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
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")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("TensorCat/TensorTalk", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- 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
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## Project Screenshot
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The UI shows the intended runtime behavior: a user asks a UM/FSKTM handbook question, TensorTalk answers with RAG and official-web support enabled, and the interface exposes trace panels for planning, retrieved evidence, selected web evidence, grounding checks, and candidate URLs.
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## What This Project Does
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TensorTalk answers handbook-style questions about UM FSKTM academic rules, student guidance, programme details, facilities, dress-code guidance, industrial training, supervision policy, postgraduate requirements, and other faculty handbook topics.
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## What This Project Does
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TensorTalk answers handbook-style questions about UM FSKTM academic rules, student guidance, programme details, facilities, dress-code guidance, industrial training, supervision policy, postgraduate requirements, and other faculty handbook topics.
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