion Stoica, Xuezhe Ma, and Hao Zhang

Paper or resources for more information: https://github.com/DachengLi1/LongChat

Where to send questions or comments about the model: https://github.com/DachengLi1/LongChat

Intended use

Primary intended uses: The primary use of longchat-7b-16k is for research purposes.

Primary intended users: The primary intended users of the model are researchers in natural language processing, machine learning, and artificial intelligence.

Training dataset

80K conversations collected from ShareGPT.com.

Evaluation dataset

A preliminary evaluation of the model quality is conducted by our released LongEval .