Instructions to use Wanfq/Yi-6b-slimorca with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Wanfq/Yi-6b-slimorca with Transformers:

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

pipe = pipeline("text-generation", model="Wanfq/Yi-6b-slimorca", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("Wanfq/Yi-6b-slimorca", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Wanfq/Yi-6b-slimorca with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Wanfq/Yi-6b-slimorca"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Wanfq/Yi-6b-slimorca",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Wanfq/Yi-6b-slimorca

SGLang

How to use Wanfq/Yi-6b-slimorca 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 "Wanfq/Yi-6b-slimorca" \
    --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": "Wanfq/Yi-6b-slimorca",
		"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 "Wanfq/Yi-6b-slimorca" \
        --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": "Wanfq/Yi-6b-slimorca",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Wanfq/Yi-6b-slimorca with Docker Model Runner:
```
docker model run hf.co/Wanfq/Yi-6b-slimorca
```

Yi-6b sft using slimorca data.

Models	MMLU	BBH	AGIEval	ARC-C	Hellaswag	TruthfulQA	Avg.
Yi-6b	63.20	44.44	44.19	55.97	76.54	41.87	54.37
Yi-6b-slimorca	64.40	45.97	45.83	57.34	76.4	50.09	56.67

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