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

Libraries

How to use Kfkcome/ToolMaster-7B with Transformers:

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

pipe = pipeline("text-generation", model="Kfkcome/ToolMaster-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Kfkcome/ToolMaster-7B")
model = AutoModelForCausalLM.from_pretrained("Kfkcome/ToolMaster-7B")
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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Kfkcome/ToolMaster-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Kfkcome/ToolMaster-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": "Kfkcome/ToolMaster-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Kfkcome/ToolMaster-7B

SGLang

How to use Kfkcome/ToolMaster-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 "Kfkcome/ToolMaster-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": "Kfkcome/ToolMaster-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 "Kfkcome/ToolMaster-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": "Kfkcome/ToolMaster-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Kfkcome/ToolMaster-7B with Docker Model Runner:
```
docker model run hf.co/Kfkcome/ToolMaster-7B
```

Add model card and metadata

by nielsr HF Staff - opened Jan 21

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

+48

-0

nielsr

Jan 21

Hi! I'm Niels from the Hugging Face community science team. This PR adds a model card for the ToolMaster model, including relevant metadata such as the pipeline tag, library name, and base model information. It also links the repository to the associated paper and GitHub repository to improve discoverability and documentation.

Add model card and metadataaf931d87

Kfkcome changed pull request status to merged Jan 22

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