Instructions to use nerkyor/Lynn-V4-Pro-Distill-Qwen-35B-A3B-Q4_K_M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nerkyor/Lynn-V4-Pro-Distill-Qwen-35B-A3B-Q4_K_M with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="nerkyor/Lynn-V4-Pro-Distill-Qwen-35B-A3B-Q4_K_M",
	filename="Lynn-V4-Pro-Distill-Qwen-35B-A3B-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use nerkyor/Lynn-V4-Pro-Distill-Qwen-35B-A3B-Q4_K_M with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf nerkyor/Lynn-V4-Pro-Distill-Qwen-35B-A3B-Q4_K_M:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf nerkyor/Lynn-V4-Pro-Distill-Qwen-35B-A3B-Q4_K_M:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf nerkyor/Lynn-V4-Pro-Distill-Qwen-35B-A3B-Q4_K_M:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf nerkyor/Lynn-V4-Pro-Distill-Qwen-35B-A3B-Q4_K_M: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 nerkyor/Lynn-V4-Pro-Distill-Qwen-35B-A3B-Q4_K_M:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf nerkyor/Lynn-V4-Pro-Distill-Qwen-35B-A3B-Q4_K_M: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 nerkyor/Lynn-V4-Pro-Distill-Qwen-35B-A3B-Q4_K_M:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf nerkyor/Lynn-V4-Pro-Distill-Qwen-35B-A3B-Q4_K_M:Q4_K_M

Use Docker

docker model run hf.co/nerkyor/Lynn-V4-Pro-Distill-Qwen-35B-A3B-Q4_K_M:Q4_K_M

LM Studio
Jan

vLLM

How to use nerkyor/Lynn-V4-Pro-Distill-Qwen-35B-A3B-Q4_K_M with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nerkyor/Lynn-V4-Pro-Distill-Qwen-35B-A3B-Q4_K_M"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nerkyor/Lynn-V4-Pro-Distill-Qwen-35B-A3B-Q4_K_M",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/nerkyor/Lynn-V4-Pro-Distill-Qwen-35B-A3B-Q4_K_M:Q4_K_M

Ollama
How to use nerkyor/Lynn-V4-Pro-Distill-Qwen-35B-A3B-Q4_K_M with Ollama:
```
ollama run hf.co/nerkyor/Lynn-V4-Pro-Distill-Qwen-35B-A3B-Q4_K_M:Q4_K_M
```

Unsloth Studio

How to use nerkyor/Lynn-V4-Pro-Distill-Qwen-35B-A3B-Q4_K_M 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 nerkyor/Lynn-V4-Pro-Distill-Qwen-35B-A3B-Q4_K_M 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 nerkyor/Lynn-V4-Pro-Distill-Qwen-35B-A3B-Q4_K_M to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for nerkyor/Lynn-V4-Pro-Distill-Qwen-35B-A3B-Q4_K_M to start chatting

How to use nerkyor/Lynn-V4-Pro-Distill-Qwen-35B-A3B-Q4_K_M with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf nerkyor/Lynn-V4-Pro-Distill-Qwen-35B-A3B-Q4_K_M: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": "nerkyor/Lynn-V4-Pro-Distill-Qwen-35B-A3B-Q4_K_M:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use nerkyor/Lynn-V4-Pro-Distill-Qwen-35B-A3B-Q4_K_M with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf nerkyor/Lynn-V4-Pro-Distill-Qwen-35B-A3B-Q4_K_M: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 nerkyor/Lynn-V4-Pro-Distill-Qwen-35B-A3B-Q4_K_M:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use nerkyor/Lynn-V4-Pro-Distill-Qwen-35B-A3B-Q4_K_M with Docker Model Runner:
```
docker model run hf.co/nerkyor/Lynn-V4-Pro-Distill-Qwen-35B-A3B-Q4_K_M:Q4_K_M
```

Lemonade

How to use nerkyor/Lynn-V4-Pro-Distill-Qwen-35B-A3B-Q4_K_M with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull nerkyor/Lynn-V4-Pro-Distill-Qwen-35B-A3B-Q4_K_M:Q4_K_M

Run and chat with the model

lemonade run user.Lynn-V4-Pro-Distill-Qwen-35B-A3B-Q4_K_M-Q4_K_M

List all available models

lemonade list

Lynn-V4-Pro-Distill-Qwen-35B-A3B-Q4_K_M

File size: 1,742 Bytes

f5190d5

MIT License

Copyright (c) 2026 Lynn (MerkyorLynn / nerkyor)

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

---

Important: This MIT License applies to:
  - The LoRA adapter delta weights produced by Lynn fine-tuning.
  - Any Lynn-original training data, scripts, and documentation in this repo.

It does NOT supersede the upstream Apache 2.0 license of the Qwen3.6-35B-A3B
base model weights. The base weights remain under Apache 2.0; see NOTICE for
attribution preserved per Apache 2.0 Section 4.

It does NOT supersede the MIT license of the DeepSeek-V4-Pro / V4-Flash
teacher outputs used as distillation data. See NOTICE.

This dual-attribution structure follows the precedent of
DeepSeek-R1-Distill-Qwen-32B (deepseek-ai, MIT card + Apache 2.0 base NOTICE).