Instructions to use Jeppcode/ScalableLab2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Jeppcode/ScalableLab2 with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Jeppcode/ScalableLab2", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Jeppcode/ScalableLab2 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 Jeppcode/ScalableLab2:F16
# Run inference directly in the terminal:
llama cli -hf Jeppcode/ScalableLab2:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Jeppcode/ScalableLab2:F16
# Run inference directly in the terminal:
llama cli -hf Jeppcode/ScalableLab2:F16

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 Jeppcode/ScalableLab2:F16
# Run inference directly in the terminal:
./llama-cli -hf Jeppcode/ScalableLab2:F16

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 Jeppcode/ScalableLab2:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Jeppcode/ScalableLab2:F16

Use Docker

docker model run hf.co/Jeppcode/ScalableLab2:F16

LM Studio
Jan
Ollama
How to use Jeppcode/ScalableLab2 with Ollama:
```
ollama run hf.co/Jeppcode/ScalableLab2:F16
```
Unsloth Desktop

How to use Jeppcode/ScalableLab2 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Jeppcode/ScalableLab2:F16

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": "Jeppcode/ScalableLab2:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Jeppcode/ScalableLab2 with Docker Model Runner:
```
docker model run hf.co/Jeppcode/ScalableLab2:F16
```

Lemonade

How to use Jeppcode/ScalableLab2 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Jeppcode/ScalableLab2:F16

Run and chat with the model

lemonade run user.ScalableLab2-F16

List all available models

lemonade list

Hermes Agent

How to use Jeppcode/ScalableLab2 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Jeppcode/ScalableLab2:F16

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 Jeppcode/ScalableLab2:F16

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Jeppcode/ScalableLab2 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Jeppcode/ScalableLab2:F16

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 "Jeppcode/ScalableLab2:F16" \
  --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"

ScalableLab2 / README.md

Jeppcode

Training in progress, step 60

5ca1e1a verified 10 months ago

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	---
	base_model: unsloth/Llama-3.2-3B-Instruct
	library_name: transformers
	model_name: ScalableLab2
	tags:
	- generated_from_trainer
	- trl
	- unsloth
	- sft
	licence: license
	---

	# Model Card for ScalableLab2

	This model is a fine-tuned version of [unsloth/Llama-3.2-3B-Instruct](https://huggingface.co/unsloth/Llama-3.2-3B-Instruct).
	It has been trained using [TRL](https://github.com/huggingface/trl).

	## Quick start

	```python
	from transformers import pipeline

	question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
	generator = pipeline("text-generation", model="Jeppcode/ScalableLab2", device="cuda")
	output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
	print(output["generated_text"])
	```

	## Training procedure




	This model was trained with SFT.

	### Framework versions

	- TRL: 0.23.0
	- Transformers: 4.57.1
	- Pytorch: 2.9.0+cu126
	- Datasets: 4.3.0
	- Tokenizers: 0.22.1

	## Citations



	Cite TRL as:

	```bibtex
	@misc{vonwerra2022trl,
	title = {{TRL: Transformer Reinforcement Learning}},
	author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
	year = 2020,
	journal = {GitHub repository},
	publisher = {GitHub},
	howpublished = {\url{https://github.com/huggingface/trl}}
	}
	```