Itachi-42/loomstack-qwen-4b-sft-terminal
Itachi-42/loomstack-qwen-4b-sft-terminal is a 4 billion parameter causal language model, fine-tuned from Itachi-42/loomstack-qwen-4b-sft-compact using SFT. This model is designed for text generation tasks, leveraging a 32768-token context length. It is optimized for conversational AI and question-answering scenarios, providing coherent and contextually relevant responses.
Loading preview...
Model Overview
Itachi-42/loomstack-qwen-4b-sft-terminal is a 4 billion parameter language model, fine-tuned from the base model Itachi-42/loomstack-qwen-4b-sft-compact. This model was developed by Itachi-42 and trained using the Supervised Fine-Tuning (SFT) method with the TRL library.
Key Capabilities
- Text Generation: Capable of generating coherent and contextually relevant text based on user prompts.
- Conversational AI: Designed to handle interactive dialogue, making it suitable for chatbots and virtual assistants.
- Question Answering: Excels at providing detailed responses to open-ended questions.
- Extended Context: Supports a substantial context length of 32768 tokens, allowing for processing and generating longer sequences of text.
Training Details
The model underwent a Supervised Fine-Tuning (SFT) process. The training utilized specific versions of key frameworks:
- TRL: 0.24.0
- Transformers: 5.5.0
- Pytorch: 2.10.0
- Datasets: 4.3.0
- Tokenizers: 0.22.2
Good For
- Developing conversational agents and chatbots.
- Generating creative content or detailed responses in interactive applications.
- Applications requiring understanding and generation over long contexts.