lakshyaixi/Llama_3_2_3B_DPO_v20_on_v7_loopcleaned
TEXT GENERATIONConcurrent Unit Cost:1Model Size:3.2BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Jun 25, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold
lakshyaixi/Llama_3_2_3B_DPO_v20_on_v7_loopcleaned is a 3.2 billion parameter Llama 3 model developed by lakshyaixi, fine-tuned from lakshyaixi/Llama_3_2_3B_Conversational_v7_SFT_10voicebot_loopcleaned. This model was trained significantly faster using Unsloth and Huggingface's TRL library, making it efficient for deployment. It is designed for conversational applications, leveraging its DPO fine-tuning for improved dialogue quality.
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Model Overview
lakshyaixi/Llama_3_2_3B_DPO_v20_on_v7_loopcleaned is a 3.2 billion parameter language model developed by lakshyaixi. It is a fine-tuned variant of the Llama 3 architecture, specifically building upon the lakshyaixi/Llama_3_2_3B_Conversational_v7_SFT_10voicebot_loopcleaned model.
Key Characteristics
- Architecture: Llama 3 family.
- Parameter Count: 3.2 billion parameters.
- Training Efficiency: This model was trained approximately two times faster by utilizing the Unsloth library in conjunction with Huggingface's TRL (Transformer Reinforcement Learning) library. This approach optimizes the fine-tuning process, allowing for quicker iteration and deployment.
- Fine-tuning Method: The model has undergone DPO (Direct Preference Optimization) fine-tuning, indicating an emphasis on aligning its outputs with human preferences, particularly for conversational quality.
- Base Model: It is fine-tuned from a conversational SFT (Supervised Fine-Tuning) model, suggesting its core capabilities are rooted in dialogue generation.
Intended Use Cases
- Conversational AI: Given its DPO fine-tuning and conversational base, this model is well-suited for dialogue systems, chatbots, and interactive AI applications where natural and preferred responses are crucial.
- Efficient Deployment: The use of Unsloth for faster training makes this model potentially attractive for developers looking for efficient fine-tuning and deployment of Llama 3-based conversational agents.