Instructions to use TheFireHacker/Qwen3-0.6b-TensorSlayerPatch with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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

TheFireHacker commited on Sep 8, 2025

Commit

bec0b3f

verified ·

1 Parent(s): 8dc1f2a

Add GGUF quantized versions for Ollama/llama.cpp compatibility with API instructions

Browse files

Files changed (6) hide show

.gitattributes +4 -0
gguf/README.md +5 -5
gguf/qwen3-0.6b-tensorslayer-f16.gguf +3 -0
gguf/qwen3-0.6b-tensorslayer-q4_0.gguf +3 -0
gguf/qwen3-0.6b-tensorslayer-q5_k_m.gguf +3 -0
gguf/qwen3-0.6b-tensorslayer-q8_0.gguf +3 -0

.gitattributes CHANGED Viewed

@@ -34,3 +34,7 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
 tokenizer.json filter=lfs diff=lfs merge=lfs -text

 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
 tokenizer.json filter=lfs diff=lfs merge=lfs -text
+gguf/qwen3-0.6b-tensorslayer-f16.gguf filter=lfs diff=lfs merge=lfs -text
+gguf/qwen3-0.6b-tensorslayer-q4_0.gguf filter=lfs diff=lfs merge=lfs -text
+gguf/qwen3-0.6b-tensorslayer-q5_k_m.gguf filter=lfs diff=lfs merge=lfs -text
+gguf/qwen3-0.6b-tensorslayer-q8_0.gguf filter=lfs diff=lfs merge=lfs -text

gguf/README.md CHANGED Viewed

@@ -6,10 +6,10 @@ This folder contains GGUF (GPT-Generated Unified Format) versions of the Tensor-
 | File | Quantization | Size | Use Case |
 |------|-------------|------|----------|
-| `qwen3-0.6b-tensorslayer-f16.gguf` | FP16 | ~1.2GB | Maximum quality |
-| `qwen3-0.6b-tensorslayer-q8_0.gguf` | 8-bit | ~650MB | High quality, smaller |
-| `qwen3-0.6b-tensorslayer-q5_k_m.gguf` | 5-bit K-quant | ~450MB | Balanced quality/size |
-| `qwen3-0.6b-tensorslayer-q4_0.gguf` | 4-bit | ~350MB | Fastest inference |
 ## Usage with Ollama
@@ -48,7 +48,7 @@ ollama run qwen3-enhanced "What is the relationship between 'semantic meaning' a
 ```python
 import requests
-# Using Ollama API (replace with your endpoint)
 OLLAMA_API = "2612b573cd924d148095de291b70bd98.MDGkuS-nd3Ms0a3tBQdpkk-Z"
 response = requests.post(f"{OLLAMA_API}/api/generate", json={
     "model": "qwen3-enhanced",

 | File | Quantization | Size | Use Case |
 |------|-------------|------|----------|
+| `qwen3-0.6b-tensorslayer-f16.gguf` | FP16 | ~1.1GB | Maximum quality |
+| `qwen3-0.6b-tensorslayer-q8_0.gguf` | 8-bit | ~610MB | High quality, smaller |
+| `qwen3-0.6b-tensorslayer-q5_k_m.gguf` | 5-bit K-quant | ~424MB | Balanced quality/size |
+| `qwen3-0.6b-tensorslayer-q4_0.gguf` | 4-bit | ~364MB | Fastest inference |
 ## Usage with Ollama
 ```python
 import requests
+# Using Ollama API
 OLLAMA_API = "2612b573cd924d148095de291b70bd98.MDGkuS-nd3Ms0a3tBQdpkk-Z"
 response = requests.post(f"{OLLAMA_API}/api/generate", json={
     "model": "qwen3-enhanced",

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