---
license: mit
model-index:
- name: blockchainlabs_tinyllama_fusion_LHK_yunkong_v2
  results:
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: AI2 Reasoning Challenge (25-Shot)
      type: ai2_arc
      config: ARC-Challenge
      split: test
      args:
        num_few_shot: 25
    metrics:
    - type: acc_norm
      value: 34.9
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=alnrg2arg/blockchainlabs_tinyllama_fusion_LHK_yunkong_v2
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: HellaSwag (10-Shot)
      type: hellaswag
      split: validation
      args:
        num_few_shot: 10
    metrics:
    - type: acc_norm
      value: 63.11
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=alnrg2arg/blockchainlabs_tinyllama_fusion_LHK_yunkong_v2
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MMLU (5-Shot)
      type: cais/mmlu
      config: all
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 26.75
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=alnrg2arg/blockchainlabs_tinyllama_fusion_LHK_yunkong_v2
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: TruthfulQA (0-shot)
      type: truthful_qa
      config: multiple_choice
      split: validation
      args:
        num_few_shot: 0
    metrics:
    - type: mc2
      value: 37.33
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=alnrg2arg/blockchainlabs_tinyllama_fusion_LHK_yunkong_v2
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: Winogrande (5-shot)
      type: winogrande
      config: winogrande_xl
      split: validation
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 57.14
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=alnrg2arg/blockchainlabs_tinyllama_fusion_LHK_yunkong_v2
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: GSM8k (5-shot)
      type: gsm8k
      config: main
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 0.76
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=alnrg2arg/blockchainlabs_tinyllama_fusion_LHK_yunkong_v2
      name: Open LLM Leaderboard
---
This model is based on the fusion strategy offered by Fanqi Wan(https://github.com/fanqiwan/FuseLLM).

Three models are fused together. 10epochs

Base model: TinyLlama/TinyLlama-1.1B-Chat-v1.0

Blending model 1: HanNayeoniee/LHK_DPO_v1

Blending model 2: yunconglong/Truthful_DPO_TomGrc_FusionNet_7Bx2_MoE_13B

This model will be optimized by Laser and DPO later.

This project is to make the on-device sLM. We are doing experiments on the models.
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_alnrg2arg__blockchainlabs_tinyllama_fusion_LHK_yunkong_v2)

|             Metric              |Value|
|---------------------------------|----:|
|Avg.                             |36.67|
|AI2 Reasoning Challenge (25-Shot)|34.90|
|HellaSwag (10-Shot)              |63.11|
|MMLU (5-Shot)                    |26.75|
|TruthfulQA (0-shot)              |37.33|
|Winogrande (5-shot)              |57.14|
|GSM8k (5-shot)                   | 0.76|