---
license: llama3.1
model-index:
- name: Llama-Spark
  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: 79.11
      name: strict accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=arcee-ai/Llama-Spark
      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: 29.77
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=arcee-ai/Llama-Spark
      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: 1.06
      name: exact match
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=arcee-ai/Llama-Spark
      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=arcee-ai/Llama-Spark
      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: 2.62
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=arcee-ai/Llama-Spark
      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: 30.23
      name: accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=arcee-ai/Llama-Spark
      name: Open LLM Leaderboard
---
<div align="center">
  <img src="https://i.ibb.co/9hwFrvL/BLMs-Wkx-NQf-W-46-FZDg-ILhg.jpg" alt="Arcee Spark" style="border-radius: 10px; box-shadow: 0 4px 8px 0 rgba(0, 0, 0, 0.2), 0 6px 20px 0 rgba(0, 0, 0, 0.19); max-width: 100%; height: auto;">
</div>


Llama-Spark is a powerful conversational AI model developed by Arcee.ai. It's built on the foundation of Llama-3.1-8B and merges the power of our Tome Dataset with Llama-3.1-8B-Instruct, resulting in a remarkable conversationalist that punches well above its 8B parameter weight class.

## GGUFs available [here](https://huggingface.co/arcee-ai/Llama-Spark-GGUF)

## Model Description

Llama-Spark is our commitment to consistently delivering the best-performing conversational AI in the 6-9B parameter range. As new base models become available, we'll continue to update and improve Spark to maintain its leadership position. 

This model is a successor to our original Arcee-Spark, incorporating advancements and learnings from our ongoing research and development.

## Intended Uses

Llama-Spark is intended for use in conversational AI applications, such as chatbots, virtual assistants, and dialogue systems. It excels at engaging in natural and informative conversations.

## Training Information

Llama-Spark is built upon the Llama-3.1-8B base model, fine-tuned using of the Tome Dataset and merged with Llama-3.1-8B-Instruct.
## Evaluation Results
Please note that these scores are consistantly higher than the OpenLLM leaderboard, and should be compared to their relative performance increase not weighed against the leaderboard.
<div align="center">
  <img src="https://i.ibb.co/pfSGLtB/Screenshot-2024-08-01-at-11-40-42-PM.png" alt="Arcee Spark" style="border-radius: 10px; box-shadow: 0 4px 8px 0 rgba(0, 0, 0, 0.2), 0 6px 20px 0 rgba(0, 0, 0, 0.19); max-width: 100%; height: auto;">
</div>

## Acknowledgements

We extend our deepest gratitude to **PrimeIntellect** for being our compute sponsor for this project. 

# [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/details_arcee-ai__Llama-Spark)

|      Metric       |Value|
|-------------------|----:|
|Avg.               |24.90|
|IFEval (0-Shot)    |79.11|
|BBH (3-Shot)       |29.77|
|MATH Lvl 5 (4-Shot)| 1.06|
|GPQA (0-shot)      | 6.60|
|MuSR (0-shot)      | 2.62|
|MMLU-PRO (5-shot)  |30.23|