Instructions to use teclabs/Llama-3.1-8b-Instruct-Ind-Tax-Act-1961 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use teclabs/Llama-3.1-8b-Instruct-Ind-Tax-Act-1961 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 teclabs/Llama-3.1-8b-Instruct-Ind-Tax-Act-1961:Q4_K_M # Run inference directly in the terminal: llama cli -hf teclabs/Llama-3.1-8b-Instruct-Ind-Tax-Act-1961:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf teclabs/Llama-3.1-8b-Instruct-Ind-Tax-Act-1961:Q4_K_M # Run inference directly in the terminal: llama cli -hf teclabs/Llama-3.1-8b-Instruct-Ind-Tax-Act-1961: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 teclabs/Llama-3.1-8b-Instruct-Ind-Tax-Act-1961:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf teclabs/Llama-3.1-8b-Instruct-Ind-Tax-Act-1961: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 teclabs/Llama-3.1-8b-Instruct-Ind-Tax-Act-1961:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf teclabs/Llama-3.1-8b-Instruct-Ind-Tax-Act-1961:Q4_K_M
Use Docker
docker model run hf.co/teclabs/Llama-3.1-8b-Instruct-Ind-Tax-Act-1961:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use teclabs/Llama-3.1-8b-Instruct-Ind-Tax-Act-1961 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "teclabs/Llama-3.1-8b-Instruct-Ind-Tax-Act-1961" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "teclabs/Llama-3.1-8b-Instruct-Ind-Tax-Act-1961", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/teclabs/Llama-3.1-8b-Instruct-Ind-Tax-Act-1961:Q4_K_M
- Ollama
How to use teclabs/Llama-3.1-8b-Instruct-Ind-Tax-Act-1961 with Ollama:
ollama run hf.co/teclabs/Llama-3.1-8b-Instruct-Ind-Tax-Act-1961:Q4_K_M
- Unsloth Studio
How to use teclabs/Llama-3.1-8b-Instruct-Ind-Tax-Act-1961 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 teclabs/Llama-3.1-8b-Instruct-Ind-Tax-Act-1961 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 teclabs/Llama-3.1-8b-Instruct-Ind-Tax-Act-1961 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for teclabs/Llama-3.1-8b-Instruct-Ind-Tax-Act-1961 to start chatting
- Pi
How to use teclabs/Llama-3.1-8b-Instruct-Ind-Tax-Act-1961 with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf teclabs/Llama-3.1-8b-Instruct-Ind-Tax-Act-1961: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": "teclabs/Llama-3.1-8b-Instruct-Ind-Tax-Act-1961:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use teclabs/Llama-3.1-8b-Instruct-Ind-Tax-Act-1961 with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf teclabs/Llama-3.1-8b-Instruct-Ind-Tax-Act-1961: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 teclabs/Llama-3.1-8b-Instruct-Ind-Tax-Act-1961:Q4_K_M
Run Hermes
hermes
- Atomic Chat new
- OpenClaw new
How to use teclabs/Llama-3.1-8b-Instruct-Ind-Tax-Act-1961 with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf teclabs/Llama-3.1-8b-Instruct-Ind-Tax-Act-1961: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 "teclabs/Llama-3.1-8b-Instruct-Ind-Tax-Act-1961: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 teclabs/Llama-3.1-8b-Instruct-Ind-Tax-Act-1961 with Docker Model Runner:
docker model run hf.co/teclabs/Llama-3.1-8b-Instruct-Ind-Tax-Act-1961:Q4_K_M
- Lemonade
How to use teclabs/Llama-3.1-8b-Instruct-Ind-Tax-Act-1961 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull teclabs/Llama-3.1-8b-Instruct-Ind-Tax-Act-1961:Q4_K_M
Run and chat with the model
lemonade run user.Llama-3.1-8b-Instruct-Ind-Tax-Act-1961-Q4_K_M
List all available models
lemonade list
Llama 3.1 8B - Indian Income Tax Act 1961 (GGUF)
Fine-tuned Llama 3.1 8B Instruct model specialized in the Indian Income Tax Act 1961. Optimized for tax law queries, compliance questions, and section references.
🎯 Model Details
- Base Model: meta-llama/Meta-Llama-3.1-8B-Instruct
- Fine-tuning Method: LoRA (r=64, alpha=64) with Unsloth
- Training Date: 20251201
- Context Length: 4096 tokens
- Format: GGUF (ready for llama.cpp, Ollama, LM Studio, Jan, etc.)
