Update README.md

README.md

@@ -1,37 +1,41 @@
----
-license: apache-2.0
-datasets:
-- cerebras/SlimPajama-627B
-- bigcode/starcoderdata
-language:
-- en
----
-<div align="center">
-
-# TinyLlama-1.1B
-</div>
-
-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. 
-
-<div align="center">
-  <img src="./TinyLlama_logo.png" width="300"/>
-</div>
-
-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 Collection
-This collection contains all checkpoints after the 1T fix. Branch name indicates the step and number of tokens seen.
-
-#### 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 |     1.5T     | 53.68     | 35.20 | 58.33      | 29.18 | 51.89 | 59.08 | 71.65 | 51.29 |
-| TinyLlama-1.1B-intermediate-step-955k-2T |     2T     | 54.63     | 33.40 | 56.83      | 28.07 | 54.67 | 63.21 | 70.67 | 51.64 |
-| **TinyLlama-1.1B-intermediate-step-1195k-token-2.5T**  |     **2.5T**     | **58.96**     | **34.40** | **58.72**      | **31.91** | **56.78** | **63.21** | **73.07** | **53.86**|
+# TinyLlama-1.1B-2.5T-chat
+It was created by starting with the TinyLlama-1.1B-2.5T-chat and training it on the open assistant dataset. We have attached the wandb report in pdf form to view the training run at a glance.
+
+# Reson
+This model was fine tuned to allow it to follow direction and is a steeping stone to further training, but still would be good for asking qestions about code.
+
+# How to use
+You will need the transformers>=4.31
+```python
+from transformers import AutoTokenizer
+import transformers 
+import torch
+model = "AIGym/TinyLlama-1.1B-2.5T-chat"
+tokenizer = AutoTokenizer.from_pretrained(model)
+pipeline = transformers.pipeline(
+    "text-generation",
+    model=model,
+    torch_dtype=torch.float16,
+    device_map="auto",
+)
+prompt = "What are the values in open source projects?"
+formatted_prompt = (
+    f"### Human: {prompt}### Assistant:"
+)
+sequences = pipeline(
+    formatted_prompt,
+    do_sample=True,
+    top_k=50,
+    top_p = 0.7,
+    num_return_sequences=1,
+    repetition_penalty=1.1,
+    max_new_tokens=500,
+)
+for seq in sequences:
+    print(f"Result: {seq['generated_text']}")
+```
+
+# Referrals
+Run Pod - This is who I use to train th emodels on huggingface. If you use it we both get free crdits. - <a href="https://runpod.io?ref=kilq83n1" target="_blank" style="color: #3498db; text-decoration: none; font-weight: bold;">Visit Runpod's Website!</a>
+
+Paypal - If you want to leave a tip, it is appecaheted. - <a href="paypal.me/OpenSourceTraining" target="_blank" style="color: #3498db; text-decoration: none; font-weight: bold;">Visit My Paypal!</a>