Instructions to use ToldByNun/mango-1.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use ToldByNun/mango-1.0 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 ToldByNun/mango-1.0:Q2_K_L
# Run inference directly in the terminal:
llama cli -hf ToldByNun/mango-1.0:Q2_K_L

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ToldByNun/mango-1.0:Q2_K_L
# Run inference directly in the terminal:
llama cli -hf ToldByNun/mango-1.0:Q2_K_L

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 ToldByNun/mango-1.0:Q2_K_L
# Run inference directly in the terminal:
./llama-cli -hf ToldByNun/mango-1.0:Q2_K_L

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 ToldByNun/mango-1.0:Q2_K_L
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ToldByNun/mango-1.0:Q2_K_L

Use Docker

docker model run hf.co/ToldByNun/mango-1.0:Q2_K_L

LM Studio
Jan

vLLM

How to use ToldByNun/mango-1.0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ToldByNun/mango-1.0"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ToldByNun/mango-1.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ToldByNun/mango-1.0:Q2_K_L

Ollama
How to use ToldByNun/mango-1.0 with Ollama:
```
ollama run hf.co/ToldByNun/mango-1.0:Q2_K_L
```

Unsloth Studio

How to use ToldByNun/mango-1.0 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 ToldByNun/mango-1.0 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 ToldByNun/mango-1.0 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ToldByNun/mango-1.0 to start chatting

How to use ToldByNun/mango-1.0 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ToldByNun/mango-1.0:Q2_K_L

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": "ToldByNun/mango-1.0:Q2_K_L"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use ToldByNun/mango-1.0 with Docker Model Runner:
```
docker model run hf.co/ToldByNun/mango-1.0:Q2_K_L
```

Lemonade

How to use ToldByNun/mango-1.0 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ToldByNun/mango-1.0:Q2_K_L

Run and chat with the model

lemonade run user.mango-1.0-Q2_K_L

List all available models

lemonade list

Hermes Agent

How to use ToldByNun/mango-1.0 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ToldByNun/mango-1.0:Q2_K_L

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 ToldByNun/mango-1.0:Q2_K_L

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use ToldByNun/mango-1.0 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ToldByNun/mango-1.0:Q2_K_L

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 "ToldByNun/mango-1.0:Q2_K_L" \
  --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"

mango-1.0 (Q2_K_L GGUF)

Local coding / agent model for Mango — a desktop coding agent built around small GGUF runtimes (llama.cpp). This repo ships a quantized GGUF of Mango 1.0 (~27B, Qwen3.8 family), fine-tuned for tool-using agent workflows (short thoughts, tool calls, file edits, Q&A over a workspace).

Model details


Family	Qwen3.8 (~27B)
Format	GGUF
Quant	Q2_K_L
File	`mango-1.0-Q2_K_L.gguf` (~12.1 GB)
Context	up to 262k (runtime-dependent; use what your VRAM allows)
Chat template	Qwen / ChatML (`<
Intended use	Local coding agent, tool calling, repo Q&A

Intended use

Run inside Mango (Electron + Python sidecar + llama.cpp), or any llama.cpp-compatible runner (LM Studio, koboldcpp, etc.).
Coding assistance: read/search/edit files, answer questions about a workspace, follow agent tool protocols.
Offline / privacy-sensitive development.

Not intended for

High-stakes medical, legal, or financial advice
Unsupervised production systems without human review
Tasks that need full FP16 quality (this is a heavy 2-bit-class quant)

How to use

Mango app

Download mango-1.0-Q2_K_L.gguf
In Mango → Settings, set the local .gguf path
Start a session and use /ask, /plan, or agent mode as usual

llama.cpp / LM Studio

Load the GGUF like any other Qwen ChatML model. Prefer a GPU offload that fits your VRAM; leave layers on CPU if needed. Example (llama.cpp CLI sketch):

./llama-cli -m mango-1.0-Q2_K_L.gguf -c 8192 -ngl 99 -p "You are a coding assistant."

Training (high level)

Base: Qwen3.8-class 27B (Unsloth / QLoRA pipeline in the Mango training scripts)
Method: 4-bit QLoRA → merge → GGUF export
Focus: Agent/coding-style SFT (tool use, short CoT, workspace workflows) Exact datasets and hyperparameters live in the Mango public repo under training/.

Limitations

Quantization: Q2_K_L is aggressive — expect weaker long-horizon reasoning and more tool-format slips than higher quants (Q4/Q5/Q8) or FP16.
Hallucinations: May invent APIs/paths if not grounded by tools; prefer Ask/Agent modes that read files.
Safety: Same class of risks as other open coding models (unsafe code suggestions, prompt injection via repo content).

License

Apache-2.0 (unless otherwise noted for the base model / training data — check base model cards as well).

Citation

@misc{mango10_gguf,
  title        = {Mango 1.0 GGUF},
  author       = {ToldByNun},
  year         = {2026},
  howpublished = {\url{https://huggingface.co/ToldByNun/mango-1.0-iq2-xs}},
  note         = {Qwen3.8-based local coding agent model, Q2\_K\_L GGUF}
}

Model tree for ToldByNun/mango-1.0

Base model

Qwen/Qwen3.8-27B

Quantized

(814)

this model

ToldByNun
/

mango-1.0

mango-1.0 (Q2_K_L GGUF)

Model details

Intended use

Not intended for

How to use

Mango app

llama.cpp / LM Studio

Training (high level)

Limitations

License

Citation

Links

Model tree for ToldByNun/mango-1.0