sampluralis/llama-sft-proj-layers-shmid
The sampluralis/llama-sft-proj-layers-shmid model is a fine-tuned language model based on gshasiri/SmolLM3-Mid, developed by sampluralis. This model has been trained using the TRL library for supervised fine-tuning (SFT). It is designed for general text generation tasks, leveraging its fine-tuned capabilities to produce coherent and contextually relevant responses. Its primary application is in generating human-like text based on given prompts.
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Model Overview
The sampluralis/llama-sft-proj-layers-shmid is a supervised fine-tuned (SFT) language model, building upon the base architecture of gshasiri/SmolLM3-Mid. This model was developed by sampluralis and trained using the TRL (Transformers Reinforcement Learning) library, a framework for fine-tuning transformer models.
Key Capabilities
- Text Generation: Excels at generating human-like text based on user prompts.
- Fine-tuned Performance: Benefits from supervised fine-tuning to enhance its response quality and relevance.
- Ease of Use: Can be readily integrated into applications using the Hugging Face
transformerspipeline for text generation tasks.
Training Details
The model underwent a supervised fine-tuning (SFT) process. The training utilized specific framework versions:
- TRL: 0.28.0
- Transformers: 4.57.6
- Pytorch: 2.6.0+cu126
- Datasets: 4.6.0
- Tokenizers: 0.22.2
Good For
- Interactive Chatbots: Generating conversational responses.
- Content Creation: Assisting with drafting articles, stories, or other textual content.
- Question Answering: Providing detailed answers to open-ended questions.