semvec/pypi-packages huggingface dataset:
surfgram 1.0.1
Modern asynchronous Telegram bot framework
License: MIT
Requires Python: <4.0,>=3.9
Keywords: telegram, bot, framework, async, aiohttp, telegram-api, python-telegram-bot

<div align="center" style="max-width: 100%; overflow-x: auto; margin: 0 auto;">
  <div style="text-align: center; padding: 0 10px; width: 100%;">
    <h1 style="font-size: clamp(24px, 5vw, 36px); margin: 0 auto;">Surfgram</h1>
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  <div style="display: flex; justify-content: center; margin: 20px auto; width: 100%;">
    <img src="./assets/surfgram_logo.svg" alt="Surfgram Logo" style="max-width: 100%; height: auto; display: block;">
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  <div style="text-align: center; margin: 20px auto; width: 100%;">
    <div style="display: inline-flex; flex-wrap: wrap; justify-content: center; gap: 8px;">
      <img src="https://img.shields.io/pypi/v/surfgram?color=blue&label=Latest%20Version" alt="PyPI Version">
      <img src="https://img.shields.io/badge/Python-3.8%2B-blue" alt="Python 3.8+">
      <img src="https://img.shields.io/badge/Telegram%20Bot%20API-9.0+-blue" alt="Telegram Bot API 9.0+">
      <img src="https://img.shields.io/badge/License-MIT-green" alt="MIT License">
    </div>
  </div>
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## Overview

Surfgram is a high-level, object-oriented framework for building scalable Telegram bots with minimal boilerplate. It's designed for developers who want a clean architecture without sacrificing flexibility.

## Key Features

- ✅ Pure OOP – Classes and dependency injection
- ✅ Fast Setup – Get a bot running in under 20 lines of code
- ✅ Extensible – Add custom handlers, filters, and database integrations
- ✅ Async-Ready – Built with asyncio for high performance
- ✅ Full support for Telegram Bot API 9.0+

## Quick Start

1. Install using PIP:

    ```bash
    pip install surfgram
    ```

2. Create your first bot:

    ```bash
    surfgram new mybot
    cd mybot
    surfgram run
    ```

## Example bot's handler
```python
from surfgram import APIObject, Bot
from surfgram.types import BotCommand
from typing import Callable, List

class StartCommand(BotCommand):
    @property
    def __names__(self) -> List[str]:
        return ["start"]

    @property
    def __callback__(self) -> Callable:
        return self.handle

    async def handle(
        self,
        update: APIObject,
        bot: Bot
    ) -> None:
        await bot.send_message(
            chat_id=update.message.chat.id,
            text="Hello, world!"
        )
```

## Documentation

📖 [Full documentation](https://github.com/surfgram/surfgram-docs)  
📚 [Examples](https://github.com/surfgram/surfgram-examples)

## Contributing

We welcome contributions! Please read our [Contributing Guidelines](https://github.com/surfgram/surfgram/blob/main/CONTRIBUTING.md) for more information on how to get started.

## Support

🐞 [Report a bug](https://github.com/surfgram/surfgram/issues)  
💡 [Start a discussion](https://github.com/surfgram/surfgram/discussions)  
📮 [Join our Telegram Channel](https://t.me/the_surfgram)

## License

MIT License. See [LICENSE](https://github.com/surfgram/surfgram/blob/main/LICENSE) for more details.
autofeedback2pr 0.2.0
Auto feedback classifier and grader for developer workflow.
License: MIT
Requires Python: >=3.7

# autofeedback2pr
        
        This is a python package which can integrate in the workflow to use Claude API to evalute the difficulty of the feedback. 
        
        ## Installation
        
        ```bash
        pip install .
        ```
        
        ## How to use it
        
        ```python
        import autofeedback2pr
        
        autofeedback2pr.config(api_key="YOUR_CLAUDE_KEY")
        result = autofeedback2pr.grade_feedback("The button does not work on mobile.", lang="en")
        print(result)
        ```
        
        - Claude API Key can be used from `autofeedback2pr.config(api_key=...)` or set in as env variable `CLAUDE_API_KEY`。
synth-graph 0.1.0
Modern graph orchestration library for building stateful, multi-actor applications with LLMs
License: MIT
Requires Python: >=3.8
Keywords: graph, orchestration, workflow, llm, agents, synth

# synth-graph

Modern graph orchestration library for building stateful, multi-actor applications with LLMs.

Part of the Synth AI ecosystem.

## Features

- 🔄 Stateful graph orchestration
- 🎭 Multi-actor coordination  
- 🧠 LLM-native workflows
- 📊 Built-in observability
- ⚡ Async-first design
- 🔗 Seamless integration with Synth AI tools

## Installation

```bash
pip install synth-graph
```

## Quick Start

```python
from synth_graph import Graph, Node

# Create a graph
g = Graph()

# Add nodes
g.add_node("start", Node(fn=lambda x: x))
g.add_node("process", Node(fn=lambda x: x * 2))
g.add_node("end", Node(fn=lambda x: x))

# Connect nodes
g.add_edge("start", "process")
g.add_edge("process", "end")

# Execute
result = g.run(input_data=5)
print(result)  # 10
```

## Related Packages

- `finetuning` - Fine-tuning library for LLMs
- `synth-ai` - Main Synth AI SDK

## License

MIT
finetuning 0.1.0
Modern fine-tuning library for LLMs with LoRA, QLoRA, and full fine-tuning support
License: MIT
Requires Python: >=3.8
Keywords: machine-learning, deep-learning, transformers, fine-tuning, lora, llm

# finetuning

Modern fine-tuning library for Large Language Models (LLMs) with support for LoRA, QLoRA, and full fine-tuning.

## Features

- 🚀 Easy-to-use API for fine-tuning LLMs
- 🔧 Support for LoRA and QLoRA parameter-efficient methods  
- 📊 Built-in evaluation and metrics
- ☁️ Cloud-native with Modal integration
- 🔄 Compatible with HuggingFace models

## Installation

```bash
pip install finetuning
```

## Quick Start

```python
from finetuning import FineTuner

# Initialize fine-tuner
tuner = FineTuner(model_name="meta-llama/Llama-2-7b-hf")

# Fine-tune on your data
tuner.train(
    dataset="your_dataset",
    method="lora",  # or "qlora", "full"
    num_epochs=3
)

# Save fine-tuned model
tuner.save("./my_finetuned_model")
```

## License

MIT