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---
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|
|