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AXERA-TECH
/
Qwen2.5-1.5B-Instruct

Text Generation
Transformers
Chinese
English
Context
Qwen2.5-1.5B-Instruct-GPTQ-INT8
Qwen2.5-1.5B-Instruct-GPTQ-INT4
Model card Files Files and versions
xet
Community
1

Instructions to use AXERA-TECH/Qwen2.5-1.5B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use AXERA-TECH/Qwen2.5-1.5B-Instruct with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="AXERA-TECH/Qwen2.5-1.5B-Instruct")
    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("AXERA-TECH/Qwen2.5-1.5B-Instruct", device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps Settings
  • vLLM

    How to use AXERA-TECH/Qwen2.5-1.5B-Instruct with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "AXERA-TECH/Qwen2.5-1.5B-Instruct"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "AXERA-TECH/Qwen2.5-1.5B-Instruct",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/AXERA-TECH/Qwen2.5-1.5B-Instruct
  • SGLang

    How to use AXERA-TECH/Qwen2.5-1.5B-Instruct 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 "AXERA-TECH/Qwen2.5-1.5B-Instruct" \
        --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": "AXERA-TECH/Qwen2.5-1.5B-Instruct",
    		"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 "AXERA-TECH/Qwen2.5-1.5B-Instruct" \
            --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": "AXERA-TECH/Qwen2.5-1.5B-Instruct",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use AXERA-TECH/Qwen2.5-1.5B-Instruct with Docker Model Runner:

    docker model run hf.co/AXERA-TECH/Qwen2.5-1.5B-Instruct
Qwen2.5-1.5B-Instruct
2.47 GB
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  • 3 contributors
History: 9 commits
lbourdois's picture
lbourdois
Improve language tag
30020cf verified over 1 year ago
  • qwen2.5-1.5b-ctx-ax650
    initial this repo over 1 year ago
  • qwen2.5_tokenizer
    initial this repo over 1 year ago
  • .gitattributes
    1.71 kB
    initial this repo over 1 year ago
  • README.md
    13.4 kB
    Improve language tag over 1 year ago
  • config.json
    0 Bytes
    initial this repo over 1 year ago
  • main
    981 kB
    xet
    Upload main over 1 year ago
  • main_axcl_aarch64
    1.72 MB
    xet
    initial this repo over 1 year ago
  • main_axcl_x86
    1.84 MB
    xet
    initial this repo over 1 year ago
  • post_config.json
    277 Bytes
    initial this repo over 1 year ago
  • qwen2.5_tokenizer_uid.py
    6.93 kB
    initial this repo over 1 year ago
  • run_qwen2.5_1.5b_ctx_ax650.sh
    653 Bytes
    initial this repo over 1 year ago
  • run_qwen2.5_1.5b_ctx_axcl_aarch64.sh
    679 Bytes
    initial this repo over 1 year ago
  • run_qwen2.5_1.5b_ctx_axcl_x86.sh
    675 Bytes
    initial this repo over 1 year ago