Instructions to use YuyaoGe/SkillForge_Search_7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use YuyaoGe/SkillForge_Search_7B with Transformers:

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

pipe = pipeline("text-generation", model="YuyaoGe/SkillForge_Search_7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

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

tokenizer = AutoTokenizer.from_pretrained("YuyaoGe/SkillForge_Search_7B")
model = AutoModelForCausalLM.from_pretrained("YuyaoGe/SkillForge_Search_7B", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use YuyaoGe/SkillForge_Search_7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "YuyaoGe/SkillForge_Search_7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "YuyaoGe/SkillForge_Search_7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/YuyaoGe/SkillForge_Search_7B

SGLang

How to use YuyaoGe/SkillForge_Search_7B 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 "YuyaoGe/SkillForge_Search_7B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "YuyaoGe/SkillForge_Search_7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "YuyaoGe/SkillForge_Search_7B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "YuyaoGe/SkillForge_Search_7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use YuyaoGe/SkillForge_Search_7B with Docker Model Runner:
```
docker model run hf.co/YuyaoGe/SkillForge_Search_7B
```

SkillForge_Search_7B

Commit History

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Commit History

Add the model card and the overview figure 3a0fed1

Add the model card and the overview figure 058eb10

Add the model card and the overview figure 188cef5

Add the model card f930e32

Upload folder using huggingface_hub 7ffbcd7 verified

initial commit 63ebdd5 verified

Add the model card and the overview figure

3a0fed1

Add the model card and the overview figure

058eb10

Add the model card and the overview figure

188cef5

Add the model card

f930e32

Upload folder using huggingface_hub

7ffbcd7
verified

initial commit

63ebdd5
verified