Instructions to use h34v7/Jackrong-Qwopus3.5-27B-v3-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 h34v7/Jackrong-Qwopus3.5-27B-v3-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 h34v7/Jackrong-Qwopus3.5-27B-v3-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf h34v7/Jackrong-Qwopus3.5-27B-v3-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 h34v7/Jackrong-Qwopus3.5-27B-v3-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf h34v7/Jackrong-Qwopus3.5-27B-v3-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 h34v7/Jackrong-Qwopus3.5-27B-v3-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf h34v7/Jackrong-Qwopus3.5-27B-v3-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 h34v7/Jackrong-Qwopus3.5-27B-v3-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf h34v7/Jackrong-Qwopus3.5-27B-v3-GGUF:Q4_K_M

Use Docker

docker model run hf.co/h34v7/Jackrong-Qwopus3.5-27B-v3-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use h34v7/Jackrong-Qwopus3.5-27B-v3-GGUF with Ollama:
```
ollama run hf.co/h34v7/Jackrong-Qwopus3.5-27B-v3-GGUF:Q4_K_M
```
Unsloth Desktop

How to use h34v7/Jackrong-Qwopus3.5-27B-v3-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf h34v7/Jackrong-Qwopus3.5-27B-v3-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": "h34v7/Jackrong-Qwopus3.5-27B-v3-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use h34v7/Jackrong-Qwopus3.5-27B-v3-GGUF with Docker Model Runner:
```
docker model run hf.co/h34v7/Jackrong-Qwopus3.5-27B-v3-GGUF:Q4_K_M
```

Lemonade

How to use h34v7/Jackrong-Qwopus3.5-27B-v3-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull h34v7/Jackrong-Qwopus3.5-27B-v3-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Jackrong-Qwopus3.5-27B-v3-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use h34v7/Jackrong-Qwopus3.5-27B-v3-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 h34v7/Jackrong-Qwopus3.5-27B-v3-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 h34v7/Jackrong-Qwopus3.5-27B-v3-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use h34v7/Jackrong-Qwopus3.5-27B-v3-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf h34v7/Jackrong-Qwopus3.5-27B-v3-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 "h34v7/Jackrong-Qwopus3.5-27B-v3-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"

Any plans on trying to make Q1 or Q2 models?

by 19440harry - opened Apr 8

Discussion

19440harry

Apr 8

Hey, your quantized models are really good and I was wondering if you could try reducing the model to Q1 and Q2 sized model. If the quality persists then this model becomes accessible to individuals with low VRAM availablity.

h34v7

Owner Apr 8

Sure if you need it i could make it. But expect pretty severre perplexity degradation it is unavoidable. i heard 1bit bonsai and 2bit turboquant are good. but... i had no idea how to make them. 1bit bonsai need retraining meanwhile turboquant 2bit infra wasn't there yet... so yeah... maybe regular and imatrix quant i could upload

19440harry

Apr 8

That would be great, I am curious about the performance since your quantizations so far have been better than most I have seen and I am curious if it could translate in the smaller quants. I am aware you use the Ik_llama fork for the IQ models, however I dont think turboquant has been integrated into the ik llama fork yet, quite new to this so I am not sure if it could be at all.
Regardless, an IQ1/2 would be great if you could try to make that happen.

Thank you for your work.

h34v7

Owner Apr 8

•

edited Apr 8

Some after some test 1-bit and 2-bit simply wasn't enough bit to fit any weight. This is the result of a simple "Hi" test at f16 kv cache.

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Quant: IQ2_XS

19440harry

Apr 8

Ahh so no IQ1 or IQ2, thats unfortunate, thank you for attempting though, I really appreciate the trial and the very quick response.

h34v7

Owner Apr 8

Yeah, unfortunately. Those stuff are too technical for me to handle. Thanks for swinging by

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