Instructions to use ShakhawatShanin/Bangla-Text-Summarization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ShakhawatShanin/Bangla-Text-Summarization with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="ShakhawatShanin/Bangla-Text-Summarization",
	filename="Bangla_Text_Summarization/bangla_summarization.Q8_0.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use ShakhawatShanin/Bangla-Text-Summarization with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf ShakhawatShanin/Bangla-Text-Summarization:Q8_0
# Run inference directly in the terminal:
llama-cli -hf ShakhawatShanin/Bangla-Text-Summarization:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf ShakhawatShanin/Bangla-Text-Summarization:Q8_0
# Run inference directly in the terminal:
llama-cli -hf ShakhawatShanin/Bangla-Text-Summarization:Q8_0

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 ShakhawatShanin/Bangla-Text-Summarization:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf ShakhawatShanin/Bangla-Text-Summarization:Q8_0

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 ShakhawatShanin/Bangla-Text-Summarization:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ShakhawatShanin/Bangla-Text-Summarization:Q8_0

Use Docker

docker model run hf.co/ShakhawatShanin/Bangla-Text-Summarization:Q8_0

LM Studio
Jan
Ollama
How to use ShakhawatShanin/Bangla-Text-Summarization with Ollama:
```
ollama run hf.co/ShakhawatShanin/Bangla-Text-Summarization:Q8_0
```

Unsloth Studio new

How to use ShakhawatShanin/Bangla-Text-Summarization 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 ShakhawatShanin/Bangla-Text-Summarization 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 ShakhawatShanin/Bangla-Text-Summarization to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ShakhawatShanin/Bangla-Text-Summarization to start chatting

Docker Model Runner
How to use ShakhawatShanin/Bangla-Text-Summarization with Docker Model Runner:
```
docker model run hf.co/ShakhawatShanin/Bangla-Text-Summarization:Q8_0
```

Lemonade

How to use ShakhawatShanin/Bangla-Text-Summarization with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ShakhawatShanin/Bangla-Text-Summarization:Q8_0

Run and chat with the model

lemonade run user.Bangla-Text-Summarization-Q8_0

List all available models

lemonade list

Bangla-Text-Summarization

File size: 786 Bytes

71db1c1

FROM /content/bangla_summarization_model_merged.Q8_0.gguf

# Set the model parameters
PARAMETER num_ctx 2048
PARAMETER num_batch 512
PARAMETER num_gpu 1

# Template for chat format
TEMPLATE """<start_of_turn>user
সংক্ষেপ করুন: {{ .Prompt }}<end_of_turn>
<start_of_turn>model
"""

# System prompt
SYSTEM """You are a helpful AI assistant specialized in summarizing Bengali text.
Your task is to create concise, accurate summaries of Bengali articles while preserving the key information and meaning.

Always respond in Bengali.
Keep summaries clear and to the point.
Focus on the main ideas and important details."""

# Model configuration
PARAMETER temperature 0.1
PARAMETER top_k 40
PARAMETER top_p 0.9
PARAMETER stop "<end_of_turn>"
PARAMETER repeat_penalty 1.1