README.md

---
language:
- en
license: apache-2.0
datasets:
- cerebras/SlimPajama-627B
- bigcode/starcoderdata
model-index:
- name: TinyLlama-1.1B-intermediate-step-480k-1T
  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: 30.89
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=PY007/TinyLlama-1.1B-intermediate-step-480k-1T
      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: 52.97
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=PY007/TinyLlama-1.1B-intermediate-step-480k-1T
      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: 25.0
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=PY007/TinyLlama-1.1B-intermediate-step-480k-1T
      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: 39.55
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=PY007/TinyLlama-1.1B-intermediate-step-480k-1T
      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: 57.3
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=PY007/TinyLlama-1.1B-intermediate-step-480k-1T
      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: 0.53
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=PY007/TinyLlama-1.1B-intermediate-step-480k-1T
      name: Open LLM Leaderboard
---
<div align="center">

# TinyLlama-1.1B
</div>

https://github.com/jzhang38/TinyLlama

The TinyLlama project aims to **pretrain** a **1.1B Llama model on 3 trillion tokens**. With some proper optimization, we can achieve this within a span of "just" 90 days using 16 A100-40G GPUs 🚀🚀. The training has started on 2023-09-01. 

<div align="center">
  <img src="./TinyLlama_logo.png" width="300"/>
</div>

We adopted exactly the same architecture and tokenizer as Llama 2. This means TinyLlama can be plugged and played in many open-source projects built upon Llama. Besides, TinyLlama is compact with only 1.1B parameters. This compactness allows it to cater to a multitude of applications demanding a restricted computation and memory footprint.

#### This Model
This is an intermediate checkpoint with 480K steps and 1007B tokens.


#### How to use
You will need the transformers>=4.31
Do check the [TinyLlama](https://github.com/jzhang38/TinyLlama) github page for more information.
```python
from transformers import AutoTokenizer
import transformers 
import torch
model = "PY007/TinyLlama-1.1B-intermediate-step-240k-503b"
tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    torch_dtype=torch.float16,
    device_map="auto",
)

sequences = pipeline(
    'The TinyLlama project aims to pretrain a 1.1B Llama model on 3 trillion tokens. With some proper optimization, we can achieve this within a span of "just" 90 days using 16 A100-40G GPUs 🚀🚀. The training has started on 2023-09-01.',
    do_sample=True,
    top_k=10,
    num_return_sequences=1,
    repetition_penalty=1.5,
    eos_token_id=tokenizer.eos_token_id,
    max_length=500,
)
for seq in sequences:
    print(f"Result: {seq['generated_text']}")
```
# [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_PY007__TinyLlama-1.1B-intermediate-step-480k-1T)

|             Metric              |Value|
|---------------------------------|----:|
|Avg.                             |34.37|
|AI2 Reasoning Challenge (25-Shot)|30.89|
|HellaSwag (10-Shot)              |52.97|
|MMLU (5-Shot)                    |25.00|
|TruthfulQA (0-shot)              |39.55|
|Winogrande (5-shot)              |57.30|
|GSM8k (5-shot)                   | 0.53|