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"

Important Note

For IQ_KS DO NOT use mainline llama.cpp,ollama or anything that use mainline llama.cpp backend use ik_llama.cpp instead.

Q_K_M is fine thought.

Still uploading BTW!

Quantization using ik_llama.cpp 6ea7f32

Calibration data by Bartowski thank you legends!

Perplexity test using Wikitext-2 test.raw

BF16 - Final estimate: PPL over 72 chunks for n_ctx=4096 = 6.2671 +/- 0.04039
Q6_K - Final estimate: PPL over 72 chunks for n_ctx=4096 = 6.2376 +/- 0.04001
Q5_K_M - Final estimate: PPL over 72 chunks for n_ctx=4096 = 6.2564 +/- 0.04021
Q4_K_M - Final estimate: PPL over 72 chunks for n_ctx=4096 = 6.2901 +/- 0.04049
IQ4_KS - Final estimate: PPL over 72 chunks for n_ctx=4096 = 6.2921 +/- 0.04055
Q3_K_M - Final estimate: PPL over 72 chunks for n_ctx=4096 = 6.4269 +/- 0.04165
IQ3_KS - Final estimate: PPL over 72 chunks for n_ctx=4096 = 6.4566 +/- 0.04177

Holy Shit!! Why is it so low thought?! That's Lossless!! (Might got butchered in creative field tho)

Note these quant model is not coherence (perhaps for draft model? or maybe with proper system prompt could work? haven't tried instruct too):

IQ2_XS Final estimate: PPL over 72 chunks for n_ctx=4096 = 7.3814 +/- 0.04912 (Even with custom quant recipe)

Dunno what's going on somehow the Q5_K_M preplexity is lower than BF16, need to investigate.

Okay so i think... prunning noise by using imatrix calibration make the models less uncertain on making word decision that mean... yes the model are more deterministic perhaps less creative? unconfirmed but more focused?? I have no idea!

So in theory you could make your own custom calibration data that worked almost like a lora except instead of adding data you're only keeping those more aligned with your goals and discard the rest.

Maybe i'm wrong perhaps it's related to QAT or something... have no idea!