Instructions to use eulogik/Bharat-Tiny-LLM-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use eulogik/Bharat-Tiny-LLM-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="eulogik/Bharat-Tiny-LLM-GGUF",
	filename="bharat-tiny-llm-f16.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 eulogik/Bharat-Tiny-LLM-GGUF 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 eulogik/Bharat-Tiny-LLM-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf eulogik/Bharat-Tiny-LLM-GGUF:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf eulogik/Bharat-Tiny-LLM-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf eulogik/Bharat-Tiny-LLM-GGUF: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 eulogik/Bharat-Tiny-LLM-GGUF:F16
# Run inference directly in the terminal:
./llama-cli -hf eulogik/Bharat-Tiny-LLM-GGUF: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 eulogik/Bharat-Tiny-LLM-GGUF:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf eulogik/Bharat-Tiny-LLM-GGUF:F16

Use Docker

docker model run hf.co/eulogik/Bharat-Tiny-LLM-GGUF:F16

LM Studio
Jan

vLLM

How to use eulogik/Bharat-Tiny-LLM-GGUF with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/eulogik/Bharat-Tiny-LLM-GGUF:F16

Ollama
How to use eulogik/Bharat-Tiny-LLM-GGUF with Ollama:
```
ollama run hf.co/eulogik/Bharat-Tiny-LLM-GGUF:F16
```

Unsloth Studio

How to use eulogik/Bharat-Tiny-LLM-GGUF 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 eulogik/Bharat-Tiny-LLM-GGUF 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 eulogik/Bharat-Tiny-LLM-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for eulogik/Bharat-Tiny-LLM-GGUF to start chatting

How to use eulogik/Bharat-Tiny-LLM-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf eulogik/Bharat-Tiny-LLM-GGUF:F16

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": "eulogik/Bharat-Tiny-LLM-GGUF:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use eulogik/Bharat-Tiny-LLM-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf eulogik/Bharat-Tiny-LLM-GGUF: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 eulogik/Bharat-Tiny-LLM-GGUF:F16

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use eulogik/Bharat-Tiny-LLM-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf eulogik/Bharat-Tiny-LLM-GGUF: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 "eulogik/Bharat-Tiny-LLM-GGUF: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"

Docker Model Runner
How to use eulogik/Bharat-Tiny-LLM-GGUF with Docker Model Runner:
```
docker model run hf.co/eulogik/Bharat-Tiny-LLM-GGUF:F16
```

Lemonade

How to use eulogik/Bharat-Tiny-LLM-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull eulogik/Bharat-Tiny-LLM-GGUF:F16

Run and chat with the model

lemonade run user.Bharat-Tiny-LLM-GGUF-F16

List all available models

lemonade list

Bharat-Tiny-LLM (GGUF)

llama.cpp builds of Bharat-Tiny-LLM — India's first native edge AI for Hinglish & Hindi. These run cross-platform: Android, Raspberry Pi, CPU, and GPU via llama.cpp / llama-cpp-python.

Built by eulogik

Files

File	Format	Size	Use
`bharat-tiny-llm-q4_k_m.gguf`	GGUF Q4_K_M	~1.06 GB	Recommended — best size/quality for edge
`bharat-tiny-llm-f16.gguf`	GGUF f16	~3.55 GB	Full precision, for re-quantizing

Quick start

pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama(model_path="bharat-tiny-llm-q4_k_m.gguf", n_ctx=1024)
print(llm.create_chat_completion(
    messages=[{"role": "user", "content": "Chai peete hain?"}],
    temperature=0.3, top_p=0.85, max_tokens=256, repeat_penalty=1.25,
)["choices"][0]["message"]["content"])

⚠️ Generation config matters. The base Qwen2.5-1.5B emits garbled out-of-script tokens at high temperature. Always use temperature ≈ 0.3 + repeat_penalty ≥ 1.25.

Other builds

Build	Repo	Size
MLX 4-bit (Apple Silicon)	`eulogik/Bharat-Tiny-LLM`	~880 MB
PyTorch fp16 (server / fine-tune)	`eulogik/Bharat-Tiny-LLM-fused`	~3.3 GB

License

Apache-2.0 (base Qwen2.5-1.5B weights Apache-2.0; LoRA adapter Apache-2.0).

Downloads last month: 159

GGUF

Model size

2B params

Architecture

qwen2

Hardware compatibility

4-bit

16-bit

Model tree for eulogik/Bharat-Tiny-LLM-GGUF

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Bharat-Tiny-LLM (GGUF)

Files

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