Instructions to use Chungulus/Agents-A1-IQ4_XS-imatrix-gguf-fable5-calibrated with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Chungulus/Agents-A1-IQ4_XS-imatrix-gguf-fable5-calibrated with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Chungulus/Agents-A1-IQ4_XS-imatrix-gguf-fable5-calibrated",
	filename="Agents-A1-IQ4_XS-imatrix-gguf-fable5-calibrated.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/Agents-A1-IQ4_XS-imatrix-gguf-fable5-calibrated 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/Agents-A1-IQ4_XS-imatrix-gguf-fable5-calibrated:IQ4_XS
# Run inference directly in the terminal:
llama cli -hf Chungulus/Agents-A1-IQ4_XS-imatrix-gguf-fable5-calibrated:IQ4_XS

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Chungulus/Agents-A1-IQ4_XS-imatrix-gguf-fable5-calibrated:IQ4_XS
# Run inference directly in the terminal:
llama cli -hf Chungulus/Agents-A1-IQ4_XS-imatrix-gguf-fable5-calibrated:IQ4_XS

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/Agents-A1-IQ4_XS-imatrix-gguf-fable5-calibrated:IQ4_XS
# Run inference directly in the terminal:
./llama-cli -hf Chungulus/Agents-A1-IQ4_XS-imatrix-gguf-fable5-calibrated:IQ4_XS

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/Agents-A1-IQ4_XS-imatrix-gguf-fable5-calibrated:IQ4_XS
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Chungulus/Agents-A1-IQ4_XS-imatrix-gguf-fable5-calibrated:IQ4_XS

Use Docker

docker model run hf.co/Chungulus/Agents-A1-IQ4_XS-imatrix-gguf-fable5-calibrated:IQ4_XS

LM Studio
Jan
Ollama
How to use Chungulus/Agents-A1-IQ4_XS-imatrix-gguf-fable5-calibrated with Ollama:
```
ollama run hf.co/Chungulus/Agents-A1-IQ4_XS-imatrix-gguf-fable5-calibrated:IQ4_XS
```

Unsloth Studio

How to use Chungulus/Agents-A1-IQ4_XS-imatrix-gguf-fable5-calibrated 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/Agents-A1-IQ4_XS-imatrix-gguf-fable5-calibrated 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/Agents-A1-IQ4_XS-imatrix-gguf-fable5-calibrated to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Chungulus/Agents-A1-IQ4_XS-imatrix-gguf-fable5-calibrated to start chatting

How to use Chungulus/Agents-A1-IQ4_XS-imatrix-gguf-fable5-calibrated with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Chungulus/Agents-A1-IQ4_XS-imatrix-gguf-fable5-calibrated:IQ4_XS

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/Agents-A1-IQ4_XS-imatrix-gguf-fable5-calibrated:IQ4_XS"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Chungulus/Agents-A1-IQ4_XS-imatrix-gguf-fable5-calibrated 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/Agents-A1-IQ4_XS-imatrix-gguf-fable5-calibrated:IQ4_XS

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/Agents-A1-IQ4_XS-imatrix-gguf-fable5-calibrated:IQ4_XS

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use Chungulus/Agents-A1-IQ4_XS-imatrix-gguf-fable5-calibrated with Docker Model Runner:
```
docker model run hf.co/Chungulus/Agents-A1-IQ4_XS-imatrix-gguf-fable5-calibrated:IQ4_XS
```

Lemonade

How to use Chungulus/Agents-A1-IQ4_XS-imatrix-gguf-fable5-calibrated with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Chungulus/Agents-A1-IQ4_XS-imatrix-gguf-fable5-calibrated:IQ4_XS

Run and chat with the model

lemonade run user.Agents-A1-IQ4_XS-imatrix-gguf-fable5-calibrated-IQ4_XS

List all available models

lemonade list

Agents-A1-IQ4_XS-imatrix-gguf-fable5-calibrated

Fable-5 trace calibrated imatrix GGUF quant of InternScience/Agents-A1.

File

File	Size	SHA-256
`Agents-A1-IQ4_XS-imatrix-gguf-fable5-calibrated.gguf`	17.44 GiB	`67bb63a2f216f48ecbd8180510d5a930b4c7a053d195d1e798cc5fbeeb70cf18`

Quick Start

llama-cli -hf Chungulus/Agents-A1-IQ4_XS-imatrix-gguf-fable5-calibrated:Agents-A1-IQ4_XS-imatrix-gguf-fable5-calibrated.gguf -p "Write a Python sorting function" -n 160

Ollama

ollama create agents-a1-iq4_xs-imatrix -f Modelfile
ollama run agents-a1-iq4_xs-imatrix

Which File Should I Download?

Use case	Recommendation
Recommended hardware	16-24 GB RAM
Best for	efficient compact 4-bit

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	0.020185
Same top p	92.06%

Notes

Calibration source: Glint-Research/Fable-5-traces
Calibration source revision: e05c417852fc59fd8da758e68b352732423ca0cb
GGUF quantization method: llama.cpp with imatrix calibration.
Static imatrix GGUF; not Unsloth Dynamic 2.0 / UD2.
MTP is not included because the downloaded checkpoint did not contain MTP tensors.
This repo contains local quantization artifacts only.

Downloads last month: -

GGUF

Model size

35B params

Architecture

qwen35moe

Hardware compatibility

4-bit

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