README.md

metadata

language:
  - en
license: apache-2.0
datasets:
  - cerebras/SlimPajama-627B
  - bigcode/starcoderdata
model-index:
  - name: TinyLlama-1.1B-intermediate-step-955k-token-2T
    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: 30.29
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TinyLlama/TinyLlama-1.1B-intermediate-step-955k-token-2T
          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: 54.84
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TinyLlama/TinyLlama-1.1B-intermediate-step-955k-token-2T
          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: 26.47
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TinyLlama/TinyLlama-1.1B-intermediate-step-955k-token-2T
          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: 36.07
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TinyLlama/TinyLlama-1.1B-intermediate-step-955k-token-2T
          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: 58.33
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TinyLlama/TinyLlama-1.1B-intermediate-step-955k-token-2T
          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: 1.36
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TinyLlama/TinyLlama-1.1B-intermediate-step-955k-token-2T
          name: Open LLM Leaderboard

TinyLlama-1.1B

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.

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 995K steps and 2003B tokens.

Releases Schedule

We will be rolling out intermediate checkpoints following the below schedule. We also include some baseline models for comparison.

Date	HF Checkpoint	Tokens	Step	HellaSwag Acc_norm
Baseline	StableLM-Alpha-3B	800B	--	38.31
Baseline	Pythia-1B-intermediate-step-50k-105b	105B	50k	42.04
Baseline	Pythia-1B	300B	143k	47.16
2023-09-04	TinyLlama-1.1B-intermediate-step-50k-105b	105B	50k	43.50
2023-09-16	--	500B	--	--
2023-10-01	--	1T	--	--
2023-10-16	--	1.5T	--	--
2023-10-31	--	2T	--	--
2023-11-15	--	2.5T	--	--
2023-12-01	--	3T	--	--

How to use

You will need the transformers>=4.31 Do check the TinyLlama github page for more information.

from transformers import AutoTokenizer
import transformers 
import torch
model = "TinyLlama/TinyLlama-1.1B-intermediate-step-955k-token-2T"
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

Detailed results can be found here

Metric	Value
Avg.	34.56
AI2 Reasoning Challenge (25-Shot)	30.29
HellaSwag (10-Shot)	54.84
MMLU (5-Shot)	26.47
TruthfulQA (0-shot)	36.07
Winogrande (5-shot)	58.33
GSM8k (5-shot)	1.36