---
license: apache-2.0
tags:
- finetuned
pipeline_tag: text-generation
model-index:
- name: TinyLlama-1.1B-2.5T-chat
  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: 34.47
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=AIGym/TinyLlama-1.1B-2.5T-chat
      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: 59.71
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=AIGym/TinyLlama-1.1B-2.5T-chat
      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: 26.45
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=AIGym/TinyLlama-1.1B-2.5T-chat
      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: 38.8
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=AIGym/TinyLlama-1.1B-2.5T-chat
      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: 61.01
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=AIGym/TinyLlama-1.1B-2.5T-chat
      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: 1.14
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=AIGym/TinyLlama-1.1B-2.5T-chat
      name: Open LLM Leaderboard
---

# TinyLlama-1.1B-2.5T-chat
It was created by starting with the TinyLlama-1.1B-2.5T-chat and training it on a llama dataset. We have attached the wandb report in pdf form to view the training run at a glance.

# Reson
This model was fine tuned to allow it to follow direction and is a steeping stone to further training.

# Referrals
Run Pod - This is who I use to train th emodels on huggingface. If you use it we both get free crdits. - <a href="https://runpod.io?ref=kilq83n1" target="_blank" style="color: #3498db; text-decoration: none; font-weight: bold;">Visit Runpod's Website!</a>

Paypal - If you want to leave a tip, it is appecaheted. - <a href="https://paypal.me/OpenSourceTraining" target="_blank" style="color: #3498db; text-decoration: none; font-weight: bold;">Visit My Paypal!</a>
# [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_AIGym__TinyLlama-1.1B-2.5T-chat)

|             Metric              |Value|
|---------------------------------|----:|
|Avg.                             |36.93|
|AI2 Reasoning Challenge (25-Shot)|34.47|
|HellaSwag (10-Shot)              |59.71|
|MMLU (5-Shot)                    |26.45|
|TruthfulQA (0-shot)              |38.80|
|Winogrande (5-shot)              |61.01|
|GSM8k (5-shot)                   | 1.14|