YeonwooSung/

Llama-3.1-8B-OpenO1-SFT-v0.2

Key Features

1. Text Generation with CoT Reasoning:
   • Implements Chain-of-Thought (CoT) prompting for logical and step-by-step reasoning tasks.
2. Conversational AI:
   • Excels in generating context-aware and coherent responses in multi-turn conversations.
3. Supervised Fine-Tuning (SFT):
   • Optimized for open-domain tasks using the O1-OPEN/OpenO1-SFT dataset.
4. Multi-Purpose Functionality:
   • Supports a wide range of NLP tasks, including summarization, question answering, and text completion.
5. Scalable Sharded Architecture:
   • Model weights are distributed across four shards, ensuring efficient loading for large-scale applications.

Training Details

• Base Model: meta-llama/Llama-3.1-8B
• Finetuned Dataset: O1-OPEN/OpenO1-SFT
  • Dataset includes 77.7k fine-tuning samples, curated for instruction-based and open-domain tasks.
• Model Size:
  • 8 Billion parameters distributed over 4 shards for efficient deployment.

Applications

1. Chain-of-Thought (CoT) Reasoning:
   • Solve complex problems step-by-step with logical reasoning capabilities.
2. Conversational Agents:
   • Ideal for chatbots, virtual assistants, and conversational systems.
3. Question Answering:
   • Answer open-domain or context-specific questions accurately.
4. Text Completion:
   • Generate coherent continuations for incomplete inputs.
5. Creative Writing:
   • Support for generating stories, articles, or brainstorming ideas.

Usage

Loading the Model

Inference Example

Expected Output

Performance Requirements

• Hardware:
  • High-performance GPUs are recommended for efficient inference.
  • Minimum memory: ~16GB VRAM for full precision; 8GB for quantized models.
• Optimization Options:
  • Use Safetensors for secure and efficient weight loading.
  • Quantization or model parallelism for resource-constrained environments.