Instructions to use bottle3695/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled-Q6_K-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use bottle3695/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled-Q6_K-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="bottle3695/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled-Q6_K-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("bottle3695/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled-Q6_K-GGUF", dtype="auto")

llama-cpp-python

How to use bottle3695/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled-Q6_K-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="bottle3695/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled-Q6_K-GGUF",
	filename="qwen3.6-35b-a3b-kimi-k2.6-reasoning-distilled-q6_k.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 bottle3695/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled-Q6_K-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf bottle3695/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled-Q6_K-GGUF:Q6_K
# Run inference directly in the terminal:
llama-cli -hf bottle3695/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled-Q6_K-GGUF:Q6_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf bottle3695/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled-Q6_K-GGUF:Q6_K
# Run inference directly in the terminal:
llama-cli -hf bottle3695/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled-Q6_K-GGUF:Q6_K

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 bottle3695/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled-Q6_K-GGUF:Q6_K
# Run inference directly in the terminal:
./llama-cli -hf bottle3695/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled-Q6_K-GGUF:Q6_K

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 bottle3695/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled-Q6_K-GGUF:Q6_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf bottle3695/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled-Q6_K-GGUF:Q6_K

Use Docker

docker model run hf.co/bottle3695/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled-Q6_K-GGUF:Q6_K

LM Studio
Jan

vLLM

How to use bottle3695/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled-Q6_K-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bottle3695/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled-Q6_K-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bottle3695/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled-Q6_K-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/bottle3695/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled-Q6_K-GGUF:Q6_K

SGLang

How to use bottle3695/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled-Q6_K-GGUF 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 "bottle3695/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled-Q6_K-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bottle3695/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled-Q6_K-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "bottle3695/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled-Q6_K-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bottle3695/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled-Q6_K-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use bottle3695/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled-Q6_K-GGUF with Ollama:
```
ollama run hf.co/bottle3695/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled-Q6_K-GGUF:Q6_K
```

Unsloth Studio

How to use bottle3695/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled-Q6_K-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 bottle3695/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled-Q6_K-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 bottle3695/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled-Q6_K-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for bottle3695/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled-Q6_K-GGUF to start chatting

How to use bottle3695/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled-Q6_K-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf bottle3695/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled-Q6_K-GGUF:Q6_K

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": "bottle3695/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled-Q6_K-GGUF:Q6_K"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use bottle3695/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled-Q6_K-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 bottle3695/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled-Q6_K-GGUF:Q6_K

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 bottle3695/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled-Q6_K-GGUF:Q6_K

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use bottle3695/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled-Q6_K-GGUF with Docker Model Runner:
```
docker model run hf.co/bottle3695/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled-Q6_K-GGUF:Q6_K
```

Lemonade

How to use bottle3695/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled-Q6_K-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull bottle3695/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled-Q6_K-GGUF:Q6_K

Run and chat with the model

lemonade run user.Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled-Q6_K-GGUF-Q6_K

List all available models

lemonade list

Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled-Q6_K-GGUF / README.md

bottle3695

Upload README.md with huggingface_hub

1ac4f9f verified about 2 months ago

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	---
	license: apache-2.0
	language:
	- en
	library_name: transformers
	pipeline_tag: text-generation
	base_model: lordx64/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled
	datasets:
	- lordx64/reasoning-distill-kimi-k2-6-max-sft
	tags:
	- text-generation
	- reasoning
	- distillation
	- chain-of-thought
	- qwen
	- qwen3.6
	- kimi
	- kimi-k2
	- mixture-of-experts
	- moe
	- lora
	- unsloth
	- llama-cpp
	- gguf-my-repo
	model-index:
	- name: Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled
	results: []
	---

	# bottle3695/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled-Q6_K-GGUF
	This model was converted to GGUF format from [`lordx64/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled`](https://huggingface.co/lordx64/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
	Refer to the [original model card](https://huggingface.co/lordx64/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled) for more details on the model.

	## Use with llama.cpp
	Install llama.cpp through brew (works on Mac and Linux)

	```bash
	brew install llama.cpp

	```
	Invoke the llama.cpp server or the CLI.

	### CLI:
	```bash
	llama-cli --hf-repo bottle3695/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled-Q6_K-GGUF --hf-file qwen3.6-35b-a3b-kimi-k2.6-reasoning-distilled-q6_k.gguf -p "The meaning to life and the universe is"
	```

	### Server:
	```bash
	llama-server --hf-repo bottle3695/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled-Q6_K-GGUF --hf-file qwen3.6-35b-a3b-kimi-k2.6-reasoning-distilled-q6_k.gguf -c 2048
	```

	Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.

	Step 1: Clone llama.cpp from GitHub.
	```
	git clone https://github.com/ggerganov/llama.cpp
	```

	Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
	```
	cd llama.cpp && LLAMA_CURL=1 make
	```

	Step 3: Run inference through the main binary.
	```
	./llama-cli --hf-repo bottle3695/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled-Q6_K-GGUF --hf-file qwen3.6-35b-a3b-kimi-k2.6-reasoning-distilled-q6_k.gguf -p "The meaning to life and the universe is"
	```
	or
	```
	./llama-server --hf-repo bottle3695/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled-Q6_K-GGUF --hf-file qwen3.6-35b-a3b-kimi-k2.6-reasoning-distilled-q6_k.gguf -c 2048
	```