---
base_model: SUSTech/SUS-Chat-34B
inference: false
license: other
license_link: LICENSE
license_name: yi-license
model_creator: Southern university of science and technology
model_name: SUS Chat 34B
model_type: yi
pipeline_tag: text-generation
prompt_template: '### Human: {prompt}
### Assistant:
'
quantized_by: TheBloke
widget:
- example_title: SUS-Chat
output:
text: ' Hello! How can I assist you today?'
text: hi
---
# SUS Chat 34B - AWQ
- Model creator: [Southern university of science and technology](https://huggingface.co/SUSTech)
- Original model: [SUS Chat 34B](https://huggingface.co/SUSTech/SUS-Chat-34B)
## Description
This repo contains AWQ model files for [Southern university of science and technology's SUS Chat 34B](https://huggingface.co/SUSTech/SUS-Chat-34B).
These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).
### About AWQ
AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings.
AWQ models are currently supported on Linux and Windows, with NVidia GPUs only. macOS users: please use GGUF models instead.
It is supported by:
- [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ
- [vLLM](https://github.com/vllm-project/vllm) - version 0.2.2 or later for support for all model types.
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)
- [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later, from any code or client that supports Transformers
- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) - for use from Python code
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/SUS-Chat-34B-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/SUS-Chat-34B-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/SUS-Chat-34B-GGUF)
* [Southern university of science and technology's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/SUSTech/SUS-Chat-34B)
## Prompt template: SUS
```
### Human: {prompt}
### Assistant:
```
## Provided files, and AWQ parameters
I currently release 128g GEMM models only. The addition of group_size 32 models, and GEMV kernel models, is being actively considered.
Models are released as sharded safetensors files.
| Branch | Bits | GS | AWQ Dataset | Seq Len | Size |
| ------ | ---- | -- | ----------- | ------- | ---- |
| [main](https://huggingface.co/TheBloke/SUS-Chat-34B-AWQ/tree/main) | 4 | 128 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 8192 | 19.23 GB
## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui)
Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install.
1. Click the **Model tab**.
2. Under **Download custom model or LoRA**, enter `TheBloke/SUS-Chat-34B-AWQ`.
3. Click **Download**.
4. The model will start downloading. Once it's finished it will say "Done".
5. In the top left, click the refresh icon next to **Model**.
6. In the **Model** dropdown, choose the model you just downloaded: `SUS-Chat-34B-AWQ`
7. Select **Loader: AutoAWQ**.
8. Click Load, and the model will load and is now ready for use.
9. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right.
10. Once you're ready, click the **Text Generation** tab and enter a prompt to get started!
## Multi-user inference server: vLLM
Documentation on installing and using vLLM [can be found here](https://vllm.readthedocs.io/en/latest/).
- Please ensure you are using vLLM version 0.2 or later.
- When using vLLM as a server, pass the `--quantization awq` parameter.
For example:
```shell
python3 -m vllm.entrypoints.api_server --model TheBloke/SUS-Chat-34B-AWQ --quantization awq --dtype auto
```
- When using vLLM from Python code, again set `quantization=awq`.
For example:
```python
from vllm import LLM, SamplingParams
prompts = [
"Tell me about AI",
"Write a story about llamas",
"What is 291 - 150?",
"How much wood would a woodchuck chuck if a woodchuck could chuck wood?",
]
prompt_template=f'''### Human: {prompt}
### Assistant:
'''
prompts = [prompt_template.format(prompt=prompt) for prompt in prompts]
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
llm = LLM(model="TheBloke/SUS-Chat-34B-AWQ", quantization="awq", dtype="auto")
outputs = llm.generate(prompts, sampling_params)
