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

fb41f39

verified ·

1 Parent(s): 1af922d

Upload README.md with huggingface_hub

Browse files

Files changed (1) hide show

README.md +6 -3

README.md CHANGED Viewed

@@ -25,12 +25,15 @@ The demo replays two real captured token streams applying the same edit to a Go
 ```sh
 llama-server \
-  -m Qwopus3.5-9B-coder-Exp-Q3_K_M.gguf \
-  -md Qwopus3.5-9B-Coder-DFlash-Q4_K_M.gguf \
   --spec-type draft-dflash --spec-draft-n-max 15 \
-  -fa on --jinja -ctxcp 2 -fitt 256
 ```
 Qwen3.5 is a hybrid linear-attention architecture; keep `-ctxcp` low because each context checkpoint stores the full recurrent state (about 100 MB).
 ## Files

 ```sh
 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