model card

README.md

@@ -0,0 +1,63 @@
+---
+license: apache-2.0
+datasets:
+- cerebras/SlimPajama-627B
+- bigcode/starcoderdata
+- OpenAssistant/oasst_top1_2023-08-25
+language:
+- en
+tags:
+- awq
+- tinyllama
+---
+# AWQ version of TinyLlama at 1Trillion tokens
+
+original model card follows below.
+
+# 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. 
+
+
+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 the chat model finetuned on top of [PY007/TinyLlama-1.1B-intermediate-step-480k-1T](https://huggingface.co/PY007/TinyLlama-1.1B-intermediate-step-480k-1T). 
+The dataset used is [OpenAssistant/oasst_top1_2023-08-25](https://huggingface.co/datasets/OpenAssistant/oasst_top1_2023-08-25) following the [chatml](https://github.com/openai/openai-python/blob/main/chatml.md) format.
+#### 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-Chat-v0.3"
+tokenizer = AutoTokenizer.from_pretrained(model)
+pipeline = transformers.pipeline(
+    "text-generation",
+    model=model,
+    torch_dtype=torch.float16,
+    device_map="auto",
+)
+
+prompt = "How to get in a good university?"
+formatted_prompt = (
+    f"<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant\n"
+)
+
+
+sequences = pipeline(
+    formatted_prompt,
+    do_sample=True,
+    top_k=50,
+    top_p = 0.9,
+    num_return_sequences=1,
+    repetition_penalty=1.1,
+    max_new_tokens=1024,
+)
+for seq in sequences:
+    print(f"Result: {seq['generated_text']}")
+```