Instructions to use danielcherubini/Qwen3.5-DeltaCoder-9B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use danielcherubini/Qwen3.5-DeltaCoder-9B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="danielcherubini/Qwen3.5-DeltaCoder-9B-GGUF",
	filename="DeltaCoder-9B-v1.1-DPO-BF16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use danielcherubini/Qwen3.5-DeltaCoder-9B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf danielcherubini/Qwen3.5-DeltaCoder-9B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf danielcherubini/Qwen3.5-DeltaCoder-9B-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 danielcherubini/Qwen3.5-DeltaCoder-9B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf danielcherubini/Qwen3.5-DeltaCoder-9B-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 danielcherubini/Qwen3.5-DeltaCoder-9B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf danielcherubini/Qwen3.5-DeltaCoder-9B-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 danielcherubini/Qwen3.5-DeltaCoder-9B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf danielcherubini/Qwen3.5-DeltaCoder-9B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/danielcherubini/Qwen3.5-DeltaCoder-9B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use danielcherubini/Qwen3.5-DeltaCoder-9B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "danielcherubini/Qwen3.5-DeltaCoder-9B-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": "danielcherubini/Qwen3.5-DeltaCoder-9B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/danielcherubini/Qwen3.5-DeltaCoder-9B-GGUF:Q4_K_M

Ollama
How to use danielcherubini/Qwen3.5-DeltaCoder-9B-GGUF with Ollama:
```
ollama run hf.co/danielcherubini/Qwen3.5-DeltaCoder-9B-GGUF:Q4_K_M
```

Unsloth Studio new

How to use danielcherubini/Qwen3.5-DeltaCoder-9B-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 danielcherubini/Qwen3.5-DeltaCoder-9B-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 danielcherubini/Qwen3.5-DeltaCoder-9B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for danielcherubini/Qwen3.5-DeltaCoder-9B-GGUF to start chatting

Pi new

How to use danielcherubini/Qwen3.5-DeltaCoder-9B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf danielcherubini/Qwen3.5-DeltaCoder-9B-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": "danielcherubini/Qwen3.5-DeltaCoder-9B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use danielcherubini/Qwen3.5-DeltaCoder-9B-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 danielcherubini/Qwen3.5-DeltaCoder-9B-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 danielcherubini/Qwen3.5-DeltaCoder-9B-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use danielcherubini/Qwen3.5-DeltaCoder-9B-GGUF with Docker Model Runner:
```
docker model run hf.co/danielcherubini/Qwen3.5-DeltaCoder-9B-GGUF:Q4_K_M
```

Lemonade

How to use danielcherubini/Qwen3.5-DeltaCoder-9B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull danielcherubini/Qwen3.5-DeltaCoder-9B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3.5-DeltaCoder-9B-GGUF-Q4_K_M

List all available models

lemonade list

Qwen3.5-DeltaCoder-9B-GGUF

v1.1-DPO — Now with DPO alignment for improved code correctness and self-verification. If you downloaded before March 28, 2026, please re-pull to get v1.1-DPO.

GGUF quantizations of Qwen3.5-DeltaCoder-9B for use with llama.cpp, Ollama, LM Studio, and other GGUF-compatible inference engines.

What's New in v1.1-DPO

DPO alignment on 4,519 preference pairs from AceCode-V2-122K
Self-correcting behavior — model now detects and fixes its own bugs rather than submitting incorrect code
Improved code correctness — trained to prefer passing solutions over failing ones
Same tool-call reliability as v1 — SFT improvements preserved through two-stage merge

Available Quantizations

File	Quant	Size	Notes
`DeltaCoder-9B-v1.1-DPO-Q2_K.gguf`	Q2_K	~3.6 GB	Smallest, lowest quality
`DeltaCoder-9B-v1.1-DPO-Q3_K_S.gguf`	Q3_K_S	~4.0 GB
`DeltaCoder-9B-v1.1-DPO-Q3_K_M.gguf`	Q3_K_M	~4.4 GB
`DeltaCoder-9B-v1.1-DPO-Q3_K_L.gguf`	Q3_K_L	~4.6 GB
`DeltaCoder-9B-v1.1-DPO-Q4_0.gguf`	Q4_0	~3.2 GB
`DeltaCoder-9B-v1.1-DPO-Q4_K_S.gguf`	Q4_K_S	~5.0 GB
`DeltaCoder-9B-v1.1-DPO-Q4_K_M.gguf`	Q4_K_M	~5.5 GB	Recommended
`DeltaCoder-9B-v1.1-DPO-Q5_K_S.gguf`	Q5_K_S	~6.1 GB
`DeltaCoder-9B-v1.1-DPO-Q5_0.gguf`	Q5_0	~6.1 GB
`DeltaCoder-9B-v1.1-DPO-Q5_K_M.gguf`	Q5_K_M	~6.5 GB
`DeltaCoder-9B-v1.1-DPO-Q6_K.gguf`	Q6_K	~7.3 GB
`DeltaCoder-9B-v1.1-DPO-Q8_0.gguf`	Q8_0	~9.4 GB	Near-lossless
`DeltaCoder-9B-v1.1-DPO-BF16.gguf`	BF16	~17.9 GB	Full precision

