Instructions to use ShakhawatShanin/Bangla-Text-Summarization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
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
File size: 608 Bytes
71db1c1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 | from flask import Flask, render_template, request
import ollama
app = Flask(__name__)
MODEL_NAME = "bts"
@app.route("/")
def index():
return render_template("index.html")
@app.route("/get", methods=["POST"])
def chat():
user_message = request.form["msg"]
try:
response = ollama.chat(
model=MODEL_NAME,
messages=[{"role": "user", "content": user_message}]
)
reply = response['message']['content']
except Exception as e:
reply = f"Error: {str(e)}"
return reply
if __name__ == "__main__":
app.run(host="0.0.0.0", port=5000)
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