Instructions to use FreedomIntelligence/AceGPT-v2-70B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use FreedomIntelligence/AceGPT-v2-70B with Transformers:

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

pipe = pipeline("text-generation", model="FreedomIntelligence/AceGPT-v2-70B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("FreedomIntelligence/AceGPT-v2-70B")
model = AutoModelForCausalLM.from_pretrained("FreedomIntelligence/AceGPT-v2-70B")

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use FreedomIntelligence/AceGPT-v2-70B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "FreedomIntelligence/AceGPT-v2-70B"
# 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-v2-70B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/FreedomIntelligence/AceGPT-v2-70B

SGLang

How to use FreedomIntelligence/AceGPT-v2-70B 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-v2-70B" \
    --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-v2-70B",
		"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-v2-70B" \
        --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-v2-70B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use FreedomIntelligence/AceGPT-v2-70B with Docker Model Runner:
```
docker model run hf.co/FreedomIntelligence/AceGPT-v2-70B
```

JuhaoLiang commited on Nov 14, 2024

Commit

10208f8

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@@ -78,8 +78,7 @@ Benchmarks for English and Chinese are conducted using the [OpenCompass](https:/
 # Reference
 ```
-@inproceedings{
-  liang2024alignment,
   title={Alignment at Pre-training! Towards Native Alignment for Arabic {LLM}s},
   author={Juhao Liang and Zhenyang Cai and Jianqing Zhu and Huang Huang and Kewei Zong and Bang An and Mosen Alharthi and Juncai He and Lian Zhang and Haizhou Li and Benyou Wang and Jinchao Xu},
   booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},

 # Reference
 ```
+@inproceedings{liang2024alignment,
   title={Alignment at Pre-training! Towards Native Alignment for Arabic {LLM}s},
   author={Juhao Liang and Zhenyang Cai and Jianqing Zhu and Huang Huang and Kewei Zong and Bang An and Mosen Alharthi and Juncai He and Lian Zhang and Haizhou Li and Benyou Wang and Jinchao Xu},
   booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},