Instructions to use TeichAI/Qwen3.6-27B-Claude-Opus-Reasoning-Distill-v2-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TeichAI/Qwen3.6-27B-Claude-Opus-Reasoning-Distill-v2-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="TeichAI/Qwen3.6-27B-Claude-Opus-Reasoning-Distill-v2-GGUF",
	filename="Qwen3.6-27B-Claude-Opus-Reasoning-Distill.bf16.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use TeichAI/Qwen3.6-27B-Claude-Opus-Reasoning-Distill-v2-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf TeichAI/Qwen3.6-27B-Claude-Opus-Reasoning-Distill-v2-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf TeichAI/Qwen3.6-27B-Claude-Opus-Reasoning-Distill-v2-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf TeichAI/Qwen3.6-27B-Claude-Opus-Reasoning-Distill-v2-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf TeichAI/Qwen3.6-27B-Claude-Opus-Reasoning-Distill-v2-GGUF:Q4_K_M

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 TeichAI/Qwen3.6-27B-Claude-Opus-Reasoning-Distill-v2-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf TeichAI/Qwen3.6-27B-Claude-Opus-Reasoning-Distill-v2-GGUF:Q4_K_M

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 TeichAI/Qwen3.6-27B-Claude-Opus-Reasoning-Distill-v2-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf TeichAI/Qwen3.6-27B-Claude-Opus-Reasoning-Distill-v2-GGUF:Q4_K_M

Use Docker

docker model run hf.co/TeichAI/Qwen3.6-27B-Claude-Opus-Reasoning-Distill-v2-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use TeichAI/Qwen3.6-27B-Claude-Opus-Reasoning-Distill-v2-GGUF with Ollama:
```
ollama run hf.co/TeichAI/Qwen3.6-27B-Claude-Opus-Reasoning-Distill-v2-GGUF:Q4_K_M
```

Unsloth Studio

How to use TeichAI/Qwen3.6-27B-Claude-Opus-Reasoning-Distill-v2-GGUF 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 TeichAI/Qwen3.6-27B-Claude-Opus-Reasoning-Distill-v2-GGUF 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 TeichAI/Qwen3.6-27B-Claude-Opus-Reasoning-Distill-v2-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for TeichAI/Qwen3.6-27B-Claude-Opus-Reasoning-Distill-v2-GGUF to start chatting

How to use TeichAI/Qwen3.6-27B-Claude-Opus-Reasoning-Distill-v2-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf TeichAI/Qwen3.6-27B-Claude-Opus-Reasoning-Distill-v2-GGUF:Q4_K_M

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": "TeichAI/Qwen3.6-27B-Claude-Opus-Reasoning-Distill-v2-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use TeichAI/Qwen3.6-27B-Claude-Opus-Reasoning-Distill-v2-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf TeichAI/Qwen3.6-27B-Claude-Opus-Reasoning-Distill-v2-GGUF:Q4_K_M

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 TeichAI/Qwen3.6-27B-Claude-Opus-Reasoning-Distill-v2-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use TeichAI/Qwen3.6-27B-Claude-Opus-Reasoning-Distill-v2-GGUF with Docker Model Runner:
```
docker model run hf.co/TeichAI/Qwen3.6-27B-Claude-Opus-Reasoning-Distill-v2-GGUF:Q4_K_M
```

Lemonade

How to use TeichAI/Qwen3.6-27B-Claude-Opus-Reasoning-Distill-v2-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull TeichAI/Qwen3.6-27B-Claude-Opus-Reasoning-Distill-v2-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3.6-27B-Claude-Opus-Reasoning-Distill-v2-GGUF-Q4_K_M

List all available models

lemonade list

armand0e commited on Apr 27

Commit

17c599a

verified ·

1 Parent(s): 8aca6d9

Update README.md

Browse files

Files changed (1) hide show

README.md +8 -4

README.md CHANGED Viewed

@@ -18,12 +18,16 @@ datasets:
 ![alt_text](Benchmark-Comparison.png)
 ```
-                                                      arc     arc/e   boolq
-                                    Qwen/Qwen3.6-27B	0.637	0.798	0.911
-TeichAI/Qwen3.6-27B-Claude-Opus-Reasoning-Distill-v2	0.665	0.838	0.911
 ```
-Provided by [@nightmedia](https://huggingface.co/nightmedia). The remaining benchmarks are still in queue and will be updated when avaliable.
 ## 🧬 Datasets:
   - [TeichAI/Claude-Opus-4.6-Reasoning-887x](https://huggingface.co/datasets/TeichAI/Claude-Opus-4.6-Reasoning-887x)

 ![alt_text](Benchmark-Comparison.png)
 ```
+Qwen3.6-27B-Claude-Opus-Reasoning-Distill-v2
+         arc   arc/e boolq hswag obkqa piqa  wino
+mxfp8    0.665,0.831,0.910,0.790,0.456,0.813,0.772
+Qwen3.6-27B
+         arc   arc/e boolq hswag obkqa piqa  wino
+mxfp8    0.647,0.803,0.910,0.773,0.450,0.806,0.742
 ```
+Provided by [@nightmedia](https://huggingface.co/nightmedia). All benchmarks were done in mxfp8 precision
 ## 🧬 Datasets:
   - [TeichAI/Claude-Opus-4.6-Reasoning-887x](https://huggingface.co/datasets/TeichAI/Claude-Opus-4.6-Reasoning-887x)