kmseong/llama2_7b-chat-gsm8k-wsr-lora-elem-kr0.1-r16-lr2e-4
The kmseong/llama2_7b-chat-gsm8k-wsr-lora-elem-kr0.1-r16-lr2e-4 is a 7 billion parameter language model, likely fine-tuned from a Llama 2 base, with a context length of 4096 tokens. This model appears to be a specialized variant, potentially optimized for specific tasks or datasets as indicated by its detailed naming convention (gsm8k, wsr, lora, elem, kr0.1). Its primary use case would depend on the specific fine-tuning objectives, which are not explicitly detailed in the provided README.
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Model Overview
This model, kmseong/llama2_7b-chat-gsm8k-wsr-lora-elem-kr0.1-r16-lr2e-4, is a 7 billion parameter language model. While specific details regarding its development, training data, and precise fine-tuning objectives are marked as "More Information Needed" in the provided model card, its naming convention suggests a specialized fine-tuning process. The llama2_7b-chat prefix indicates it is likely based on the Llama 2 7B Chat model, and elements like gsm8k (a mathematical reasoning dataset), wsr, lora (a fine-tuning technique), elem, and kr0.1-r16-lr2e-4 point towards a targeted optimization for particular tasks or domains.
Key Characteristics
- Base Model: Likely Llama 2 7B Chat.
- Parameter Count: 7 billion parameters.
- Context Length: 4096 tokens.
- Fine-tuning: Implies LoRA (Low-Rank Adaptation) fine-tuning, potentially on datasets related to mathematical reasoning (GSM8K) and other specific tasks.
Potential Use Cases
Given the lack of explicit information, potential use cases are inferred from the model's name:
- Mathematical Reasoning: The inclusion of
gsm8ksuggests potential strengths in solving grade school math problems. - Chat Applications: As a derivative of a Llama 2 Chat model, it may retain general conversational abilities.
- Specialized Tasks: The other specific tags (
wsr,elem,kr0.1-r16-lr2e-4) indicate it might be tailored for niche applications, though these are not detailed.