- Specialization: Indian Income Tax Act 1961
📦 Available Quantizations
| File | Size | Use Case | RAM Required | Quality |
|---|---|---|---|---|
q4_k_m |
~4.5GB | Recommended - Best balance | 6-8GB | ⭐⭐⭐⭐ |
q5_k_m |
~5.5GB | High quality responses | 8-10GB | ⭐⭐⭐⭐⭐ |
q8_0 |
~8GB | Near-original quality | 10-12GB | ⭐⭐⭐⭐⭐ |
f16 |
~15GB | Maximum quality (if available) | 18-20GB | ⭐⭐⭐⭐⭐ |
Quantization Guide
- q4_k_m: Best for most users - good quality, reasonable size
- q5_k_m: Better quality with slight size increase
- q8_0: Minimal quality loss, larger file
- f16: Full precision, largest file
🚀 Quick Start
Using Ollama
Download the model file (e.g., q4_k_m) wget https://huggingface.co/teclabs/Llama-3.1-8b-Instruct-Ind-Tax-Act-1961/resolve/main/llama-tax-act-q4_k_m.gguf
Create Modelfile cat > Modelfile << 'EOF' FROM ./llama-tax-act-q4_k_m.gguf
PARAMETER temperature 0.7 PARAMETER top_p 0.9 PARAMETER top_k 40 PARAMETER repeat_penalty 1.1
SYSTEM """You are an expert on the Indian Income Tax Act 1961. Provide accurate, detailed information about tax regulations, exemptions, deductions, and compliance requirements. Always cite relevant sections when applicable.""" EOF
Create the model ollama create llama-tax-act -f Modelfile
Run it ollama run llama-tax-act
Using llama.cpp
Download model wget https://huggingface.co/teclabs/Llama-3.1-8b-Instruct-Ind-Tax-Act-1961/resolve/main/llama-tax-act-q4_k_m.gguf
Run inference ./llama-cli -m llama-tax-act-q4_k_m.gguf -p "Explain Section 80C deductions:" -n 512
Download model wget https://huggingface.co/teclabs/Llama-3.1-8b-Instruct-Ind-Tax-Act-1961/resolve/main/llama-tax-act-q4_k_m.gguf
Run inference ./llama-cli -m llama-tax-act-q4_k_m.gguf -p "Explain Section 80C deductions:" -n 512
from llama_cpp import Llama
Load model llm = Llama( model_path="./llama-tax-act-q4_k_m.gguf", n_ctx=4096, n_threads=8, n_gpu_layers=35 # Adjust based on your GPU )
Generate response output = llm( "What are the tax implications under Section 54?", max_tokens=512, temperature=0.7, top_p=0.9, )
print(output['choices']['text'])
💡 Example Queries
Q: What are the deductions available under Chapter VI-A? Q: Explain Section 80C and its limit for FY 2023-24 Q: What is the difference between Section 80C and 80D? Q: How is capital gains tax calculated under Section 112A? Q: What are the exemptions available under Section 10?
📊 Training Details
- Training Hardware: NVIDIA A100 80GB
- Training Time: ~7 minutes (including quantization)
- Dataset: Custom corpus from Income Tax Act 1961
- Epochs: Optimized for convergence
- Learning Rate: 2e-4 with cosine schedule
- Precision: BF16 training, quantized for deployment
⚙️ Technical Specifications
- Architecture: Llama 3.1 (8B parameters)
- Vocabulary: 128,256 tokens
- Max Context: 4096 tokens
- Attention: Grouped-Query Attention (GQA)
- Activation: SwiGLU
- LoRA Rank: 64 (higher than standard for better quality)
📈 Performance
- Inference Speed (q4_k_m on RTX 3090): ~40-50 tokens/sec
- Inference Speed (q4_k_m on M1 Max): ~25-35 tokens/sec
- Quality: Specialized responses with section references
⚠️ Limitations
- Trained on Income Tax Act 1961 as of training date (20251201)
- May not reflect latest amendments after this date
- Should be used as reference only, not legal advice
- Always verify with official sources
📜 License
This model inherits the Llama 3.1 Community License.
🙏 Acknowledgments
- Meta AI for Llama 3.1 base model
- Unsloth AI for efficient fine-tuning framework
- ggerganov for llama.cpp and GGUF format
📧 Contact
For questions or issues, please open an issue on the repository.
🔄 Updates
2025-12-01: Initial release with q4_k_m, q5_k_m, q8_0 quantizations
Disclaimer: This model is for educational and research purposes. Tax laws are complex and subject to change. Always consult qualified tax professionals for advice.
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Model tree for teclabs/Llama-3.1-8b-Instruct-Ind-Tax-Act-1961
Base model
meta-llama/Llama-3.1-8B