# Print the outputs.
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
```
## Multi-user inference server: Hugging Face Text Generation Inference (TGI)
Use TGI version 1.1.0 or later. The official Docker container is: `ghcr.io/huggingface/text-generation-inference:1.1.0`
Example Docker parameters:
```shell
--model-id TheBloke/SUS-Chat-34B-AWQ --port 3000 --quantize awq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096
```
Example Python code for interfacing with TGI (requires [huggingface-hub](https://github.com/huggingface/huggingface_hub) 0.17.0 or later):
```shell
pip3 install huggingface-hub
```
```python
from huggingface_hub import InferenceClient
endpoint_url = "https://your-endpoint-url-here"
prompt = "Tell me about AI"
prompt_template=f'''### Human: {prompt}
### Assistant:
'''
client = InferenceClient(endpoint_url)
response = client.text_generation(prompt,
max_new_tokens=128,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1)
print(f"Model output: ", response)
```
## Inference from Python code using Transformers
### Install the necessary packages
- Requires: [Transformers](https://huggingface.co/docs/transformers) 4.35.0 or later.
- Requires: [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) 0.1.6 or later.
```shell
pip3 install --upgrade "autoawq>=0.1.6" "transformers>=4.35.0"
```
Note that if you are using PyTorch 2.0.1, the above AutoAWQ command will automatically upgrade you to PyTorch 2.1.0.
If you are using CUDA 11.8 and wish to continue using PyTorch 2.0.1, instead run this command:
```shell
pip3 install https://github.com/casper-hansen/AutoAWQ/releases/download/v0.1.6/autoawq-0.1.6+cu118-cp310-cp310-linux_x86_64.whl
```
If you have problems installing [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) using the pre-built wheels, install it from source instead:
```shell
pip3 uninstall -y autoawq
git clone https://github.com/casper-hansen/AutoAWQ
cd AutoAWQ
pip3 install .
```
### Transformers example code (requires Transformers 4.35.0 and later)
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
model_name_or_path = "TheBloke/SUS-Chat-34B-AWQ"
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
model = AutoModelForCausalLM.from_pretrained(
model_name_or_path,
low_cpu_mem_usage=True,
device_map="cuda:0"
)
# Using the text streamer to stream output one token at a time
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
prompt = "Tell me about AI"
prompt_template=f'''### Human: {prompt}
### Assistant:
'''
# Convert prompt to tokens
tokens = tokenizer(
prompt_template,
return_tensors='pt'
).input_ids.cuda()
generation_params = {
"do_sample": True,
"temperature": 0.7,
"top_p": 0.95,
"top_k": 40,
"max_new_tokens": 512,
"repetition_penalty": 1.1
}
# Generate streamed output, visible one token at a time
generation_output = model.generate(
tokens,
streamer=streamer,
**generation_params
)
# Generation without a streamer, which will include the prompt in the output
generation_output = model.generate(
tokens,
**generation_params
)
# Get the tokens from the output, decode them, print them
token_output = generation_output[0]
text_output = tokenizer.decode(token_output)
print("model.generate output: ", text_output)
# Inference is also possible via Transformers' pipeline
from transformers import pipeline
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
**generation_params
)
pipe_output = pipe(prompt_template)[0]['generated_text']
print("pipeline output: ", pipe_output)
```
## Compatibility
The files provided are tested to work with:
- [text-generation-webui](https://github.com/oobabooga/text-generation-webui) using `Loader: AutoAWQ`.
- [vLLM](https://github.com/vllm-project/vllm) version 0.2.0 and later.
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) version 1.1.0 and later.
- [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later.
- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) version 0.1.1 and later.
