Instructions to use h4rm0n1c/qwen3.5-24b-a10b-IQ4_NL-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use h4rm0n1c/qwen3.5-24b-a10b-IQ4_NL-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf h4rm0n1c/qwen3.5-24b-a10b-IQ4_NL-GGUF:IQ4_NL
# Run inference directly in the terminal:
llama cli -hf h4rm0n1c/qwen3.5-24b-a10b-IQ4_NL-GGUF:IQ4_NL

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf h4rm0n1c/qwen3.5-24b-a10b-IQ4_NL-GGUF:IQ4_NL
# Run inference directly in the terminal:
llama cli -hf h4rm0n1c/qwen3.5-24b-a10b-IQ4_NL-GGUF:IQ4_NL

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 h4rm0n1c/qwen3.5-24b-a10b-IQ4_NL-GGUF:IQ4_NL
# Run inference directly in the terminal:
./llama-cli -hf h4rm0n1c/qwen3.5-24b-a10b-IQ4_NL-GGUF:IQ4_NL

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 h4rm0n1c/qwen3.5-24b-a10b-IQ4_NL-GGUF:IQ4_NL
# Run inference directly in the terminal:
./build/bin/llama-cli -hf h4rm0n1c/qwen3.5-24b-a10b-IQ4_NL-GGUF:IQ4_NL

Use Docker

docker model run hf.co/h4rm0n1c/qwen3.5-24b-a10b-IQ4_NL-GGUF:IQ4_NL

LM Studio
Jan
Ollama
How to use h4rm0n1c/qwen3.5-24b-a10b-IQ4_NL-GGUF with Ollama:
```
ollama run hf.co/h4rm0n1c/qwen3.5-24b-a10b-IQ4_NL-GGUF:IQ4_NL
```

Unsloth Studio

How to use h4rm0n1c/qwen3.5-24b-a10b-IQ4_NL-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 h4rm0n1c/qwen3.5-24b-a10b-IQ4_NL-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 h4rm0n1c/qwen3.5-24b-a10b-IQ4_NL-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for h4rm0n1c/qwen3.5-24b-a10b-IQ4_NL-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use h4rm0n1c/qwen3.5-24b-a10b-IQ4_NL-GGUF with Docker Model Runner:
```
docker model run hf.co/h4rm0n1c/qwen3.5-24b-a10b-IQ4_NL-GGUF:IQ4_NL
```

Lemonade

How to use h4rm0n1c/qwen3.5-24b-a10b-IQ4_NL-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull h4rm0n1c/qwen3.5-24b-a10b-IQ4_NL-GGUF:IQ4_NL

Run and chat with the model

lemonade run user.qwen3.5-24b-a10b-IQ4_NL-GGUF-IQ4_NL

List all available models

lemonade list

qwen3.5b-24b-a10b IQ4_NL GGUF

Quanter's Note: Sibling of sandeshrajx/Qwen3.5-24B-A3B-REAP-0.32, this one has been passed over completely for reasoning distills, I'd be interested to see what difference 7b more active experts can make.

GGUF quantization of sandeshrajx/qwen3.5b-24b-a10b — a 24B-parameter MoE model with 10B active parameters per token. Architecture is Qwen3.5 MoE.

Source: sandeshrajx/qwen3.5b-24b-a10b
Converted by: QuantZhai benchmark pipeline
Quantization: IQ4_NL (importance-matrix 4-bit non-linear)

Model Details

Property	Value
Architecture	Qwen3.5 MoE (Dense + Mamba-2 SSM interleaved)
Parameters	24B total, 10B active per token
Experts	39, 8 active per token
Context length	262144 (256K)
Hidden size	3072
Attention heads	32, KV heads = 2
Head dim	256
RoPE	MRope (multimodal), theta = 10,000,000
SSM	Mamba-2 inspired conv/state-space per 4th layer
Quantization	IQ4_NL (4.50 bpw)
File size	13.9 GB
Tokenizer	Qwen2 (GPT-2 based BPE, vocab 248,320)

Benchmarks

Hardware: dual-GPU (RTX 3080 10GB + V100-SXM2 32GB, 42 GB total)
Engine: llama.cpp with TurboQuant KV (q8_0 K / turbo3 V)
Perplexity: macvox68 code corpus, ctx=4096, stride=512

Metric	Cold	Warm
PPL	8.0217	3.9654
TPS	26.1 tok/s	31.4 tok/s
TTFT	1919 ms	1594 ms

QuantZhai Ranking

Rank #30 of 46 — combined score 50.5 (equal-weight: TPS, PPL, convergence).

Usage

llama-cli -m qwen3.5b-24b-a10b-IQ4_NL.gguf \
  -p "Write a mergesort in Python" \
  -n 1024 -t 12 --temp 0.6 --top-p 0.95

llama-server -m qwen3.5b-24b-a10b-IQ4_NL.gguf \
  --host 0.0.0.0 --port 8080 -ngl 99 -t 12 \
  --cache-type-k q8_0 --cache-type-v turbo3

Recommended: temp 0.6, top-p 0.95, context up to 256K.

License

MIT (this quantization).
Source model by sandeshrajx — review its license separately.

Downloads last month: 94

GGUF

Model size

24B params

Architecture

qwen35moe

Hardware compatibility

4-bit

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for h4rm0n1c/qwen3.5-24b-a10b-IQ4_NL-GGUF

Base model

sandeshrajx/qwen3.5b-24b-a10b

Quantized

(1)

this model