license, license_name, license_link, language, library_name, pipeline_tag, tags, base_model

license	license_name	license_link	language	library_name	pipeline_tag	tags	base_model
other	agni-community	https://huggingface.co/laabamone/laabam-ai-3b-v1-gguf/blob/main/LICENSE-AGNI-COMMUNITY.md	en hi te kn ta	gguf	text-generation	agni agni-lite laabam-ai multilingual indic llama-cpp quantized conversational code tamil hindi telugu kannada	Qwen/Qwen2.5-3B-Instruct

🔥 Agni Lite 3B v1 — GGUF

Quantized for Ollama, llama.cpp, and LM Studio

Run Agni on your laptop, phone, or edge device

Full Model (Safetensors) • LoRA Adapter • Website

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Available Files

File	Quantization	Size	Description	Recommendation
laabam-ai-3b-v1-Q4_K_M.gguf	Q4_K_M	1.8 GB	4-bit quantized (medium)	✅ Recommended — best balance
laabam-ai-3b-v1-bf16.gguf	BF16	5.8 GB	Full precision	Maximum quality, needs 8GB+ RAM

Which file should I use?

• Q4_K_M (1.8 GB) — Best for most users. Runs on 4GB+ RAM. Minimal quality loss.
• BF16 (5.8 GB) — Full precision. Use when you need maximum accuracy.

Quick Start

Ollama

# Download the model
huggingface-cli download laabamone/laabam-ai-3b-v1-gguf \
  laabam-ai-3b-v1-Q4_K_M.gguf --local-dir .

# Create a Modelfile
cat > Modelfile << 'EOF'
FROM ./laabam-ai-3b-v1-Q4_K_M.gguf

TEMPLATE """<|im_start|>system
{{ .System }}<|im_end|>
<|im_start|>user
{{ .Prompt }}<|im_end|>
<|im_start|>assistant
"""

SYSTEM "You are Agni, a helpful multilingual AI assistant created by Laabam One Business Solutions. You support Hindi, Tamil, Telugu, Kannada, and English."

PARAMETER temperature 0.7
PARAMETER top_p 0.9
PARAMETER stop "<|im_end|>"
EOF

# Create and run
ollama create agni-lite -f Modelfile
ollama run agni-lite "Write hello world in Python"

llama.cpp

./llama-cli -m laabam-ai-3b-v1-Q4_K_M.gguf \
  -p "You are Agni, a helpful assistant.\n\nUser: Tell me about Tamil Nadu.\nAssistant:" \
  -n 256 --temp 0.7

LM Studio

1. Download laabam-ai-3b-v1-Q4_K_M.gguf
2. Place in your LM Studio models directory
3. Load and chat

Python (llama-cpp-python)

from llama_cpp import Llama

llm = Llama(
    model_path="laabam-ai-3b-v1-Q4_K_M.gguf",
    n_ctx=1024,
    n_threads=8,
)

output = llm.create_chat_completion(messages=[
    {"role": "system", "content": "You are Agni, a helpful multilingual AI assistant."},
    {"role": "user", "content": "भारत के बारे में बताओ।"}
])

print(output["choices"][0]["message"]["content"])

Tamil Example

output = llm.create_chat_completion(messages=[
    {"role": "system", "content": "நீங்கள் அக்னி AI. தமிழில் பதிலளிக்கவும்."},
    {"role": "user", "content": "சிலப்பதிகாரம் பற்றி சொல்லுங்கள்."}
])
print(output["choices"][0]["message"]["content"])

Model Details

Detail	Value
Model Name	Agni Lite 3B v1
Developer	Laabam One Business Solutions Pvt Ltd
Architecture	Qwen2.5 (3B parameters)
Training Method	QLoRA (r=16, alpha=32, 4-bit NF4) with Unsloth
Training Data	~98,000 curated multilingual samples
Quantization Tool	llama.cpp (convert + quantize)
License	Agni Community License

Capabilities

• 🇮🇳 Indian Languages — Native Hindi, Tamil, Telugu, Kannada support
• 💻 Coding — Python, JavaScript, Bash, SQL, and more
• 🧠 Reasoning — Math, logic, and step-by-step problem solving
• 🤖 Agentic — Function calling and tool use
• 🏢 Business — Customer support, ERP, and automation

Limitations

• 3B parameter model — may struggle with very complex multi-step reasoning
• Quantized versions have slight quality reduction vs full-precision
• Indian language quality is improving; English is currently stronger
• May generate incorrect or fabricated information
• Not a substitute for professional medical, legal, or financial advice

Citation

@misc{agni-lite-3b-v1-gguf,
  title={Agni Lite 3B v1: India's Multilingual AI Assistant (GGUF)},
  author={Laabam One Business Solutions Pvt Ltd},
  year={2026},
  url={https://huggingface.co/laabamone/laabam-ai-3b-v1-gguf}
}

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Built with 🔥 in India by Laabam One Business Solutions Pvt Ltd

Website • Contact