Hugging Face's logo Hugging Face
  • Models
  • Datasets
  • Spaces
  • Buckets new
  • Docs
  • Enterprise
  • Pricing
    • Website
      • Tasks
      • HuggingChat
      • Collections
      • Languages
      • Organizations
    • Community
      • Blog
      • Posts
      • Daily Papers
      • Learn
      • Discord
      • Forum
      • GitHub
    • Solutions
      • Team & Enterprise
      • Hugging Face PRO
      • Enterprise Support
      • Inference Providers
      • Inference Endpoints
      • Storage Buckets

  • Log In
  • Sign Up

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
732 MB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 27 commits
TensorCat's picture
TensorCat
Upload 4 files
70ae1f6 verified 7 days ago
  • Dataset
    Upload 4 files 7 days ago
  • assets
    Upload tensortalk_demo_chat.jpg about 1 month ago
  • models
    Delete UM_Handbook/models/.DS_Store 20 days ago
  • outputs
    Upload 50 files 21 days ago
  • Baseline_1_SFT_QWEN3_UM_Handbook_.ipynb
    276 kB
    Upload 2 files about 1 month ago
  • Baseline_2_RAG_SFT_QWEN3_UM_Handbook_A100_intelligent_harness_agent.ipynb
    764 kB
    Upload Baseline_2_RAG_SFT_QWEN3_UM_Handbook_A100_intelligent_harness_agent.ipynb 21 days ago
  • Improved_Model_PPO_QWEN3_UM_Handbook_RAG_Agent_Harness (3).ipynb
    1.03 MB
    Upload Improved_Model_PPO_QWEN3_UM_Handbook_RAG_Agent_Harness (3).ipynb 7 days ago
  • UM_Handbook_Markdown_Preprocess.py
    8.94 kB
    Upload 30 files about 1 month ago
  • UM_SFT_QA_Dataset_Builder_from_Index.py
    22.7 kB
    Upload 30 files about 1 month ago
  • UM_Source_Chunk_Dataset_Builder.py
    8.87 kB
    Upload 30 files about 1 month ago
  • um_handbook_config.py
    15.3 kB
    Upload 30 files about 1 month ago