Update README.md

README.md

@@ -21,50 +21,5 @@ The TinyLlama project aims to **pretrain** a **1.1B Llama model on 3 trillion to
 
 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 50K steps and 105B 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](https://huggingface.co/stabilityai/stablelm-base-alpha-3b)| 800B   | --   |  38.31            |
-| Baseline   | [Pythia-1B-intermediate-step-50k-105b](https://huggingface.co/EleutherAI/pythia-1b/tree/step50000)             | 105B   | 50k   |  42.04            |
-| Baseline   | [Pythia-1B](https://huggingface.co/EleutherAI/pythia-1b)             | 300B   | 143k   |  47.16            |
-| 2023-09-04 | [TinyLlama-1.1B-intermediate-step-50k-105b](https://huggingface.co/PY007/TinyLlama-1.1B-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](https://github.com/jzhang38/TinyLlama) github page for more information.
-```
-from transformers import AutoTokenizer
-import transformers 
-import torch
-model = "PY007/TinyLlama-1.1B-step-50K-105b"
-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']}")
-```
+#### This Collection
+This collection contains all checkpoints after the 1T fix. Branch name indicates the step and number of tokens seen.