Instructions to use arunmcops/Param-1-2.9B-Instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use arunmcops/Param-1-2.9B-Instruct-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 arunmcops/Param-1-2.9B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf arunmcops/Param-1-2.9B-Instruct-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf arunmcops/Param-1-2.9B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf arunmcops/Param-1-2.9B-Instruct-GGUF: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 arunmcops/Param-1-2.9B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf arunmcops/Param-1-2.9B-Instruct-GGUF: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 arunmcops/Param-1-2.9B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf arunmcops/Param-1-2.9B-Instruct-GGUF:Q4_K_M

Use Docker

docker model run hf.co/arunmcops/Param-1-2.9B-Instruct-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use arunmcops/Param-1-2.9B-Instruct-GGUF with Ollama:
```
ollama run hf.co/arunmcops/Param-1-2.9B-Instruct-GGUF:Q4_K_M
```
Unsloth Desktop

How to use arunmcops/Param-1-2.9B-Instruct-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf arunmcops/Param-1-2.9B-Instruct-GGUF:Q4_K_M

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": "arunmcops/Param-1-2.9B-Instruct-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use arunmcops/Param-1-2.9B-Instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/arunmcops/Param-1-2.9B-Instruct-GGUF:Q4_K_M
```

Lemonade

How to use arunmcops/Param-1-2.9B-Instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull arunmcops/Param-1-2.9B-Instruct-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Param-1-2.9B-Instruct-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use arunmcops/Param-1-2.9B-Instruct-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 arunmcops/Param-1-2.9B-Instruct-GGUF: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 arunmcops/Param-1-2.9B-Instruct-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use arunmcops/Param-1-2.9B-Instruct-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf arunmcops/Param-1-2.9B-Instruct-GGUF: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 "arunmcops/Param-1-2.9B-Instruct-GGUF: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"

Param-1 2.9B Instruct — GGUF

General Purpose · Bilingual | BharatGenAI | हिन्दी + English

Param-1 is BharatGenAI's general-purpose bilingual large language model with 2.9B parameters, instruction-tuned for Hindi and English. Designed for conversational AI, reasoning, summarization, and knowledge-intensive tasks across Indian languages.

Available Quantizations

Quantization	File	Size	Description
F16	`Param-1-2.9B-Instruct-F16.gguf`	~5.7 GB	Full float 16, near-lossless, GPU recommended
Q2_K	`Param-1-2.9B-Instruct-Q2_K.gguf`	~0.9 GB	2-bit, smallest, significant quality loss
Q3_K_M	`Param-1-2.9B-Instruct-Q3_K_M.gguf`	~1.3 GB	3-bit medium, moderate quality
Q4_K_M	`Param-1-2.9B-Instruct-Q4_K_M.gguf`	~1.7 GB	4-bit medium — recommended for most users
Q5_K_M	`Param-1-2.9B-Instruct-Q5_K_M.gguf`	~2.0 GB	5-bit medium, great quality
Q6_K	`Param-1-2.9B-Instruct-Q6_K.gguf`	~2.4 GB	6-bit, excellent quality
Q8_0	`Param-1-2.9B-Instruct-Q8_0.gguf`	~3.0 GB	8-bit, best quality/size balance

Not sure which to pick? → Q4_K_M for everyday use on CPU/GPU → Q8_0 for best quality on GPU with ≥ 6 GB VRAM → F16/BF16 for full precision research use

Use Cases

Hindi and English question answering
Conversational AI and chatbots
Text summarization and translation
Code assistance in Indian context
General reasoning and knowledge tasks

Quick Start

llama.cpp

# Windows
winget install llama.cpp

# Run directly from Hugging Face
llama-cli -hf arunmcops/Param-1-2.9B-Instruct-GGUF:Param-1-2.9B-Instruct-Q4_K_M.gguf

# OpenAI-compatible server
llama-server -hf arunmcops/Param-1-2.9B-Instruct-GGUF:Param-1-2.9B-Instruct-Q4_K_M.gguf --port 8080

Ollama

ollama run hf.co/arunmcops/Param-1-2.9B-Instruct-GGUF:Param-1-2.9B-Instruct-Q4_K_M.gguf

Python (llama-cpp-python)

from llama_cpp import Llama

llm = Llama.from_pretrained(
    repo_id="arunmcops/Param-1-2.9B-Instruct-GGUF",
    filename="Param-1-2.9B-Instruct-Q4_K_M.gguf",
)
response = llm.create_chat_completion(
    messages=[{"role": "user", "content": "नमस्ते! आप कौन हैं?"}]
)
print(response["choices"][0]["message"]["content"])

About BharatGenAI

BharatGenAI builds open-source language models for India — bilingual (Hindi + English) and domain-specialized for agriculture, healthcare, legal, and general-purpose use.

HuggingFace: arunmcops

License

Apache 2.0 — free for personal, research, and commercial use.

Downloads last month: 284

GGUF

Model size

3B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support