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Model: VMware/open-llama-0.7T-7B-open-instruct-v1.1
Source: Original Platform

README.md

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+---
+license: cc
+datasets:
+- VMware/open-instruct-v1.1-oasst-dolly-hhrlhf
+language:
+- en
+library_name: transformers
+pipeline_tag: conversational
+---
+
+# VMware/open-llama-0.7T-7B-open-instruct-v1.1
+
+---
+
+# UPDATE: Final Version Now Available!
+
+Please use the final version: [Open LLaMA 7B Open Instruct](https://huggingface.co/VMware/open-llama-7b-open-instruct)
+
+---
+
+## License
+- <b>Commercially Viable </b>
+- Instruction dataset, [VMware/open-instruct-v1-oasst-dolly-hhrlhf](https://huggingface.co/datasets/VMware/open-instruct-v1-oasst-dolly-hhrlhf) is under cc-by-sa-3.0
+- Language Model ([openlm-research/open_llama_7b_700bt_preview](https://huggingface.co/openlm-research/open_llama_7b_700bt_preview)) is under apache-2.0
+
+
+## Nomenclature 
+
+- Model : Open-llama
+- Model trained on : 700B or 0.7 T tokens
+- Model Size: 7B parameters
+- Dataset: Open-instruct-v1.1 (oasst,dolly, hhrlhf)
+- Version: 1.1 (Alpaca prompt template)
+
+
+## Use in Transformers
+
+
+```
+import os
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = 'VMware/open-llama-0.7T-7B-open-instruct-v1.1'
+
+
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+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.
+
+'''
+```
+  
+
+
+
+# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
+Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_VMware__open-llama-0.7T-7B-open-instruct-v1.1)
+
+| Metric                | Value                     |
+|-----------------------|---------------------------|
+| Avg.                  | 39.33   |
+| ARC (25-shot)         | 46.67          |
+| HellaSwag (10-shot)   | 67.67    |
+| MMLU (5-shot)         | 28.55         |
+| TruthfulQA (0-shot)   | 37.6   |
+| Winogrande (5-shot)   | 65.43   |
+| GSM8K (5-shot)        | 0.76        |
+| DROP (3-shot)         | 28.61         |