README.md

metadata

language:
  - en
license: apache-2.0
datasets:
  - cerebras/SlimPajama-627B
  - bigcode/starcoderdata
  - OpenAssistant/oasst_top1_2023-08-25
model-index:
  - name: TinyLlama-1.1B-Chat-v0.5
    results:
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: IFEval (0-Shot)
          type: HuggingFaceH4/ifeval
          args:
            num_few_shot: 0
        metrics:
          - type: inst_level_strict_acc and prompt_level_strict_acc
            value: 16.34
            name: strict accuracy
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=TinyLlama/TinyLlama-1.1B-Chat-v0.5
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: BBH (3-Shot)
          type: BBH
          args:
            num_few_shot: 3
        metrics:
          - type: acc_norm
            value: 3.41
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=TinyLlama/TinyLlama-1.1B-Chat-v0.5
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: MATH Lvl 5 (4-Shot)
          type: hendrycks/competition_math
          args:
            num_few_shot: 4
        metrics:
          - type: exact_match
            value: 0.08
            name: exact match
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=TinyLlama/TinyLlama-1.1B-Chat-v0.5
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: GPQA (0-shot)
          type: Idavidrein/gpqa
          args:
            num_few_shot: 0
        metrics:
          - type: acc_norm
            value: 0
            name: acc_norm
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=TinyLlama/TinyLlama-1.1B-Chat-v0.5
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: MuSR (0-shot)
          type: TAUR-Lab/MuSR
          args:
            num_few_shot: 0
        metrics:
          - type: acc_norm
            value: 3.57
            name: acc_norm
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=TinyLlama/TinyLlama-1.1B-Chat-v0.5
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: MMLU-PRO (5-shot)
          type: TIGER-Lab/MMLU-Pro
          config: main
          split: test
          args:
            num_few_shot: 5
        metrics:
          - type: acc
            value: 1.07
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=TinyLlama/TinyLlama-1.1B-Chat-v0.5
          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 the chat model finetuned on top of TinyLlama/TinyLlama-1.1B-intermediate-step-955k-2T. The dataset used is OpenAssistant/oasst_top1_2023-08-25 following the chatml format.

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 = "PY007/TinyLlama-1.1B-Chat-v0.5"
tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    torch_dtype=torch.float16,
    device_map="auto",
)

CHAT_EOS_TOKEN_ID = 32002

prompt = "How to get in a good university?"
formatted_prompt = (
    f"<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant\n"
)


sequences = pipeline(
    formatted_prompt,
    do_sample=True,
    top_k=50,
    top_p = 0.9,
    num_return_sequences=1,
    repetition_penalty=1.1,
    max_new_tokens=1024,
    eos_token_id=CHAT_EOS_TOKEN_ID,
)

for seq in sequences:
    print(f"Result: {seq['generated_text']}")

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	4.08
IFEval (0-Shot)	16.34
BBH (3-Shot)	3.41
MATH Lvl 5 (4-Shot)	0.08
GPQA (0-shot)	0.00
MuSR (0-shot)	3.57
MMLU-PRO (5-shot)	1.07