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute
Thanks to the [chirper.ai](https://chirper.ai) team!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
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.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: Michael Levine, 阿明, Trailburnt, Nikolai Manek, John Detwiler, Randy H, Will Dee, Sebastain Graf, NimbleBox.ai, Eugene Pentland, Emad Mostaque, Ai Maven, Jim Angel, Jeff Scroggin, Michael Davis, Manuel Alberto Morcote, Stephen Murray, Robert, Justin Joy, Luke @flexchar, Brandon Frisco, Elijah Stavena, S_X, Dan Guido, Undi ., Komninos Chatzipapas, Shadi, theTransient, Lone Striker, Raven Klaugh, jjj, Cap'n Zoog, Michel-Marie MAUDET (LINAGORA), Matthew Berman, David, Fen Risland, Omer Bin Jawed, Luke Pendergrass, Kalila, OG, Erik Bjäreholt, Rooh Singh, Joseph William Delisle, Dan Lewis, TL, John Villwock, AzureBlack, Brad, Pedro Madruga, Caitlyn Gatomon, K, jinyuan sun, Mano Prime, Alex, Jeffrey Morgan, Alicia Loh, Illia Dulskyi, Chadd, transmissions 11, fincy, Rainer Wilmers, ReadyPlayerEmma, knownsqashed, Mandus, biorpg, Deo Leter, Brandon Phillips, SuperWojo, Sean Connelly, Iucharbius, Jack West, Harry Royden McLaughlin, Nicholas, terasurfer, Vitor Caleffi, Duane Dunston, Johann-Peter Hartmann, David Ziegler, Olakabola, Ken Nordquist, Trenton Dambrowitz, Tom X Nguyen, Vadim, Ajan Kanaga, Leonard Tan, Clay Pascal, Alexandros Triantafyllidis, JM33133, Xule, vamX, ya boyyy, subjectnull, Talal Aujan, Alps Aficionado, wassieverse, Ari Malik, James Bentley, Woland, Spencer Kim, Michael Dempsey, Fred von Graf, Elle, zynix, William Richards, Stanislav Ovsiannikov, Edmond Seymore, Jonathan Leane, Martin Kemka, usrbinkat, Enrico Ros
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
# Original model card: Southern university of science and technology's SUS Chat 34B
# 🐷SUS-Chat: Instruction tuning done right
# News
- 2023-12-05: SUS-Chat is ranked 2nd in [Open LLM
leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
and surpassed all models under 70B.
- 2023-12-01: SUS-Chat-34B is now avaliable on HuggingFace🤗.
# Inrtoduction
**SUS-Chat** is a 34B bilingual Chinese-English dialogue model, jointly
released by the **Southern University of Science and Technology** and
**International Digital Economy Academy**. The SUS-Chat-34B model has
been fine-tuned on millions of high-quality, multilingual instruction
data. While maintaining the strong language capabilities of the base
model, the SUS-Chat-34B model has improved the model’s response to human
instructions through high-quality instruction fine-tuning and excels at
imitating human thought processes through chains of thought. It
introduces inter-instruction attention sharing in long texts, expanding
the window size from 4K to 8K, significantly enhancing the usability of
multi-round dialogues.
It has surpassed all models of the same size in almost all benchmark
tests and is better suited to meet the practical needs of complex
multilingual tasks. Compared to larger models, SUS-Chat-34B remains
highly competitive and achieved state-of-the-art performance in our
comprehensive evaluations.
SUS-Chat powerfully demonstrates that through the right instruction
fine-tuning, academic institutions can achieve better performance
without increasing model parameters, using open-source datasets and
models. This bridges the gap between academia and industry in large
language models and opens new possibilities for collaboration between
academic and industrial sectors.
# Performance
To better evaluate the performance of the SUS-Chat-34B model, we
conducted assessments across multiple benchmark tests and have
open-sourced the evaluation framework
[TLEM](https://huggingface.co/spaces/SUSTech/tlem) to facilitate
replication and comparison by other researchers.
In TLEM, we utilized various benchmark tests including MMLU, CMMLU,
C-Eval, BBH, GSM-8K, and MATH, focusing on measuring the model’s
knowledge and thinking capabilities. In these metrics, the SUS-Chat-34B
model achieved state-of-the-art performance. Additionally, we
incorporated
[lm-eval](https://github.com/EleutherAI/lm-evaluation-harness) to test
SUS-Chat and similar models on winogrande, hellaswag, arc, and
truthful-qa, assessing the model’s common-sense reasoning ability and
susceptibility to illusions.
