---
license: mit
widget:
- text: '<|system|>

    You are a helpful assistant</s>

    <|user|>

    What is your name? Tell me about yourself.</s>

    <|assistant|>'
model-index:
- name: tinyllama-730M-test
  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: 25.09
      name: normalized accuracy
    source:
      url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=Josephgflowers/tinyllama-730M-test
      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: 33.82
      name: normalized accuracy
    source:
      url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=Josephgflowers/tinyllama-730M-test
      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: 24.43
      name: accuracy
    source:
      url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=Josephgflowers/tinyllama-730M-test
      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: 42.9
    source:
      url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=Josephgflowers/tinyllama-730M-test
      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: 51.07
      name: accuracy
    source:
      url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=Josephgflowers/tinyllama-730M-test
      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: 0.0
      name: accuracy
    source:
      url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=Josephgflowers/tinyllama-730M-test
      name: Open LLM Leaderboard
---
I cut my TinyLlama 1.1B cinder v 2 down from 22 layers to 14. At 14 there was no coherent text but there were emerging ideas of a response. 1000 steps on step-by-step dataset.
6000 on Reason-with-cinder. The loss was still over 1 and the learning rate was still over 4. This model needs significat training. I am putting it up as a base model that
needs work. If you continue training please let me know on the tinyllama discord, I have some interesting plans for this model. 
# [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_Josephgflowers__tinyllama-730M-test)

|             Metric              |Value|
|---------------------------------|----:|
|Avg.                             |29.55|
|AI2 Reasoning Challenge (25-Shot)|25.09|
|HellaSwag (10-Shot)              |33.82|
|MMLU (5-Shot)                    |24.43|
|TruthfulQA (0-shot)              |42.90|
|Winogrande (5-shot)              |51.07|
|GSM8k (5-shot)                   | 0.00|