mohit-1710/loomstack-qwen-sft-terminal
The mohit-1710/loomstack-qwen-sft-terminal is a 2 billion parameter causal language model, fine-tuned from mohit-1710/loomstack-qwen-sft-compact-v3 using the TRL library. With a context length of 32768 tokens, this model is specifically optimized for instruction-following tasks, making it suitable for terminal-like interactions and generating responses to user prompts. Its fine-tuning process focuses on supervised fine-tuning (SFT) to enhance its conversational and interactive capabilities.
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Model Overview
The mohit-1710/loomstack-qwen-sft-terminal is a 2 billion parameter instruction-tuned language model, building upon the mohit-1710/loomstack-qwen-sft-compact-v3 base. It has been fine-tuned using the TRL (Transformer Reinforcement Learning) library, specifically employing Supervised Fine-Tuning (SFT) techniques.
Key Capabilities
- Instruction Following: Designed to understand and respond to user instructions effectively, making it suitable for interactive applications.
- Conversational AI: Optimized for generating coherent and contextually relevant text in response to prompts, mimicking terminal-like interactions.
- Efficient Fine-tuning: Leverages the TRL framework for its training, indicating a focus on robust and scalable fine-tuning methodologies.
Training Details
The model's training procedure involved SFT, utilizing 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
This model is ideal for developers looking for a compact yet capable instruction-tuned model for various text generation and conversational AI tasks, particularly where a smaller footprint and efficient performance are desired.