---
base_model:
- unsloth/Meta-Llama-3.1-8B
model-index:
- name: Llama-3.1-8B-Experimental-1206-Instruct
  results:
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: IFEval (0-Shot)
      type: HuggingFaceH4/ifeval
      args:
        num_few_shot: 0
    metrics:
    - type: inst_level_strict_acc and prompt_level_strict_acc
      value: 69.67
      name: strict accuracy
    source:
      url: https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=sethuiyer/Llama-3.1-8B-Experimental-1206-Instruct
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: BBH (3-Shot)
      type: BBH
      args:
        num_few_shot: 3
    metrics:
    - type: acc_norm
      value: 30.06
      name: normalized accuracy
    source:
      url: https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=sethuiyer/Llama-3.1-8B-Experimental-1206-Instruct
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MATH Lvl 5 (4-Shot)
      type: hendrycks/competition_math
      args:
        num_few_shot: 4
    metrics:
    - type: exact_match
      value: 11.1
      name: exact match
    source:
      url: https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=sethuiyer/Llama-3.1-8B-Experimental-1206-Instruct
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: GPQA (0-shot)
      type: Idavidrein/gpqa
      args:
        num_few_shot: 0
    metrics:
    - type: acc_norm
      value: 6.6
      name: acc_norm
    source:
      url: https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=sethuiyer/Llama-3.1-8B-Experimental-1206-Instruct
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MuSR (0-shot)
      type: TAUR-Lab/MuSR
      args:
        num_few_shot: 0
    metrics:
    - type: acc_norm
      value: 8.5
      name: acc_norm
    source:
      url: https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=sethuiyer/Llama-3.1-8B-Experimental-1206-Instruct
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MMLU-PRO (5-shot)
      type: TIGER-Lab/MMLU-Pro
      config: main
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 28.1
      name: accuracy
    source:
      url: https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=sethuiyer/Llama-3.1-8B-Experimental-1206-Instruct
      name: Open LLM Leaderboard
---
# Llama 3.1 8B Experimental 1206

### 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)

|      Metric       |Value|
|-------------------|----:|
|Avg.               |25.67|
|IFEval (0-Shot)    |69.67|
|BBH (3-Shot)       |30.06|
|MATH Lvl 5 (4-Shot)|11.10|
|GPQA (0-shot)      | 6.60|
|MuSR (0-shot)      | 8.50|
|MMLU-PRO (5-shot)  |28.10|