Instructions to use Archi-medes/LabGuide_Preview with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Archi-medes/LabGuide_Preview 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 Archi-medes/LabGuide_Preview
# Run inference directly in the terminal:
llama cli -hf Archi-medes/LabGuide_Preview

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Archi-medes/LabGuide_Preview
# Run inference directly in the terminal:
llama cli -hf Archi-medes/LabGuide_Preview

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 Archi-medes/LabGuide_Preview
# Run inference directly in the terminal:
./llama-cli -hf Archi-medes/LabGuide_Preview

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 Archi-medes/LabGuide_Preview
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Archi-medes/LabGuide_Preview

Use Docker

docker model run hf.co/Archi-medes/LabGuide_Preview

LM Studio
Jan
Ollama
How to use Archi-medes/LabGuide_Preview with Ollama:
```
ollama run hf.co/Archi-medes/LabGuide_Preview
```

Unsloth Studio

How to use Archi-medes/LabGuide_Preview 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 Archi-medes/LabGuide_Preview 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 Archi-medes/LabGuide_Preview to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Archi-medes/LabGuide_Preview to start chatting

How to use Archi-medes/LabGuide_Preview with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Archi-medes/LabGuide_Preview

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": "Archi-medes/LabGuide_Preview"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Archi-medes/LabGuide_Preview with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Archi-medes/LabGuide_Preview

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 Archi-medes/LabGuide_Preview

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use Archi-medes/LabGuide_Preview with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Archi-medes/LabGuide_Preview

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 "Archi-medes/LabGuide_Preview" \
  --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"

Docker Model Runner
How to use Archi-medes/LabGuide_Preview with Docker Model Runner:
```
docker model run hf.co/Archi-medes/LabGuide_Preview
```

Lemonade

How to use Archi-medes/LabGuide_Preview with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Archi-medes/LabGuide_Preview

Run and chat with the model

lemonade run user.LabGuide_Preview-{{QUANT_TAG}}

List all available models

lemonade list

LabGuide_Preview / README.md

Archi-medes

Update README.md

b65c438 verified 8 months ago

preview code

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	---
	license: other
	license_name: lfm1.0
	license_link: https://huggingface.co/LiquidAI/LFM2-350M/blob/main/LICENSE
	datasets:
	- Archi-medes/LabGuide_Preview
	base_model:
	- LiquidAI/LFM2-700M
	base_model_relation: finetune
	pipeline_tag: question-answering
	---
	# LabGuide Preview Model

	## Model Summary
	The LabGuide Preview Model is a demonstration release built entirely with Madlab, using its synthetic dataset generator and training workflow.
	It is based on [LiquidAI/LFM2-700M](https://huggingface.co/LiquidAI/LFM2-700M), adapted to showcase Madlab’s end-to-end capabilities for dataset creation, model training, and assistant deployment.

	This model illustrates how applications can leverage Madlab to train their own assistants in a reproducible and accessible way.
	It is not intended for production use, but rather as a preview for contributors, collaborators, and community feedback.

	---

	## Training Data
	- Source: Synthetic dataset generated entirely with Madlab’s dataset generator.
	- Purpose: Designed to demonstrate Madlab’s ability to produce structured, reproducible training data.
	- Scope: Preview-scale dataset, not representative of real-world or production-ready corpora.

	---

	## Training Process
	- Framework: Madlab training pipeline.
	- Base Model: [LiquidAI/LFM2-700M](https://huggingface.co/LiquidAI/LFM2-700M).
	- Workflow: Synthetic dataset generation → Madlab training loop → Magic Judge Evaluation → Preview model release.
	- Objective: Demonstrate Madlab’s integrated workflow for building application-specific assistants.

	---

	## Intended Uses
	- Contributor onboarding and workflow validation.
	- Demonstration of Madlab’s synthetic dataset generator and training pipeline.
	- Benchmarking and experimentation in controlled preview settings.

	---

	## Limitations
	- Demo-only: Not suitable for production or deployment in real-world applications.
	- Synthetic data: Training data is fully synthetic and may not reflect natural language distributions.
	- Preview scale: Model performance is illustrative, not optimized for accuracy or robustness.

	---

	## Ethical Considerations
	- This model is provided for demonstration and educational purposes.
	- It should not be used in applications where accuracy, safety, or reliability are critical.
	- Contributors are encouraged to treat outputs as illustrative examples only.

	---

	## Acknowledgements
	- Base model: [LiquidAI/LFM2-700M](https://huggingface.co/LiquidAI/LFM2-700M).
	- Built and trained with [Madlab](https://github.com/archimedes1618/madlab).