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TensorCat
/
TensorTalk

Text Generation
Transformers
Safetensors
English
Chinese
qwen3
qwen3-8b
lora
qlora
sft
rag
faiss
dense-retrieval
agent
ppo
rlhf
rule-reward
harness-engineering
um-handbook
question-answering
chatbot
education
tensor-talk
Model card Files Files and versions
xet
Community

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

  • Libraries
  • Transformers

    How to use TensorCat/TensorTalk with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="TensorCat/TensorTalk")
    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("TensorCat/TensorTalk", dtype="auto")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps
  • vLLM

    How to use TensorCat/TensorTalk with vLLM:

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

    How to use TensorCat/TensorTalk 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 "TensorCat/TensorTalk" \
        --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": "TensorCat/TensorTalk",
    		"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 "TensorCat/TensorTalk" \
            --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": "TensorCat/TensorTalk",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use TensorCat/TensorTalk with Docker Model Runner:

    docker model run hf.co/TensorCat/TensorTalk
TensorTalk / UM_Handbook /outputs
675 MB
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  • 1 contributor
History: 6 commits
TensorCat's picture
TensorCat
Upload 50 files
2244cf2 verified 21 days ago
  • baseline2_rag_harness_agent
    Upload 50 files 21 days ago
  • qwen3_um_handbook_optimized_1
    Rename UM_Handbook/outputs/qwen3_um_handbook_optimized_1/merged_model/model.safetensor file link.txt to UM_Handbook/outputs/qwen3_um_handbook_optimized_1/merged_model/model.safetensor model file download link.txt about 1 month ago