Instructions to use Chungulus/A1-Q2_K-imatrix-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Chungulus/A1-Q2_K-imatrix-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Chungulus/A1-Q2_K-imatrix-GGUF",
	filename="A1-Q2_K-imatrix.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 Chungulus/A1-Q2_K-imatrix-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 Chungulus/A1-Q2_K-imatrix-GGUF:Q2_K
# Run inference directly in the terminal:
llama cli -hf Chungulus/A1-Q2_K-imatrix-GGUF:Q2_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Chungulus/A1-Q2_K-imatrix-GGUF:Q2_K
# Run inference directly in the terminal:
llama cli -hf Chungulus/A1-Q2_K-imatrix-GGUF:Q2_K

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 Chungulus/A1-Q2_K-imatrix-GGUF:Q2_K
# Run inference directly in the terminal:
./llama-cli -hf Chungulus/A1-Q2_K-imatrix-GGUF:Q2_K

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 Chungulus/A1-Q2_K-imatrix-GGUF:Q2_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Chungulus/A1-Q2_K-imatrix-GGUF:Q2_K

Use Docker

docker model run hf.co/Chungulus/A1-Q2_K-imatrix-GGUF:Q2_K

LM Studio
Jan
Ollama
How to use Chungulus/A1-Q2_K-imatrix-GGUF with Ollama:
```
ollama run hf.co/Chungulus/A1-Q2_K-imatrix-GGUF:Q2_K
```

Unsloth Studio

How to use Chungulus/A1-Q2_K-imatrix-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 Chungulus/A1-Q2_K-imatrix-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 Chungulus/A1-Q2_K-imatrix-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Chungulus/A1-Q2_K-imatrix-GGUF to start chatting

How to use Chungulus/A1-Q2_K-imatrix-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Chungulus/A1-Q2_K-imatrix-GGUF:Q2_K

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": "Chungulus/A1-Q2_K-imatrix-GGUF:Q2_K"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Chungulus/A1-Q2_K-imatrix-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 Chungulus/A1-Q2_K-imatrix-GGUF:Q2_K

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 Chungulus/A1-Q2_K-imatrix-GGUF:Q2_K

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use Chungulus/A1-Q2_K-imatrix-GGUF with Docker Model Runner:
```
docker model run hf.co/Chungulus/A1-Q2_K-imatrix-GGUF:Q2_K
```

Lemonade

How to use Chungulus/A1-Q2_K-imatrix-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Chungulus/A1-Q2_K-imatrix-GGUF:Q2_K

Run and chat with the model

lemonade run user.A1-Q2_K-imatrix-GGUF-Q2_K

List all available models

lemonade list

A1-Q2_K-imatrix

Static imatrix-calibrated GGUF quant of InternScience/Agents-A1.

llama-cli -hf Chungulus/A1-Q2_K-imatrix-GGUF -p "Write a Python sorting function" -n 160

File

File	Size	SHA-256
`A1-Q2_K-imatrix.gguf`	12.05 GiB	`58d9f6e1de32a960c4dca0dca932241956303687b3a7436f8852a69812864067`

Quality Snapshot

F16 baseline mini accuracy: 89.58%. F16 baseline PPL on KL holdout: 13.0194.

Metric	Value
Mini accuracy	87.50%
Retention vs F16	97.67%
Mean KLD vs F16	0.128242
Same top p	81.75%

Notes

Calibration source: Glint-Research/Fable-5-traces
MTP is not included because the downloaded checkpoint did not contain MTP tensors.
Static imatrix GGUF, not Unsloth Dynamic 2.0 / UD2.

Downloads last month: -

GGUF

Model size

35B params

Architecture

qwen35moe

Hardware compatibility

2-bit

Inference Providers NEW

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Model tree for Chungulus/A1-Q2_K-imatrix-GGUF

Base model

InternScience/Agents-A1

Quantized

(39)

this model

Collection including Chungulus/A1-Q2_K-imatrix-GGUF

Agents-A1 imatrix GGUF

Collection

Local imatrix GGUF quantizations of InternScience/Agents-A1 with KL and mini accuracy results. • 1 item • Updated about 4 hours ago