wassname/llama-3.2-3b-sft
The wassname/llama-3.2-3b-sft model is a fine-tuned version of tanliboy/Llama-3.2-3B, developed by wassname. This 3 billion parameter model is specifically trained using Supervised Fine-Tuning (SFT) on the wassname/ultrachat_200k_filtered dataset. It serves as an intermediate SFT model, making it suitable for subsequent DPO (Direct Preference Optimization) experiments. Its primary use case is as a foundational SFT layer for advanced alignment techniques.
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Model Overview
The wassname/llama-3.2-3b-sft model is a 3 billion parameter language model developed by wassname. It is a supervised fine-tuned (SFT) variant of the tanliboy/Llama-3.2-3B base model. The fine-tuning process utilized the wassname/ultrachat_200k_filtered dataset and was performed using the TRL (Transformer Reinforcement Learning) library.
Key Characteristics
- SFT Foundation: This model is explicitly designed as an SFT layer, which is a prerequisite for advanced alignment techniques like Direct Preference Optimization (DPO).
- Dataset: Fine-tuned on the
wassname/ultrachat_200k_filtereddataset, indicating a focus on conversational or instruction-following capabilities. - Training Framework: Developed using Hugging Face's TRL library, known for its tools in reinforcement learning from human feedback (RLHF) and alignment.
Intended Use Cases
- DPO Experiments: Ideal for researchers and developers looking to conduct DPO experiments, as it provides the necessary SFT baseline.
- Intermediate Alignment Step: Serves as a crucial intermediate model in the pipeline for training more advanced, preference-aligned language models.
- Instruction Following: Given its training on an UltraChat-derived dataset, it can be used for basic instruction-following tasks, though its primary purpose is as an SFT base.