Instructions to use Hanuman2/Veda-Labs-0.5B-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Hanuman2/Veda-Labs-0.5B-instruct with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Hanuman2/Veda-Labs-0.5B-instruct",
	filename="supralabs-q4_k_m.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Hanuman2/Veda-Labs-0.5B-instruct 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 Hanuman2/Veda-Labs-0.5B-instruct:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Hanuman2/Veda-Labs-0.5B-instruct:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Hanuman2/Veda-Labs-0.5B-instruct:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Hanuman2/Veda-Labs-0.5B-instruct:Q4_K_M

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 Hanuman2/Veda-Labs-0.5B-instruct:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Hanuman2/Veda-Labs-0.5B-instruct:Q4_K_M

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 Hanuman2/Veda-Labs-0.5B-instruct:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Hanuman2/Veda-Labs-0.5B-instruct:Q4_K_M

Use Docker

docker model run hf.co/Hanuman2/Veda-Labs-0.5B-instruct:Q4_K_M

LM Studio
Jan

vLLM

How to use Hanuman2/Veda-Labs-0.5B-instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Hanuman2/Veda-Labs-0.5B-instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Hanuman2/Veda-Labs-0.5B-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Hanuman2/Veda-Labs-0.5B-instruct:Q4_K_M

Ollama
How to use Hanuman2/Veda-Labs-0.5B-instruct with Ollama:
```
ollama run hf.co/Hanuman2/Veda-Labs-0.5B-instruct:Q4_K_M
```

Unsloth Studio

How to use Hanuman2/Veda-Labs-0.5B-instruct 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 Hanuman2/Veda-Labs-0.5B-instruct 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 Hanuman2/Veda-Labs-0.5B-instruct to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Hanuman2/Veda-Labs-0.5B-instruct to start chatting

How to use Hanuman2/Veda-Labs-0.5B-instruct with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Hanuman2/Veda-Labs-0.5B-instruct:Q4_K_M

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": "Hanuman2/Veda-Labs-0.5B-instruct:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Hanuman2/Veda-Labs-0.5B-instruct with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Hanuman2/Veda-Labs-0.5B-instruct:Q4_K_M

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 Hanuman2/Veda-Labs-0.5B-instruct:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use Hanuman2/Veda-Labs-0.5B-instruct with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Hanuman2/Veda-Labs-0.5B-instruct:Q4_K_M

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 "Hanuman2/Veda-Labs-0.5B-instruct:Q4_K_M" \
  --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 Hanuman2/Veda-Labs-0.5B-instruct with Docker Model Runner:
```
docker model run hf.co/Hanuman2/Veda-Labs-0.5B-instruct:Q4_K_M
```

Lemonade

How to use Hanuman2/Veda-Labs-0.5B-instruct with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Hanuman2/Veda-Labs-0.5B-instruct:Q4_K_M

Run and chat with the model

lemonade run user.Veda-Labs-0.5B-instruct-Q4_K_M

List all available models

lemonade list

Veda-Labs-0.5B-instruct / README.md

Hanuman2

Upload README.md with huggingface_hub

ef8289e verified 26 days ago

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	---
	license: apache-2.0
	language:
	- en
	tags:
	- text-generation
	- nlp
	- supra-labs
	- custom-model
	- llama-cpp
	- gguf-my-repo
	base_model: Hanuman2/Supralabs
	---

	# Hanuman2/Supralabs-Q4_K_M-GGUF
	This model was converted to GGUF format from [`Hanuman2/Supralabs`](https://huggingface.co/Hanuman2/Supralabs) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
	Refer to the [original model card](https://huggingface.co/Hanuman2/Supralabs) for more details on the model.

	## Use with llama.cpp
	Install llama.cpp through brew (works on Mac and Linux)

	```bash
	brew install llama.cpp

	```
	Invoke the llama.cpp server or the CLI.

	### CLI:
	```bash
	llama-cli --hf-repo Hanuman2/Supralabs-Q4_K_M-GGUF --hf-file supralabs-q4_k_m.gguf -p "The meaning to life and the universe is"
	```

	### Server:
	```bash
	llama-server --hf-repo Hanuman2/Supralabs-Q4_K_M-GGUF --hf-file supralabs-q4_k_m.gguf -c 2048
	```

	Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.

	Step 1: Clone llama.cpp from GitHub.
	```
	git clone https://github.com/ggerganov/llama.cpp
	```

	Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
	```
	cd llama.cpp && LLAMA_CURL=1 make
	```

	Step 3: Run inference through the main binary.
	```
	./llama-cli --hf-repo Hanuman2/Supralabs-Q4_K_M-GGUF --hf-file supralabs-q4_k_m.gguf -p "The meaning to life and the universe is"
	```
	or
	```
	./llama-server --hf-repo Hanuman2/Supralabs-Q4_K_M-GGUF --hf-file supralabs-q4_k_m.gguf -c 2048
	```