Instructions to use GauravGosain/Qwopus3.5-9B-Coder-DFlash-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use GauravGosain/Qwopus3.5-9B-Coder-DFlash-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="GauravGosain/Qwopus3.5-9B-Coder-DFlash-GGUF",
	filename="Qwopus3.5-9B-Coder-DFlash-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use GauravGosain/Qwopus3.5-9B-Coder-DFlash-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 GauravGosain/Qwopus3.5-9B-Coder-DFlash-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf GauravGosain/Qwopus3.5-9B-Coder-DFlash-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf GauravGosain/Qwopus3.5-9B-Coder-DFlash-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf GauravGosain/Qwopus3.5-9B-Coder-DFlash-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 GauravGosain/Qwopus3.5-9B-Coder-DFlash-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf GauravGosain/Qwopus3.5-9B-Coder-DFlash-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 GauravGosain/Qwopus3.5-9B-Coder-DFlash-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf GauravGosain/Qwopus3.5-9B-Coder-DFlash-GGUF:Q4_K_M

Use Docker

docker model run hf.co/GauravGosain/Qwopus3.5-9B-Coder-DFlash-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use GauravGosain/Qwopus3.5-9B-Coder-DFlash-GGUF with Ollama:
```
ollama run hf.co/GauravGosain/Qwopus3.5-9B-Coder-DFlash-GGUF:Q4_K_M
```

Unsloth Studio

How to use GauravGosain/Qwopus3.5-9B-Coder-DFlash-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 GauravGosain/Qwopus3.5-9B-Coder-DFlash-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 GauravGosain/Qwopus3.5-9B-Coder-DFlash-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for GauravGosain/Qwopus3.5-9B-Coder-DFlash-GGUF to start chatting

How to use GauravGosain/Qwopus3.5-9B-Coder-DFlash-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf GauravGosain/Qwopus3.5-9B-Coder-DFlash-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": "GauravGosain/Qwopus3.5-9B-Coder-DFlash-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use GauravGosain/Qwopus3.5-9B-Coder-DFlash-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf GauravGosain/Qwopus3.5-9B-Coder-DFlash-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 GauravGosain/Qwopus3.5-9B-Coder-DFlash-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use GauravGosain/Qwopus3.5-9B-Coder-DFlash-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf GauravGosain/Qwopus3.5-9B-Coder-DFlash-GGUF:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "GauravGosain/Qwopus3.5-9B-Coder-DFlash-GGUF:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use GauravGosain/Qwopus3.5-9B-Coder-DFlash-GGUF with Docker Model Runner:
```
docker model run hf.co/GauravGosain/Qwopus3.5-9B-Coder-DFlash-GGUF:Q4_K_M
```

Lemonade

How to use GauravGosain/Qwopus3.5-9B-Coder-DFlash-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull GauravGosain/Qwopus3.5-9B-Coder-DFlash-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwopus3.5-9B-Coder-DFlash-GGUF-Q4_K_M

List all available models

lemonade list

Qwopus3.5-9B-Coder-DFlash-GGUF

DFlash draft model for Jackrong/Qwopus3.5-9B-Coder, for use with upstream llama.cpp speculative decoding (--spec-type draft-dflash, merged in #22105).

This is z-lab/Qwen3.5-9B-DFlash converted with --target-model-dir pointing at the Qwopus tokenizer. Qwopus extends the Qwen3.5 tokenizer with 7 added tokens (ids 248070 to 248076), so drafts converted against the base Qwen3.5-9B tokenizer do not pass the vocab compatibility check for this target. The draft GGUF carries no token embeddings or lm_head; llama.cpp shares the target model's at runtime, so the draft matches whatever Qwopus quant you serve.

Conversion and launch scripts: https://github.com/Gaurav-Gosain/qwopus-dflash

The demo replays two real captured token streams applying the same edit to a Go file (same prompt, temperature 0): baseline left, DFlash right.

Usage

llama-server \
  -hf Jackrong/Qwopus3.5-9B-Coder-GGUF:Q3_K_M \
  -hfd GauravGosain/Qwopus3.5-9B-Coder-DFlash-GGUF:Q4_K_M \
  --no-mmproj \
  --spec-type draft-dflash --spec-draft-n-max 15 \
  -fa on --jinja -c 4096 -ctk q8_0 -ctv q8_0 -ctxcp 2 -fitt 256

Pass -hf and -hfd together (local -m plus -hfd currently fails to resolve the draft) and keep --no-mmproj (the target repo ships a 921 MB vision projector).

Qwen3.5 is a hybrid linear-attention architecture; keep -ctxcp low because each context checkpoint stores the full recurrent state (about 100 MB).

Files

file	size	note
Qwopus3.5-9B-Coder-DFlash-Q4_K_M.gguf	766 MB	recommended
Qwopus3.5-9B-Coder-DFlash-Q8_0.gguf	1.4 GB	measured identical speed to Q4_K_M
Qwopus3.5-9B-Coder-DFlash-bf16.gguf	2.6 GB	for requantizing

Measured (RTX 3070 8 GB, target Q3_K_M, temp 0, back to back, both fully on GPU)

workload	baseline	DFlash	speedup	acceptance
code editing (rename a field, echo the file)	62 tok/s	304 tok/s	4.9x	0.84
fresh code generation	58 tok/s	145 tok/s	2.5x	0.34

The speedup tracks output predictability; editing existing code is the best case (mean draft length 13.7 of 15). Freeform prose drops to about 0.15 acceptance, still a net win. Both models plus buffers need about 6.5 GB free VRAM and a low fit margin (-fitt 256); if the target spills layers to CPU, speculation goes net-negative.

Also works on Apple Silicon via dflash-mlx: on an M3 Pro 18 GB, code editing goes 28 to 55 tok/s (1.95x) and fresh generation 28 to 42 tok/s (1.49x) with --draft-quant w4 --block-tokens 8. Setup scripts in the GitHub repo.

Downloads last month: 985

GGUF

Model size

1B params

Architecture

dflash

Hardware compatibility

4-bit

8-bit

16-bit

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Model tree for GauravGosain/Qwopus3.5-9B-Coder-DFlash-GGUF

Base model

Qwen/Qwen3.5-9B-Base

Finetuned

Qwen/Qwen3.5-9B

Finetuned

z-lab/Qwen3.5-9B-DFlash

Quantized

(4)

this model