Create README.md

README.md

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+---
+license: cc
+datasets:
+- VMware/open-instruct-v1-oasst-dolly-hhrlhf
+language:
+- en
+library_name: transformers
+pipeline_tag: text-generation
+---
+
+# VMware/open-llama-7B-open-instruct
+Instruction-tuned version of SalesForce/Xgen-7b-8k-base. The model is open for <b>COMMERCIAL USE</b>. <br>
+
+We expanded Open-instruct with additional commercially viable zero-shot COT datasets from Flan v2 (~70k). (TODO: List out the datasets) <br>
+
+The model supports up to <b>8192 tokens </b>
+
+<b> NOTE </b> : The model was trained using the Alpaca prompt template
+
+
+## License
+- <b>Commercially Viable </b>
+- The instruction datasets used for instruction tuning are open for commercial usage. (TODO LIST OUT THE DATASETS)
+- Language Model, ([Salesforce/xgen-7b-8k-base](https://huggingface.co/Salesforce/xgen-7b-8k-base)) is under apache-2.0
+
+
+
+## Use in Transformers
+
+```
+import os
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = 'VMware/xgen-7b-8k-open-instruct'
+
+
+tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False, trust_remote_code = True)
+
+model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, 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=8192)
+input_length = input_ids.shape[1]
+output1 = output1[:, input_length:]
+output = tokenizer.decode(output1[0])
+
+print(output)
+
+```
+  
+## Finetuning details
+The finetuning scripts will be available in our [RAIL Github Repository](https://github.com/vmware-labs/research-and-development-artificial-intelligence-lab/tree/main/instruction-tuning)
+## Evaluation
+
+<B>TODO</B>