Instructions to use FreedomIntelligence/AceGPT-7B-chat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use FreedomIntelligence/AceGPT-7B-chat with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="FreedomIntelligence/AceGPT-7B-chat")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("FreedomIntelligence/AceGPT-7B-chat")
model = AutoModelForCausalLM.from_pretrained("FreedomIntelligence/AceGPT-7B-chat", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use FreedomIntelligence/AceGPT-7B-chat with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "FreedomIntelligence/AceGPT-7B-chat"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FreedomIntelligence/AceGPT-7B-chat",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/FreedomIntelligence/AceGPT-7B-chat

SGLang

How to use FreedomIntelligence/AceGPT-7B-chat with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "FreedomIntelligence/AceGPT-7B-chat" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FreedomIntelligence/AceGPT-7B-chat",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "FreedomIntelligence/AceGPT-7B-chat" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FreedomIntelligence/AceGPT-7B-chat",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use FreedomIntelligence/AceGPT-7B-chat with Docker Model Runner:
```
docker model run hf.co/FreedomIntelligence/AceGPT-7B-chat
```

sxn6144 commited on Sep 21, 2023

Commit

4cb711c

1 Parent(s): 1ca4341

Update README.md

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@@ -9,27 +9,18 @@ Arabic language domain. This is the repository for the 7B-chat pretrained model.
 ---
 ## Model Details
-We have released the AceGPT family of large language models, which is a collection of fully fine-tuned generative
-text models based on LlaMA2, ranging from 7B to 13B parameters. Our models include two main categories: AceGPT
-and AceGPT-chat. AceGPT-chat is an optimized version specifically designed for dialogue applications. It is worth
-mentioning that our models have demonstrated superior performance compared to all currently available open-source
-Arabic dialogue models in multiple benchmark tests. Furthermore, in our human evaluations, our models have shown
-comparable satisfaction levels to some closed-source models, such as ChatGPT, in the Arabic language.
 ## Model Developers
-We are from the School of Data Science, the Chinese University of Hong Kong, Shenzhen (CUHKSZ), and
-the Shenzhen Research Institute of Big Data (SRIBD).
 ## Variations
-AceGPT famils comes in a range of parameter sizes —— 7B and 13B, each size of model has a base categorie
-and a -chat categorie.
 ## Input
 Models input text only.
 ## Output
 Models output text only.
 ## Model Evaluation Results
-Experiments on Arabic Vicuna-80, Arabic AlpacaEval. Numbers are the average perfor-mance ratio of ChatGPT
-over three runs. We do not report results of raw Llama-2 models since they cannot properly generate Arabic
-texts.
 |                              | Arabic Vicuna-80   | Arabic AlpacaEval   |
 |------------------------------|--------------------|---------------------|
 | Phoenix Chen et al. (2023a)                | 71.92% ± 0.2%      | 65.62% ± 0.3%       |

 ---
 ## Model Details
+We have released the AceGPT family of large language models, which is a collection of fully fine-tuned generative text models based on LlaMA2, ranging from 7B to 13B parameters. Our models include two main categories: AceGPT and AceGPT-chat. AceGPT-chat is an optimized version specifically designed for dialogue applications. It is worth mentioning that our models have demonstrated superior performance compared to all currently available open-source Arabic dialogue models in multiple benchmark tests. Furthermore, in our human evaluations, our models have shown comparable satisfaction levels to some closed-source models, such as ChatGPT, in the Arabic language.
 ## Model Developers
+We are from the School of Data Science, the Chinese University of Hong Kong, Shenzhen (CUHKSZ), and the Shenzhen Research Institute of Big Data (SRIBD).
 ## Variations
+AceGPT famils comes in a range of parameter sizes —— 7B and 13B, each size of model has a base categorie and a -chat categorie.
 ## Input
 Models input text only.
 ## Output
 Models output text only.
 ## Model Evaluation Results
+Experiments on Arabic Vicuna-80, Arabic AlpacaEval. Numbers are the average perfor-mance ratio of ChatGPT over three runs. We do not report results of raw Llama-2 models since they cannot properly generate Arabic texts.
 |                              | Arabic Vicuna-80   | Arabic AlpacaEval   |
 |------------------------------|--------------------|---------------------|
 | Phoenix Chen et al. (2023a)                | 71.92% ± 0.2%      | 65.62% ± 0.3%       |