Recommended Quant

Low VRAM (8GB): Q4_K_M
Mid VRAM (12GB): Q5_K_M or Q6_K
High VRAM (16GB+): Q8_0
Full precision: BF16

Training Lineage

Qwen/Qwen3.5-9B-Base
 └─ Qwen/Qwen3.5-9B  (instruction tuned)
     └─ Jackrong/Qwen3.5-9B-Claude-4.6-Opus-Reasoning-Distilled-v2
         (SFT on Claude 4.6 Opus reasoning traces)
         └─ danielcherubini/Qwen3.5-DeltaCoder-9B  (v1 SFT — tool-call reliability)
             (LoRA SFT on CoderForge-Preview)
             └─ danielcherubini/Qwen3.5-DeltaCoder-9B v1.1-DPO  ← this model
                 (DPO on AceCode-V2-122K preference pairs)

Recommended Sampling Settings

Parameter	Value
temperature	0.6
top_k	20
top_p	0.95
min_p	0.0
presence_penalty	0.0
repeat_penalty	1.0

Do not use temperature below 0.5 — low temperatures cause deterministic looping in multi-turn agentic use.

KV Cache Quantization

Context Length	KV Cache	VRAM (Q4_K_M)	Generation Speed
102,400	f16/q4_0	~8.5 GB	~111 tok/s
131,072	f16/q4_0	~9.1 GB	~110 tok/s

# llama.cpp / ik_llama.cpp flags
-ctk f16 -ctv q4_0

Usage

Ollama

ollama create deltacoder -f Modelfile

Example Modelfile:

FROM ./DeltaCoder-9B-v1.1-DPO-Q5_K_M.gguf

llama.cpp

./llama-server -m DeltaCoder-9B-v1.1-DPO-Q5_K_M.gguf -ngl 999 -c 131072 -ctk f16 -ctv q4_0 -fa 1 --jinja

LM Studio

Download any GGUF file and load it directly in LM Studio.

Benchmarks

Model	HumanEval	HumanEval+	Terminal-Bench Easy
Jackrong Qwen3.5-9B-v2 (base)	53.7%	—	—
DeltaCoder-9B v1 (temp=0.6)	50.6%	49.4%	2/4 (50%)
DeltaCoder-9B v1.1-DPO (temp=0.6)	TBD	TBD	2/4 (50%)*

*v1.1-DPO timed out on 2 tasks that v1 answered incorrectly — behavioral improvement confirmed, running with extended timeout.

Acknowledgements

Unsloth for Qwen3.5 training support
Together AI for the CoderForge dataset
TIGER Lab for AceCode-V2-122K
Jackrong for the reasoning distillation
Qwen for the base model

Downloads last month: 1,643

GGUF

Model size

9B params

Architecture

qwen35

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Model tree for danielcherubini/Qwen3.5-DeltaCoder-9B-GGUF

Base model

Qwen/Qwen3.5-9B-Base

Finetuned

Qwen/Qwen3.5-9B

Adapter

danielcherubini/Qwen3.5-DeltaCoder-9B

Quantized

(1)

this model

danielcherubini
/

Qwen3.5-DeltaCoder-9B-GGUF

Qwen3.5-DeltaCoder-9B-GGUF

What's New in v1.1-DPO

Available Quantizations

Recommended Quant

Training Lineage

Recommended Sampling Settings

KV Cache Quantization

Usage

Ollama

llama.cpp

LM Studio

Benchmarks

Acknowledgements

Model tree for danielcherubini/Qwen3.5-DeltaCoder-9B-GGUF

Datasets used to train danielcherubini/Qwen3.5-DeltaCoder-9B-GGUF