Instructions to use FINAL-Bench/Darwin-28B-Coder-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use FINAL-Bench/Darwin-28B-Coder-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="FINAL-Bench/Darwin-28B-Coder-GGUF",
	filename="Darwin-28B-Coder-F16.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 FINAL-Bench/Darwin-28B-Coder-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf FINAL-Bench/Darwin-28B-Coder-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf FINAL-Bench/Darwin-28B-Coder-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 FINAL-Bench/Darwin-28B-Coder-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf FINAL-Bench/Darwin-28B-Coder-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 FINAL-Bench/Darwin-28B-Coder-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf FINAL-Bench/Darwin-28B-Coder-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 FINAL-Bench/Darwin-28B-Coder-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf FINAL-Bench/Darwin-28B-Coder-GGUF:Q4_K_M

Use Docker

docker model run hf.co/FINAL-Bench/Darwin-28B-Coder-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use FINAL-Bench/Darwin-28B-Coder-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "FINAL-Bench/Darwin-28B-Coder-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": "FINAL-Bench/Darwin-28B-Coder-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/FINAL-Bench/Darwin-28B-Coder-GGUF:Q4_K_M

Ollama
How to use FINAL-Bench/Darwin-28B-Coder-GGUF with Ollama:
```
ollama run hf.co/FINAL-Bench/Darwin-28B-Coder-GGUF:Q4_K_M
```

Unsloth Studio

How to use FINAL-Bench/Darwin-28B-Coder-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 FINAL-Bench/Darwin-28B-Coder-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 FINAL-Bench/Darwin-28B-Coder-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for FINAL-Bench/Darwin-28B-Coder-GGUF to start chatting

How to use FINAL-Bench/Darwin-28B-Coder-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf FINAL-Bench/Darwin-28B-Coder-GGUF: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": "FINAL-Bench/Darwin-28B-Coder-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use FINAL-Bench/Darwin-28B-Coder-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 FINAL-Bench/Darwin-28B-Coder-GGUF: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 FINAL-Bench/Darwin-28B-Coder-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use FINAL-Bench/Darwin-28B-Coder-GGUF with Docker Model Runner:
```
docker model run hf.co/FINAL-Bench/Darwin-28B-Coder-GGUF:Q4_K_M
```

Lemonade

How to use FINAL-Bench/Darwin-28B-Coder-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull FINAL-Bench/Darwin-28B-Coder-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Darwin-28B-Coder-GGUF-Q4_K_M

List all available models

lemonade list

Darwin-28B-Coder-GGUF / README.md

SeaWolf-AI

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	---
	license: apache-2.0
	base_model: FINAL-Bench/Darwin-28B-Coder
	base_model_relation: quantized
	library_name: gguf
	pipeline_tag: text-generation
	tags:
	- gguf
	- llama.cpp
	- mtp
	- multi-token-prediction
	- speculative-decoding
	- code
	language:
	- en
	- ko
	---

	# Darwin-28B-Coder — GGUF (MTP-enabled)

	GGUF builds of [FINAL-Bench/Darwin-28B-Coder](https://huggingface.co/FINAL-Bench/Darwin-28B-Coder) with the native Multi-Token Prediction (MTP) head preserved, for self-speculative decoding in llama.cpp.

	Requested in the [base model discussion](https://huggingface.co/FINAL-Bench/Darwin-28B-Coder/discussions/1).

	## Files

	\| File \| Quant \| Size \| Notes \|
	\|------\|-------\|------\|-------\|
	\| `Darwin-28B-Coder-Q4_K_M.gguf` \| Q4_K_M \| 16.8 GB \| recommended for most GPUs \|
	\| `Darwin-28B-Coder-Q8_0.gguf` \| Q8_0 \| 29.0 GB \| near-lossless \|
	\| `Darwin-28B-Coder-F16.gguf` \| F16 \| 54.7 GB \| full precision \|

	All files include the MTP layer — verified in metadata:
	`general.architecture = qwen35`, `qwen35.nextn_predict_layers = 1`, tensors `blk.64.nextn.*`.

	## Multi-Token Prediction (MTP)

	This model ships with a trained MTP head (1 prediction layer). With a recent llama.cpp build that includes MTP support (merged in [PR #22673](https://github.com/ggml-org/llama.cpp/pull/22673)), the `nextn` layer is used for self-speculative decoding — typically ~1.5–2× faster generation with identical output (the main model verifies every drafted token, so quality is unchanged).

	> A standard (non-MTP) GGUF does not contain the prediction head — you need these MTP-enabled files to benefit from the speedup.

	## Usage

	```bash
	# 1) Build a recent llama.cpp (MTP support is in mainline since PR #22673)
	git clone https://github.com/ggml-org/llama.cpp && cd llama.cpp
	cmake -B build -DGGML_CUDA=ON && cmake --build build -j --config Release

	# 2) Run — the nextn (MTP) layer enables self-speculative decoding
	./build/bin/llama-cli \
	-m Darwin-28B-Coder-Q4_K_M.gguf \
	-ngl 99 -c 8192 \
	-p "Write a quicksort in Python."
	```

	For the exact MTP/speculative flags and the latest behaviour, see the llama.cpp MTP documentation / PR #22673. Works with `llama-cli` and `llama-server`.

	## Model spec (public)

	\| \| \|
	\|---\|---\|
	\| Architecture \| `qwen35` (hybrid attention) \|
	\| Layers \| 64 + 1 MTP \|
	\| Hidden size \| 5120 \|
	\| Attention heads \| 24 (KV 4) \|
	\| Context length \| 262,144 \|
	\| Vocab \| 248,320 \|
	\| Precision (source) \| bfloat16 \|

	## License & attribution

	License and usage follow the base model [FINAL-Bench/Darwin-28B-Coder](https://huggingface.co/FINAL-Bench/Darwin-28B-Coder). These are GGUF conversions only; refer to the base model card for model details, intended use, and limitations.

	GGUF conversion + quantization by the FINAL-Bench team using `llama.cpp/convert_hf_to_gguf.py`.