Overall, the SUS-Chat-34B model significantly outperformed models of
similar scale and achieved the most advanced comprehensive performance.
| model | mmlu-chat | cmmlu-chat | ceval-chat | gsm8k | BBH | MATH | winogrande | arc | hellaswag | truthfulqa | average |
|:------------------|----------:|-----------:|-----------:|------:|------:|------:|-----------:|------:|----------:|-----------:|--------:|
| GPT-4 | 83 | 71 | 69.9 | 91.4 | 86.7 | 45.8 | 87.5 | 94.5 | 91.4 | nan | 80.1333 |
| SUS-Chat-34B | 77.35 | 78.68 | 82.42 | 80.06 | 67.62 | 28.8 | 81.22 | 81.54 | 83.79 | 57.47 | 71.895 |
| Qwen-72B-Chat | 74.52 | 77.02 | 77.22 | 76.57 | 72.63 | 35.9 | 80.58 | 81.29 | 87.02 | 50.64 | 71.339 |
| DeepSeek-67B-Chat | 69.43 | 48.51 | 59.7 | 74.45 | 69.73 | 29.56 | 76.09 | 82.1 | 86.06 | 56.37 | 65.2 |
| OrionStar-34B | 68.51 | 66.88 | 65.13 | 54.36 | 62.88 | 12.8 | 77.27 | 80.19 | 84.54 | 53.24 | 62.58 |
| Yi-34B-Chat | 66.96 | 55.16 | 77.16 | 63.76 | 61.54 | 10.02 | 76.64 | 70.66 | 82.29 | 54.57 | 61.876 |
# Usage
SUS-Chat-34B is a standard LLaMA model and should be seamlessly
compatible with the LLaMA ecosystem. We provide the following example to
demonstrate how it can be used for multi-turn dialogues.
``` python
from transformers import AutoModelForCausalLM, AutoTokenizer
def chat_template(messages):
history = ""
for message in messages:
match message:
case {"role": "user", "content": message}:
history += f"### Human: {message}\n\n### Assistant: "
case {"role": "assistant", "content": message}:
history += message
return history
model_path = "SUSTech/SUS-Chat-34B"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False)
model = AutoModelForCausalLM.from_pretrained(
model_path, device_map="auto", torch_dtype="auto"
).eval()
messages = [{"role": "user", "content": "hi"}]
input_ids = tokenizer.encode(chat_template(messages), return_tensors="pt").to("cuda")
output_ids = model.generate(input_ids.to("cuda"))
response = tokenizer.decode(
output_ids[0][input_ids.shape[1] :], skip_special_tokens=True
)
messages.append({"role": "assistant", "content": response})
# Second round
messages.append({"role": "user", "content": "What is the capital of China?"})
input_ids = tokenizer.encode(chat_template(messages), return_tensors="pt").to("cuda")
output_ids = model.generate(input_ids.to("cuda"))
response = tokenizer.decode(
output_ids[0][input_ids.shape[1] :], skip_special_tokens=True
)
messages.append({"role": "assistant", "content": response})
```
# Limitations
SUS-Chat has only undergone supervised fine-tuning and has not yet been
trained on human preference learning. As a result, it may produce
unreasonable responses in some situations and exacerbate existing issues
in language models, including hallucinations, non-determinism, and
cumulative errors. To achieve better performance for downstream tasks,
we recommend adjusting the generation configuration parameters
accordingly.
# Disclaimer
During the training process, we used data compliance check algorithms to
ensure the compliance of the training model as much as possible. Due to
the complexity of the data and the diverse use cases of language models,
we cannot guarantee that the model will produce correct and reasonable
outputs in all scenarios. Please be aware that there is still a risk of
the model generating problematic outputs. We will not be responsible for
any risks or issues arising from misuse, misguidance, illegal use, and
related misinformation, as well as data security issues related to the
model.
# License
This model is developed entirely for academic research and free
commercial use, but it must adhere to the
[license](https://github.com/SUSTech-IDEA/SUS-Chat/blob/main/MODEL_LICENSE_AGREEMENT.txt)
from 01-ai.
