shuoxing/llama3-8b-full-pretrain-control-tweet-1m-en-sft
The shuoxing/llama3-8b-full-pretrain-control-tweet-1m-en-sft model is an 8 billion parameter Llama 3 variant, fine-tuned from shuoxing/llama3-8b-full-pretrain-control-tweet-1m-en-reproduce-bs8. This model is specifically fine-tuned on the alpaca_en dataset, suggesting an optimization for instruction-following tasks in English. Its primary use case is likely general-purpose text generation and understanding, with a focus on conversational or instruction-based applications.
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Model Overview
This model, shuoxing/llama3-8b-full-pretrain-control-tweet-1m-en-sft, is an 8 billion parameter Llama 3-based language model. It is a fine-tuned iteration of the shuoxing/llama3-8b-full-pretrain-control-tweet-1m-en-reproduce-bs8 model, specifically adapted using the alpaca_en dataset. This fine-tuning process typically enhances a model's ability to follow instructions and engage in conversational exchanges.
Training Details
The model was trained with a learning rate of 1e-05 over 3.0 epochs, utilizing a cosine learning rate scheduler with a 0.1 warmup ratio. The training involved a multi-GPU setup with 8 devices, a train_batch_size of 8, and a gradient_accumulation_steps of 2, resulting in a total_train_batch_size of 128. The optimizer used was ADAMW_TORCH_FUSED.
Intended Uses
Given its fine-tuning on the alpaca_en dataset, this model is likely well-suited for:
- Instruction Following: Generating responses based on explicit instructions.
- Conversational AI: Engaging in dialogue and chat-like interactions.
- General Text Generation: Creating coherent and contextually relevant text in English.
Limitations
As noted in the original model card, more information is needed regarding specific intended uses and limitations. Users should perform their own evaluations for specific applications.