license: other
widget:
- example_title: SUS-Chat
text: hi
output:
text: ' Hello! How can I assist you today?'
pipeline_tag: text-generation
tags:
- yi
- long context
- commercial use
- gptq
- function-calling
- function calling
extra_gated_prompt: >-
Purchase access to this repo
[HERE](https://buy.stripe.com/6oE9Bmg8t1Dt1ck9BL)!
Function Calling Fine-tuned Yi Chat 200k Context
Purchase access to this model here.
This model is fine-tuned for function calling.
- The function metadata format is the same as used for OpenAI.
- The model is suitable for commercial use.
- See the 'gptq' branch for the GPTQ model.
- AWQ and GGUF are available on request after purchase.
Check out other fine-tuned function calling models here.
Quick Server Setup
Runpod one click template, TGI API with EETQ (8bit) here. You must add a HuggingFace Hub access token (HUGGING_FACE_HUB_TOKEN) to the environment variables as this is a gated model. Runpod one click template, vLLM API with AWQ (4bit) here. You must add a HuggingFace Hub access token (HUGGING_FACE_HUB_TOKEN) to the environment variables as this is a gated model.
Runpod Affiliate Link (helps support the Trelis channel).
Inference Scripts
See below for sample prompt format.
Complete inference scripts are available for purchase here:
- Easily format prompts using tokenizer.apply_chat_format (starting from openai formatted functions and a list of messages)
- Automate catching, handling and chaining of function calls.
Prompt Format
B_FUNC, E_FUNC = "You have access to the following functions. Use them if required:\n\n", "\n\n"
B_INST, E_INST = "### Human: ", "\n\n### Assistant: " #SUSChat
prompt = f"{B_INST}{B_FUNC}{functionList.strip()}{E_FUNC}{user_prompt.strip()}{E_INST}\n\n"
Using tokenizer.apply_chat_template
For an easier application of the prompt, you can set up as follows:
Set up messages:
[
{
"role": "function_metadata",
"content": "FUNCTION_METADATA"
},
{
"role": "user",
"content": "What is the current weather in London?"
},
{
"role": "function_call",
"content": "{\n \"name\": \"get_current_weather\",\n \"arguments\": {\n \"city\": \"London\"\n }\n}"
},
{
"role": "function_response",
"content": "{\n \"temperature\": \"15 C\",\n \"condition\": \"Cloudy\"\n}"
},
{
"role": "assistant",
"content": "The current weather in London is Cloudy with a temperature of 15 Celsius"
}
]
with FUNCTION_METADATA as:
[
{
"type": "function",
"function": {
"name": "get_current_weather",
"description": "This function gets the current weather in a given city",
"parameters": {
"type": "object",
"properties": {
"city": {
"type": "string",
"description": "The city, e.g., San Francisco"
},
"format": {
"type": "string",
"enum": ["celsius", "fahrenheit"],
"description": "The temperature unit to use."
}
},
"required": ["city"]
}
}
},
{
"type": "function",
"function": {
"name": "get_clothes",
"description": "This function provides a suggestion of clothes to wear based on the current weather",
"parameters": {
"type": "object",
"properties": {
"temperature": {
"type": "string",
"description": "The temperature, e.g., 15 C or 59 F"
},
"condition": {
"type": "string",
"description": "The weather condition, e.g., 'Cloudy', 'Sunny', 'Rainy'"
}
},
"required": ["temperature", "condition"]
}
}
}
]
and then apply the chat template to get a formatted prompt:
tokenizer = AutoTokenizer.from_pretrained('Trelis/SUS-Chat-34B-function-calling-v3', trust_remote_code=True)
prompt = tokenizer.apply_chat_template(prompt, tokenize=False)
If you are using a gated model, you need to first run:
pip install huggingface_hub
huggingface-cli login
Manual Prompt:
Human: You have access to the following functions. Use them if required:
[
{
"type": "function",
"function": {
"name": "get_stock_price",
"description": "Get the stock price of an array of stocks",
"parameters": {
"type": "object",
"properties": {
"names": {
"type": "array",
"items": {
"type": "string"
},
"description": "An array of stocks"
}
},
"required": [
"names"
]
}
}
},
{
"type": "function",
"function": {
"name": "get_big_stocks",
"description": "Get the names of the largest N stocks by market cap",
"parameters": {
"type": "object",
"properties": {
"number": {
"type": "integer",
"description": "The number of largest stocks to get the names of, e.g. 25"
},
"region": {
"type": "string",
"description": "The region to consider, can be \"US\" or \"World\"."
}
},
"required": [
"number"
]
}
}
}
]
Get the names of the five largest stocks by market cap Assistant:
{
"name": "get_big_stocks",
"arguments": {
"number": 5
}
}<|endoftext|>
Dataset
See Trelis/function_calling_v3.
