Update README.md

README.md

@@ -6,4 +6,59 @@ language:
 - en
 library_name: transformers
 pipeline_tag: conversational
----
+---
+
+# VMware/open-llama-0.3T-7B-open-instruct-v1.1
+
+Fully Open Source, <b>Commerically viable.</b>
+The instruction dataset, [VMware/open-instruct-v1.1-oasst-dolly-hhrlhf](https://huggingface.co/datasets/VMware/open-instruct-v1.1-oasst-dolly-hhrlhf) is under cc-by-sa-3.0, and the Language Model ([openlm-research/open_llama_7b_preview_300bt](https://huggingface.co/openlm-research/open_llama_7b_preview_300bt/tree/main/open_llama_7b_preview_300bt_transformers_weights)) is under apache-2.0 License.
+
+## Useage
+
+Please load the tokenizer with 'add_bos_token = True' parameter as the underlying OpenLLaMa model and this model were trained with a BOS token. 
+
+```
+import os
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = 'VMware/open-llama-0.3T-7B-open-instruct-v1.1'
+
+
+tokenizer = AutoTokenizer.from_pretrained(model_name, add_bos_token = True)
+
+model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype= torch.float16, device_map = 'sequential')
+
+prompt_template = "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{instruction}\n\n### Response:"
+
+prompt=  'Explain in simple terms how the attention mechanism of a transformer model works'
+
+
+inputt = prompt_template.format(instruction= prompt)
+input_ids = tokenizer(inputt, return_tensors="pt").input_ids.to("cuda")
+
+output1 = model.generate(input_ids, max_length=512)
+input_length = input_ids.shape[1]
+output1 = output1[:, input_length:]
+output= tokenizer.decode(output1[0])
+
+print(output)
+
+'''
+The attention mechanism of a transformer model is designed to help the model understand the relationship between different parts of a sentence.
+The model uses a weighted attention score to determine how much each input token contributes to the output.
+The attention score is calculated by looking at the similarity between each input token and the output token,and assigning a weight to each input token based on this similarity.
+This way, the model can better understand the relationship between different parts of a sentence and generate more accurate predictions.
+
+'''
+```
+
+## Drawbacks
+<ul>
+<li>The model was trained on a partially trained Open-LLaMA checkpoint. (300B tokens).
+</li>The model is inconsistent with outputting '\n' tokens as majority of the dataset is obtained from [mosaicml/dolly_hhrlhf](https://huggingface.co/datasets/mosaicml/dolly_hhrlhf) and that dataset removed newline characters from responses.
+</ul>
+
+## Evaluation
+
+<B>TODO</B>