fix base repo reference

README.md

@@ -0,0 +1,72 @@
+---
+license: apache-2.0
+datasets:
+- cerebras/SlimPajama-627B
+- bigcode/starcoderdata
+- OpenAssistant/oasst_top1_2023-08-25
+- Trelis/openassistant-llama-style
+language:
+- en
+tags:
+- chat
+- tinyllama
+---
+# TinyLlama-1.1B Chat (1 Trillion token checkpoint)
+
+The prompt format is:
+```
+f"[INST] {prompt} [INST]"
+```
+just like Llama 2 base models.
+
+Note that this model has trouble being succinct and does not emit the end of sequence (< /s >) token well.
+
+The model was fine tuned using an adapted filtered Openassistant dataset [here](https://huggingface.co/datasets/Trelis/openassistant-llama-style).
+
+The base repo follows here:
+
+# 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 Model
+This is an intermediate checkpoint with 480K steps and 1007B tokens.
+
+
+#### How to use
+You will need the transformers>=4.31
+Do check the [TinyLlama](https://github.com/jzhang38/TinyLlama) github page for more information.
+```python
+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']}")
+```