License
This model may be used commercially for inference according to the terms of the Yi license, or for further fine-tuning and inference. Users may not re-publish or re-sell this model in the same or derivative form (including fine-tunes).
** The SFT chat fine-tuned model's repo card follows below. **
🐷SUS-Chat: Instruction tuning done right
中文 ｜ English
News
2023-12-09: 🔥
Tigerbotvariant has been deleted,SUS-Chat-34Bis now the the top-ranked LLaMA model and the top-ranked chat model.2023-12-07: SUS-Chat-34B is now available on WiseModel🧠.
2023-12-06: Try SUS-Chat-34B chat-ui.
2023-12-05: SUS-Chat-34B is now available on ModelScope🤖
2023-12-05: SUS-Chat-34B is ranked 2nd in Open LLM leaderboard and surpassed all models under 70B.
2023-12-01: SUS-Chat-34B is now available on HuggingFace🤗.
Introduction
SUS-Chat-34B is a 34B bilingual Chinese-English dialogue model,
jointly released by the Southern University of Science and
Technology and
IDEA-CCNL. This model is based
on 01-ai/Yi-34B and 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-turn 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 has achieved state-of-the-art performance in our comprehensive evaluations.
SUS-Chat-34B model has the following highlights:
- Large-scale complex instruction following data: Trained with 1.4 billion tokens of high-quality complex instruction data, covering Chinese and English, multi-turn dialogues, mathematics, reasoning, and various other types of instruction data;
- Strong performance in general tasks: The SUS-Chat-34B model excels in numerous mainstream Chinese and English tasks, surpassing other open-source instruction fine-tuned models of the same parameter scale. It also competes well against models with larger parameter scales;
- Longer context window and excellent multi-turn dialogue capabilities: Currently, SUS-Chat-34B supports an 8K context window, and is trained with a large amount of multi-turn instruction and single-multi-turn mixed data, demonstrating remarkable capabilities in long-text dialogue information focus and instruction follow-up.
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 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, to measure 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 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.
English Understanding
|
Chinese Capabilities
|
C-Eval results are evaluated on the validation datasets↩︎
Math & Reasoning
| Model | gsm8k (0-shot) | MATH (0-shot) | BBH (0-shot) |
|---|---|---|---|
| GPT-4 | 91.4 | 45.8 | 86.7 |
| SUS-Chat-34B | 80.06 | 28.7 | 67.62 |
| Qwen-72b-Chat | 76.57 | 35.9 | 72.63 |
| Deepseek-68b-Chat | 74.45 | 29.56 | 69.73 |
| OrionStar-Yi-34B-Chat | 54.36 | 12.8 | 62.88 |
| Yi-34B-Chat | 63.76 | 10.02 | 61.54 |
More Tasks
| Model | winogrande (5-shot) | arc (25-shot) | hellaswag (10-shot) | TruthfulQA mc1 (0-shot) | TruthfulQA mc2 (0-shot) |
|---|---|---|---|---|---|
| GPT-4 | — | 94.5 | 91.4 | 59.00 | — |
| SUS-Chat-34B | 81.22 | 81.54 | 83.79 | 40.64 | 57.47 |
| Qwen-72b-Chat | 76.09 | 82.10 | 86.06 | 39.17 | 56.37 |
| Deepseek-68b-Chat | 80.58 | 81.29 | 87.02 | 40.02 | 50.64 |
| OrionStar-Yi-34B-Chat | 77.27 | 80.19 | 84.54 | 36.47 | 53.24 |
| Yi-34B-Chat | 76.64 | 70.66 | 82.29 | 38.19 | 54.57 |
Overall
| Model | Average |
|---|---|
| SUS-Chat-34B | 69.05 |
| Qwen-72b-Chat | 68.41 |
| Deepseek-68b-Chat | 62.91 |
| OrionStar-Yi-34B-Chat | 60.21 |
| Yi-34B-Chat | 59.72 |
To reproduce the results, please start a corresponding vllm server and refer to here.
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.
Feel free to open an issue if you have any questions.
from transformers import AutoModelForCausalLM, AutoTokenizer # 🤗 Transformers, or
# from modelscope import AutoModelForCausalLM, AutoTokenizer # 🤖 ModelScope
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"
# model_path = "SUSTC/SUS-Chat-34B" # ModelScope
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", add_special_tokens=False
).to("cuda")
output_ids = model.generate(input_ids.to("cuda"), max_length=256)
response = tokenizer.decode(
output_ids[0][input_ids.shape[1] :], skip_special_tokens=False
)
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", add_special_tokens=False
).to("cuda")
output_ids = model.generate(input_ids.to("cuda"), max_length=256)
response = tokenizer.decode(
output_ids[0][input_ids.shape[1] :], skip_special_tokens=False
)
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 from 01-ai.