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README.md

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 # Llama 3.1 8B Experimental 1206
 
-Overall Strengths
-Logical and Boolean Reasoning – Excels in tasks requiring clear, rule-based logic and manipulation of true/false statements.
-Focused Domain Knowledge – Strong at certain specialized tasks (sports rules, ruin names, hyperbaton) that blend world knowledge with language comprehension.
-Good Instruction Compliance – High prompt-level and instance-level accuracy (both strict and loose) indicate it follows user instructions effectively.
-Reasonable Multi-step Reasoning – While not top-tier across all logic tasks, it still shows solid performance in areas like disambiguation and causal reasoning (over 60%).
+### Overall Strengths
 
+1. **Logical and Boolean Reasoning** – Excels in tasks requiring clear, rule-based logic and manipulation of true/false statements.  
+2. **Focused Domain Knowledge** – Strong at certain specialized tasks (sports rules, ruin names, hyperbaton) that blend world knowledge with language comprehension.  
+3. **Good Instruction Compliance** – High prompt-level and instance-level accuracy (both strict and loose) indicate that it follows user instructions effectively, even in more complex or nuanced prompts.  
+4. **Reasonable Multi-step Reasoning** – While not the best in every logic category, it still shows solid performance (60%+) on tasks like disambiguation and causal reasoning.  
+5. **Extended Context Window (138k)** – The large 138k token context allows the model to handle lengthy inputs and maintain coherence across extensive passages or multi-turn conversations. This is especially valuable for tasks like long-document question answering, summarization, or complex scenario analysis where context retention is crucial.
 
 # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
 Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/sethuiyer__Llama-3.1-8B-Experimental-1206-Instruct-details)