Update README.md

README.md

@@ -21,6 +21,8 @@ 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.
@@ -38,31 +40,31 @@ We will be rolling out intermediate checkpoints following the below schedule. We
 | 2023-11-15 | --                                             | 2.5T   | --   |  --               |
 | 2023-12-01 | --                                             | 3T     | --   |  --               |
 
-<!-- | Baseline   | [Pythia-1B-intermediate-52b](https://huggingface.co/EleutherAI/pythia-1b/tree/step25000)             | 52B   | 25k   |  38.81            | -->
-<!-- | Baseline   | [Pythia-1.4B-intermediate-52b](https://huggingface.co/EleutherAI/pythia-1.4b/tree/step25000)             | 52B   | 25k   |  42.49            | -->
-<!-- | Baseline   | [Pythia-1.4B-intermediate-105b](https://huggingface.co/EleutherAI/pythia-1.4b/tree/step50000)             | 105B   | 50k   |  46.14            | -->
-<!-- | 2023-09-04 | [TinyLlama-1.1B-intermediate-52b](https://huggingface.co/PY007/TinyLlama-1.1B-52b)   | 52B    | 25k  |  40.85            |
-| 2023-09-04 | [TinyLlama-1.1B-intermediate-84b](https://huggingface.co/PY007/TinyLlama-1.1B-84b)   | 84B    | 40k  |  42.65            |  -->
+#### 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",
+)
 
-It can be observed that TinyLlama has so far progressed well 🎉🎉. 
-
-Meanwhile, you can track the live cross entropy loss [here](https://wandb.ai/lance777/lightning_logs/reports/metric-train_loss-23-09-02-15-26-17---Vmlldzo1MjkzNzMw?accessToken=9843chbl7rfi1w03hxttpcnbo9z8t6088pw3ddn4h8teunaq0cy7j8hw9c5i02ve).
-
-## Training Details
-Below are some details of our training setup:
-
-| Setting                         | Description                                                    |
-|---------------------------------|----------------------------------------------------------------|
-| Parameters                      | 1.1B                                                           |
-| Attention Variant               | Grouped Query Attention                                        |
-| Model Size                      | Layers: 22, Heads: 32, Query Groups: 4, Embedding Size: 2048, Intermediate Size (Swiglu): 5632|
-| Sequence Length                 | 2048                                                           |
-| Batch Size                      | 2 million tokens (2048 * 1024)                                             |
-| Learning Rate                   | 4e-4                                                           |
-| Learning Rate Schedule          | Cosine with 2000 warmup steps                                  |
-| Training Data                   | [Slimpajama](https://huggingface.co/datasets/cerebras/slimpajama-627b) & [Starcoderdata](https://huggingface.co/datasets/bigcode/starcoderdata) |
-| Data Preprocessing              | Excluded GitHub subset of Slimpajama; Sampled all code from Starcoderdata |
-| Combined Dataset Size           | 1 trillion tokens                                              |
-| Total Tokens During Training    | 3 trillion (3 epochs/1430k steps)                                          |
-| Natural Language to Code Ratio  | 7:3                                                            |
-| Hardware                        | 16 A100-40G GPUs                                               |
+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']}")
+```