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JetLM
/
SDAR-1.7B-Chat

Text Generation
Transformers
Safetensors
sdar
conversational
custom_code
Model card Files Files and versions
xet
Community
1

Instructions to use JetLM/SDAR-1.7B-Chat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use JetLM/SDAR-1.7B-Chat with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="JetLM/SDAR-1.7B-Chat", trust_remote_code=True)
    messages = [
        {"role": "user", "content": "Who are you?"},
    ]
    pipe(messages)
    # Load model directly
    from transformers import AutoModelForCausalLM
    model = AutoModelForCausalLM.from_pretrained("JetLM/SDAR-1.7B-Chat", trust_remote_code=True, dtype="auto")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps
  • vLLM

    How to use JetLM/SDAR-1.7B-Chat with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "JetLM/SDAR-1.7B-Chat"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/chat/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "JetLM/SDAR-1.7B-Chat",
    		"messages": [
    			{
    				"role": "user",
    				"content": "What is the capital of France?"
    			}
    		]
    	}'
    Use Docker
    docker model run hf.co/JetLM/SDAR-1.7B-Chat
  • SGLang

    How to use JetLM/SDAR-1.7B-Chat 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 "JetLM/SDAR-1.7B-Chat" \
        --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": "JetLM/SDAR-1.7B-Chat",
    		"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 "JetLM/SDAR-1.7B-Chat" \
            --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": "JetLM/SDAR-1.7B-Chat",
    		"messages": [
    			{
    				"role": "user",
    				"content": "What is the capital of France?"
    			}
    		]
    	}'
  • Docker Model Runner

    How to use JetLM/SDAR-1.7B-Chat with Docker Model Runner:

    docker model run hf.co/JetLM/SDAR-1.7B-Chat
SDAR-1.7B-Chat
4.08 GB
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  • 3 contributors
History: 15 commits
chengs18's picture
chengs18
Update modeling_sdar.py
97cf0dc verified 3 months ago
  • .gitattributes
    1.52 kB
    initial commit 10 months ago
  • README.md
    4.35 kB
    Update README.md 7 months ago
  • added_tokens.json
    727 Bytes
    Upload folder using huggingface_hub 10 months ago
  • chat_template.jinja
    4.12 kB
    Upload folder using huggingface_hub 10 months ago
  • config.json
    932 Bytes
    Update config.json 9 months ago
  • configuration_sdar.py
    11.2 kB
    Replace Qwen3 configs/modeling with SDAR version 9 months ago
  • generation_config.json
    239 Bytes
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  • merges.txt
    1.67 MB
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  • model.safetensors
    4.06 GB
    xet
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  • modeling_sdar.py
    39.1 kB
    Update modeling_sdar.py 3 months ago
  • special_tokens_map.json
    630 Bytes
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  • tokenization_qwen2.py
    14 kB
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  • tokenization_qwen2_fast.py
    5.25 kB
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  • tokenizer.json
    7.03 MB
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  • tokenizer_config.json
    10 kB
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  • vocab.json
    2.78 MB
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