Instructions to use Melvin56/Qwen3-1.7B-abliterated-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Melvin56/Qwen3-1.7B-abliterated-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Melvin56/Qwen3-1.7B-abliterated-GGUF",
	filename="qwen3-1.7b-abliterated-BF16.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Melvin56/Qwen3-1.7B-abliterated-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Melvin56/Qwen3-1.7B-abliterated-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Melvin56/Qwen3-1.7B-abliterated-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 Melvin56/Qwen3-1.7B-abliterated-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Melvin56/Qwen3-1.7B-abliterated-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 Melvin56/Qwen3-1.7B-abliterated-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Melvin56/Qwen3-1.7B-abliterated-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 Melvin56/Qwen3-1.7B-abliterated-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Melvin56/Qwen3-1.7B-abliterated-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Melvin56/Qwen3-1.7B-abliterated-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use Melvin56/Qwen3-1.7B-abliterated-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Melvin56/Qwen3-1.7B-abliterated-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Melvin56/Qwen3-1.7B-abliterated-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Melvin56/Qwen3-1.7B-abliterated-GGUF:Q4_K_M

Ollama
How to use Melvin56/Qwen3-1.7B-abliterated-GGUF with Ollama:
```
ollama run hf.co/Melvin56/Qwen3-1.7B-abliterated-GGUF:Q4_K_M
```

Unsloth Studio

How to use Melvin56/Qwen3-1.7B-abliterated-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 Melvin56/Qwen3-1.7B-abliterated-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 Melvin56/Qwen3-1.7B-abliterated-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Melvin56/Qwen3-1.7B-abliterated-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use Melvin56/Qwen3-1.7B-abliterated-GGUF with Docker Model Runner:
```
docker model run hf.co/Melvin56/Qwen3-1.7B-abliterated-GGUF:Q4_K_M
```

Lemonade

How to use Melvin56/Qwen3-1.7B-abliterated-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Melvin56/Qwen3-1.7B-abliterated-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3-1.7B-abliterated-GGUF-Q4_K_M

List all available models

lemonade list

Melvin56/Qwen3-1.7B-abliterated-GGUF

Original Model : huihui-ai/Qwen3-1.7B-abliterated

Llama.cpp build: 0208355 (5342)

I used imatrix to create all these quants using this Dataset.

	CPU (AVX2)	CPU (ARM NEON)	Metal	cuBLAS	rocBLAS	SYCL	CLBlast	Vulkan	Kompute
K-quants	✅	✅	✅	✅	✅	✅	✅ 🐢5	✅ 🐢5	❌
I-quants	✅ 🐢4	✅ 🐢4	✅ 🐢4	✅	✅	Partial¹	❌	❌	❌

✅: feature works
🚫: feature does not work
❓: unknown, please contribute if you can test it youself
🐢: feature is slow
¹: IQ3_S and IQ1_S, see #5886
²: Only with -ngl 0
³: Inference is 50% slower
⁴: Slower than K-quants of comparable size
⁵: Slower than cuBLAS/rocBLAS on similar cards
⁶: Only q8_0 and iq4_nl