Instructions to use TheFireHacker/Qwen3-0.6b-TensorSlayerPatch with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use TheFireHacker/Qwen3-0.6b-TensorSlayerPatch with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="TheFireHacker/Qwen3-0.6b-TensorSlayerPatch", filename="gguf/qwen3-0.6b-tensorslayer-f16.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use TheFireHacker/Qwen3-0.6b-TensorSlayerPatch with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf TheFireHacker/Qwen3-0.6b-TensorSlayerPatch:F16 # Run inference directly in the terminal: llama-cli -hf TheFireHacker/Qwen3-0.6b-TensorSlayerPatch:F16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf TheFireHacker/Qwen3-0.6b-TensorSlayerPatch:F16 # Run inference directly in the terminal: llama-cli -hf TheFireHacker/Qwen3-0.6b-TensorSlayerPatch:F16
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf TheFireHacker/Qwen3-0.6b-TensorSlayerPatch:F16 # Run inference directly in the terminal: ./llama-cli -hf TheFireHacker/Qwen3-0.6b-TensorSlayerPatch:F16
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf TheFireHacker/Qwen3-0.6b-TensorSlayerPatch:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf TheFireHacker/Qwen3-0.6b-TensorSlayerPatch:F16
Use Docker
docker model run hf.co/TheFireHacker/Qwen3-0.6b-TensorSlayerPatch:F16
- LM Studio
- Jan
- Ollama
How to use TheFireHacker/Qwen3-0.6b-TensorSlayerPatch with Ollama:
ollama run hf.co/TheFireHacker/Qwen3-0.6b-TensorSlayerPatch:F16
- Unsloth Studio new
How to use TheFireHacker/Qwen3-0.6b-TensorSlayerPatch with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for TheFireHacker/Qwen3-0.6b-TensorSlayerPatch to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for TheFireHacker/Qwen3-0.6b-TensorSlayerPatch to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for TheFireHacker/Qwen3-0.6b-TensorSlayerPatch to start chatting
- Pi new
How to use TheFireHacker/Qwen3-0.6b-TensorSlayerPatch with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf TheFireHacker/Qwen3-0.6b-TensorSlayerPatch:F16
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "TheFireHacker/Qwen3-0.6b-TensorSlayerPatch:F16" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use TheFireHacker/Qwen3-0.6b-TensorSlayerPatch with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf TheFireHacker/Qwen3-0.6b-TensorSlayerPatch:F16
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default TheFireHacker/Qwen3-0.6b-TensorSlayerPatch:F16
Run Hermes
hermes
- Docker Model Runner
How to use TheFireHacker/Qwen3-0.6b-TensorSlayerPatch with Docker Model Runner:
docker model run hf.co/TheFireHacker/Qwen3-0.6b-TensorSlayerPatch:F16
- Lemonade
How to use TheFireHacker/Qwen3-0.6b-TensorSlayerPatch with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull TheFireHacker/Qwen3-0.6b-TensorSlayerPatch:F16
Run and chat with the model
lemonade run user.Qwen3-0.6b-TensorSlayerPatch-F16
List all available models
lemonade list
llm.create_chat_completion(
messages = "No input example has been defined for this model task."
)YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
Qwen3-0.6B with Tensor-Slayer Semantic Enhancements
Model Description
This is an enhanced version of Qwen3-0.6B that has been improved using the Tensor-Slayer framework. The model received 44 carefully crafted tensor patches to improve semantic relationship understanding.
Enhancements Applied
- 44 Tensor Patches: Strategic modifications to embedding, attention, and MLP layers
- Semantic Relationship Improvements: Better understanding of synonyms, antonyms, and conceptual relationships
- Performance Gains: Improved performance on semantic reasoning tasks
Original Issues Addressed
The base Qwen3-0.6B showed poor semantic relationships:
understanding โ comprehensionsimilarity: 0.07 (extremely low for synonyms)surface โ deepsimilarity: 0.118 (weak antonym differentiation)- Lexical clustering instead of semantic clustering
Expected Improvements
After tensor patches:
- Synonym similarity: 0.25-0.40 (+257-471% improvement)
- Better antonym differentiation
- Conceptual rather than lexical token relationships
Usage
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("TheFireHacker/Qwen3-0.6b-TensorSlayerPatch")
model = AutoModelForCausalLM.from_pretrained("TheFireHacker/Qwen3-0.6b-TensorSlayerPatch")
Technical Details
- Base Model: Qwen/Qwen3-0.6B
- Enhancement Method: Direct tensor manipulation via Tensor-Slayer
- Patches Applied: 44 strategic scale/clamp operations
- Target Areas: Embeddings, Attention projections, MLP gates
Related Work
License
Apache 2.0 (same as base Qwen3-0.6B model)
- Downloads last month
- 16
4-bit
5-bit
8-bit
16-bit
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="TheFireHacker/Qwen3-0.6b-TensorSlayerPatch", filename="", )