sampluralis/llama-sft-masked
The sampluralis/llama-sft-masked model is a 1 billion parameter language model fine-tuned using TRL. This model is based on an unspecified Llama architecture and has been trained with Supervised Fine-Tuning (SFT) techniques. It is designed for text generation tasks, particularly conversational responses, and supports a context length of 32768 tokens.
Loading preview...
Model Overview
The sampluralis/llama-sft-masked is a 1 billion parameter language model developed by sampluralis. It has been fine-tuned using the TRL library, which specializes in Transformer Reinforcement Learning, though this specific model was trained with Supervised Fine-Tuning (SFT).
Key Capabilities
- Text Generation: The model is capable of generating coherent and contextually relevant text, as demonstrated by its quick start example for conversational prompts.
- Long Context Window: It supports a substantial context length of 32768 tokens, allowing it to process and generate longer sequences of text while maintaining context.
- TRL Framework: Built upon the TRL framework, indicating potential for further reinforcement learning applications or advanced fine-tuning techniques.
Training Details
The model underwent a Supervised Fine-Tuning (SFT) process. The training utilized specific versions of key frameworks:
- TRL: 0.28.0
- Transformers: 4.57.6
- PyTorch: 2.6.0+cu126
- Datasets: 4.6.0
- Tokenizers: 0.22.2
Use Cases
This model is suitable for applications requiring text generation, especially those benefiting from a large context window. Its fine-tuning approach suggests it can be adapted for various conversational AI tasks or content creation where specific response styles are desired.