初始化项目，由ModelHub XC社区提供模型

Model: aayanmishra-ml/Athena-1-3B
Source: Original Platform

README.md

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+---
+base_model: Qwen/Qwen2.5-3B-Instruct
+tags:
+- text-generation-inference
+- transformers
+- unsloth
+- qwen2
+- trl
+license: other
+license_name: qwen-research
+license_link: https://huggingface.co/Spestly/Athena-1-3B/blob/main/LICENSE
+language:
+- en
+---
+![Header](https://raw.githubusercontent.com/Aayan-Mishra/Images/refs/heads/main/Athena.png)
+
+# Athena-1 3B:
+
+Athena-1 3B is a fine-tuned, instruction-following large language model derived from [Qwen/Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct). It is designed to provide efficient, high-quality text generation while maintaining a compact size. Athena 3B is optimized for lightweight applications, conversational AI, and structured data tasks, making it ideal for real-world use cases where performance and resource efficiency are critical.
+
+---
+
+## Key Features
+
+### ⚡ Lightweight and Efficient
+- **Compact Size**: At just **3.09 billion parameters**, Athena-1 3B offers excellent performance with reduced computational requirements.
+- **Instruction Following**: Fine-tuned for precise and reliable adherence to user prompts.
+- **Coding and Mathematics**: Proficient in solving coding challenges and handling mathematical tasks.
+
+### 📖 Long-Context Understanding
+- **Context Length**: Supports up to **32,768 tokens**, enabling the processing of moderately lengthy documents or conversations.
+- **Token Generation**: Can generate up to **8K tokens** of output.
+
+### 🌍 Multilingual Support
+- Supports **29+ languages**, including:
+  - English, Chinese, French, Spanish, Portuguese, German, Italian, Russian
+  - Japanese, Korean, Vietnamese, Thai, Arabic, and more.
+
+### 📊 Structured Data & Outputs
+- **Structured Data Interpretation**: Processes structured formats like tables and JSON.
+- **Structured Output Generation**: Generates well-formatted outputs, including JSON and other structured formats.
+
+---
+
+## Model Details
+
+- **Base Model**: [Qwen/Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct)
+- **Architecture**: Transformers with RoPE, SwiGLU, RMSNorm, Attention QKV bias, and tied word embeddings.
+- **Parameters**: 3.09B total (2.77B non-embedding).
+- **Layers**: 36
+- **Attention Heads**: 16 for Q, 2 for KV.
+- **Context Length**: Up to **32,768 tokens**.
+
+---
+
+## Applications
+
+Athena 3B is designed for a variety of real-world applications:
+- **Conversational AI**: Build fast, responsive, and lightweight chatbots.
+- **Code Generation**: Generate, debug, or explain code snippets.
+- **Mathematical Problem Solving**: Assist with calculations and reasoning.
+- **Document Processing**: Summarize and analyze moderately large documents.
+- **Multilingual Applications**: Support for global use cases with diverse language requirements.
+- **Structured Data**: Process and generate structured data, such as tables and JSON.
+
+---
+
+## Quickstart
+
+Here’s how you can use Athena 3B for quick text generation:
+
+```python
+# Use a pipeline as a high-level helper
+from transformers import pipeline
+
+messages = [
+    {"role": "user", "content": "Who are you?"},
+]
+pipe = pipeline("text-generation", model="Spestly/Athena-1-3B")
+pipe(messages)
+
+# Load model directly
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("Spestly/Athena-1-3B")
+model = AutoModelForCausalLM.from_pretrained("Spestly/Athena-1-3B")
+```