PY007/TinyLlama-1.1B-intermediate-step-715k-1.5T-GGUF

Quantized GGUF model files for TinyLlama-1.1B-intermediate-step-715k-1.5T from PY007

Name	Quant method	Size
tinyllama-1.1b-intermediate-step-715k-1.5t.q2_k.gguf	q2_k	482.14 MB
tinyllama-1.1b-intermediate-step-715k-1.5t.q3_k_m.gguf	q3_k_m	549.85 MB
tinyllama-1.1b-intermediate-step-715k-1.5t.q4_k_m.gguf	q4_k_m	252.38 MB
tinyllama-1.1b-intermediate-step-715k-1.5t.q5_k_m.gguf	q5_k_m	200.14 MB
tinyllama-1.1b-intermediate-step-715k-1.5t.q6_k.gguf	q6_k	903.41 MB
tinyllama-1.1b-intermediate-step-715k-1.5t.q8_0.gguf	q8_0	1.17 GB

Original Model Card:

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 715K steps and 1.49T tokens. We suggest you not use this directly for inference.

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-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']}")

Eval

Model	Pretrain Tokens	HellaSwag	Obqa	WinoGrande	ARC_c	ARC_e	boolq	piqa	avg
Pythia-1.0B	300B	47.16	31.40	53.43	27.05	48.99	60.83	69.21	48.30
TinyLlama-1.1B-intermediate-step-50K-104b	103B	43.50	29.80	53.28	24.32	44.91	59.66	67.30	46.11
TinyLlama-1.1B-intermediate-step-240k-503b	503B	49.56	31.40	55.80	26.54	48.32	56.91	69.42	48.28
TinyLlama-1.1B-intermediate-step-480k-1007B	1007B	52.54	33.40	55.96	27.82	52.36	59.54	69.91	50.22
TinyLlama-1.1B-intermediate-step-715k-1.5T	1.49T	53.68	35.20	58.33	29.18	51.89	59.08	71.65	51.29