Instructions to use Crossberry/tamila with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Crossberry/tamila with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Crossberry/tamila",
	filename="tamila (1).gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Crossberry/tamila with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Crossberry/tamila
# Run inference directly in the terminal:
llama-cli -hf Crossberry/tamila

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Crossberry/tamila
# Run inference directly in the terminal:
llama-cli -hf Crossberry/tamila

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 Crossberry/tamila
# Run inference directly in the terminal:
./llama-cli -hf Crossberry/tamila

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 Crossberry/tamila
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Crossberry/tamila

Use Docker

docker model run hf.co/Crossberry/tamila

LM Studio
Jan
Ollama
How to use Crossberry/tamila with Ollama:
```
ollama run hf.co/Crossberry/tamila
```

Unsloth Studio

How to use Crossberry/tamila 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 Crossberry/tamila 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 Crossberry/tamila to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Crossberry/tamila to start chatting

Docker Model Runner
How to use Crossberry/tamila with Docker Model Runner:
```
docker model run hf.co/Crossberry/tamila
```

Lemonade

How to use Crossberry/tamila with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Crossberry/tamila

Run and chat with the model

lemonade run user.tamila-{{QUANT_TAG}}

List all available models

lemonade list

tamila

File size: 1,586 Bytes


---
language:
- ta
- en
license: apache-2.0
library_name: llama.cpp
tags:
- tamil
- bilingual
- nlp
- classification
- crossberryweb
- gguf
# Specific tag to enable Inference API for GGUF
extra_gated_heading: "Tamila Model Access"
extra_gated_button_content: "Acknowledge"
---

# 🚀 Tamila Master v0.3

**Created by [crossberryweb](https://huggingface.co/crossberryweb)**

Tamila is a high-performance bilingual model (Tamil/English) trained on a massive global corpus of over 2.2 million segments.

## 🔗 Project Links
- **Live Demo (HF Space):** [https://huggingface.co/spaces/Crossberry/tamila-test-app](https://huggingface.co/spaces/Crossberry/tamila-test-app)
- **Web Deployment:** [crossberry.vercel.app](https://crossberry.vercel.app)
- **Dataset Repository:** [Hugging Face Tamila](https://huggingface.co/datasets/crossberryweb/tamila)

## 📊 Model Benchmarks
| Task | Dataset | Accuracy | Loss |
| :--- | :--- | :--- | :--- |
| Global Corpus Tuning | 2.2M Segments | 1.0000 | 6.64e-10 |
| Literature (Thirukkural) | Kaggle NLP | 0.9868 | 0.0612 |
| Technical (Kimi K2) | PDF Extract | 1.0000 | 1.17e-06 |

## 🛠 Future Roadmap
- [ ] Integration with advanced Transformer architectures.
- [ ] Expanded support for regional Tamil dialects.
- [ ] Real-time API integration for mobile applications.

## 📖 More Info
This model utilizes a custom MLP architecture optimized for GGUF deployment. It categorizes text into four primary contexts: History/Literature, Technical/AI, Tanglish, and General Corpus.

---
*Developed for the open-source community by Crossberryweb.*