Instructions to use amkkk/Qwen3.5_2B_Finetune_Uncensor_GGUF_quantized with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use amkkk/Qwen3.5_2B_Finetune_Uncensor_GGUF_quantized with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="amkkk/Qwen3.5_2B_Finetune_Uncensor_GGUF_quantized",
	filename="mmproj-Qwen3.5-2B-F16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": [
				{
					"type": "text",
					"text": "Describe this image in one sentence."
				},
				{
					"type": "image_url",
					"image_url": {
						"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
					}
				}
			]
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use amkkk/Qwen3.5_2B_Finetune_Uncensor_GGUF_quantized with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf amkkk/Qwen3.5_2B_Finetune_Uncensor_GGUF_quantized:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf amkkk/Qwen3.5_2B_Finetune_Uncensor_GGUF_quantized:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf amkkk/Qwen3.5_2B_Finetune_Uncensor_GGUF_quantized:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf amkkk/Qwen3.5_2B_Finetune_Uncensor_GGUF_quantized: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 amkkk/Qwen3.5_2B_Finetune_Uncensor_GGUF_quantized:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf amkkk/Qwen3.5_2B_Finetune_Uncensor_GGUF_quantized: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 amkkk/Qwen3.5_2B_Finetune_Uncensor_GGUF_quantized:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf amkkk/Qwen3.5_2B_Finetune_Uncensor_GGUF_quantized:Q4_K_M

Use Docker

docker model run hf.co/amkkk/Qwen3.5_2B_Finetune_Uncensor_GGUF_quantized:Q4_K_M

LM Studio
Jan

vLLM

How to use amkkk/Qwen3.5_2B_Finetune_Uncensor_GGUF_quantized with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "amkkk/Qwen3.5_2B_Finetune_Uncensor_GGUF_quantized"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "amkkk/Qwen3.5_2B_Finetune_Uncensor_GGUF_quantized",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/amkkk/Qwen3.5_2B_Finetune_Uncensor_GGUF_quantized:Q4_K_M

Ollama
How to use amkkk/Qwen3.5_2B_Finetune_Uncensor_GGUF_quantized with Ollama:
```
ollama run hf.co/amkkk/Qwen3.5_2B_Finetune_Uncensor_GGUF_quantized:Q4_K_M
```

Unsloth Studio new

How to use amkkk/Qwen3.5_2B_Finetune_Uncensor_GGUF_quantized 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 amkkk/Qwen3.5_2B_Finetune_Uncensor_GGUF_quantized 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 amkkk/Qwen3.5_2B_Finetune_Uncensor_GGUF_quantized to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for amkkk/Qwen3.5_2B_Finetune_Uncensor_GGUF_quantized to start chatting

Pi new

How to use amkkk/Qwen3.5_2B_Finetune_Uncensor_GGUF_quantized with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf amkkk/Qwen3.5_2B_Finetune_Uncensor_GGUF_quantized: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": "amkkk/Qwen3.5_2B_Finetune_Uncensor_GGUF_quantized:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use amkkk/Qwen3.5_2B_Finetune_Uncensor_GGUF_quantized with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf amkkk/Qwen3.5_2B_Finetune_Uncensor_GGUF_quantized: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 amkkk/Qwen3.5_2B_Finetune_Uncensor_GGUF_quantized:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use amkkk/Qwen3.5_2B_Finetune_Uncensor_GGUF_quantized with Docker Model Runner:
```
docker model run hf.co/amkkk/Qwen3.5_2B_Finetune_Uncensor_GGUF_quantized:Q4_K_M
```

Lemonade

How to use amkkk/Qwen3.5_2B_Finetune_Uncensor_GGUF_quantized with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull amkkk/Qwen3.5_2B_Finetune_Uncensor_GGUF_quantized:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3.5_2B_Finetune_Uncensor_GGUF_quantized-Q4_K_M

List all available models

lemonade list

Qwen3.5_2B_Finetune_Uncensor_GGUF

GGUF companion release for Qwen3.5_2B_Finetune_Uncensor.

Files

qwen3.5_2b_finetune_uncensor.f16.gguf
qwen3.5_2b_finetune_uncensor.Q2_K.gguf
qwen3.5_2b_finetune_uncensor.Q3_K_S.gguf
qwen3.5_2b_finetune_uncensor.Q3_K_M.gguf
qwen3.5_2b_finetune_uncensor.Q3_K_L.gguf
qwen3.5_2b_finetune_uncensor.Q4_K_S.gguf
qwen3.5_2b_finetune_uncensor.Q4_K_M.gguf
qwen3.5_2b_finetune_uncensor.Q5_K_S.gguf
qwen3.5_2b_finetune_uncensor.Q5_K_M.gguf
qwen3.5_2b_finetune_uncensor.Q6_K.gguf
qwen3.5_2b_finetune_uncensor.Q8_0.gguf

Quant guide

Quant	Size	Use when	Tradeoff
`Q2_K`	`0.85 GB`	You need the smallest possible file and can tolerate a clear quality drop	Lowest memory use, weakest output quality
`Q3_K_S`	`0.95 GB`	You are below 8 GB RAM/VRAM and want a small step up from `Q2_K`	Still a noticeable quality hit
`Q3_K_M`	`1.02 GB`	You want the best low-end compromise for constrained devices	Better than `Q3_K_S`, still compressed hard
`Q3_K_L`	`1.06 GB`	You want a slightly safer `Q3` choice without moving into `Q4`	Marginally larger for a modest gain
`Q4_K_S`	`1.12 GB`	You want a compact everyday quant and are optimizing for size first	Good balance, a bit weaker than `Q4_K_M`
`Q4_K_M`	`1.18 GB`	You want the standard balanced option for general local use	Best default size/quality compromise for many setups
`Q5_K_S`	`1.28 GB`	You have more headroom and want to preserve quality better than `Q4`	Larger file for a smaller quality jump
`Q5_K_M`	`1.33 GB`	You want a strong general-purpose quant without going near full precision	Best practical choice if RAM/VRAM is not very tight
`Q6_K`	`1.45 GB`	You want near-high-quality local inference and can afford the extra memory	Larger and slower than `Q5_K_M`
`Q8_0`	`1.87 GB`	You want to stay as close as possible to `f16` while still using GGUF quantization	Highest quality quant here, but much heavier
`f16`	`3.52 GB`	You want the least quantization loss and have plenty of memory/storage	Largest file by a wide margin

Local usage

local.bat

To run a specific quant without editing the script:

set MODEL=qwen3.5_2b_finetune_uncensor.Q5_K_M.gguf
local.bat

Notes

Source HF checkpoint: Qwen3.5_2B_Finetune_Uncensor

Downloads last month: 327

GGUF

Model size

2B params

Architecture

qwen35

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit