Instructions to use GauravGosain/Qwopus3.5-9B-Coder-DFlash-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 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 Desktop

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 @earendil-works/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

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

Hermes Agent

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

OpenClaw

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"

GauravGosain commited on Jul 13

Commit

1af922d

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1 Parent(s): e4ee1ee

Upload README.md with huggingface_hub

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README.md CHANGED Viewed

@@ -50,4 +50,4 @@ Qwen3.5 is a hybrid linear-attention architecture; keep `-ctxcp` low because eac
 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](https://github.com/bstnxbt/dflash-mlx): on an M3 Pro 18 GB, code editing goes 28 to 52 tok/s (1.87x) and fresh generation 28 to 41 tok/s (1.46x) with `--draft-quant w4`. Setup scripts in the [GitHub repo](https://github.com/Gaurav-Gosain/qwopus-dflash).


50
51	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.
52
53	+ Also works on Apple Silicon via [dflash-mlx](https://github.com/bstnxbt/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](https://github.com/Gaurav-Gosain/qwopus-dflash).