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

Libraries

How to use JinyiHan/JET-7B with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("JinyiHan/JET-7B")
model = AutoModelForCausalLM.from_pretrained("JinyiHan/JET-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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use JinyiHan/JET-7B with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/JinyiHan/JET-7B

SGLang

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

Docker Model Runner
How to use JinyiHan/JET-7B with Docker Model Runner:
```
docker model run hf.co/JinyiHan/JET-7B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

JET-7B

JET-7B is designed to improve the efficient reasoning of LLMs by training the base DeepSeek-Distill-Qwen-7B model with a reinforcement learning framework. Through this training, the model learns to generate high-quality reasoning steps while minimizing unnecessary computation and token usage.

Chat Template

def build_JET_chat_template(question, tokenizer):
    system_prompt = (
        "You are a helpful AI assistant. A conversation takes place between the User "
        "and the Assistant. The User asks a question, and the Assistant solves it.\n"
        "Please help me solve this question. Wrap only the final answer in \\boxed{}."
    )
    return tokenizer.apply_chat_template(
        [
            {"role": "system", "content": system_prompt},
            {"role": "user", "content": question}
        ],
        tokenize=False,
        add_generation_prompt=True
    )

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Safetensors

Model size

8B params

Tensor type

BF16

Model tree for JinyiHan/JET-7B

Quantizations

2 models