mohit-1710/loomstack-qwen-sft-compact
mohit-1710/loomstack-qwen-sft-compact is a 2 billion parameter causal language model, fine-tuned from mohit-1710/loomstack-qwen-sft-prompted using Supervised Fine-Tuning (SFT) with TRL. This model is designed for text generation tasks, offering a compact solution with a 32768 token context length. It is optimized for generating coherent and contextually relevant responses based on user prompts.
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Model Overview
mohit-1710/loomstack-qwen-sft-compact is a 2 billion parameter language model, representing a fine-tuned iteration of the mohit-1710/loomstack-qwen-sft-prompted base model. This compact model was developed using Supervised Fine-Tuning (SFT) techniques, leveraging the TRL library for its training procedure.
Key Capabilities
- Text Generation: Primarily designed for generating human-like text based on given prompts.
- Context Handling: Supports a substantial context window of 32768 tokens, allowing for processing and generating longer sequences of text.
- Instruction Following: As an SFT model, it is expected to follow instructions provided in the input prompt effectively.
Training Details
The model's training involved the use of SFT, indicating a focus on aligning its outputs with specific task-oriented instructions or desired response formats. The development environment included:
- TRL: 0.24.0
- Transformers: 5.5.0
- Pytorch: 2.10.0
- Datasets: 4.3.0
- Tokenizers: 0.22.2
Use Cases
This model is suitable for applications requiring efficient text generation from a compact model, such as chatbots, content creation, or summarization tasks where a balance between performance and resource usage is critical.