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--- |
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license: cc |
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datasets: |
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- VMware/open-instruct-v1.1-oasst-dolly-hhrlhf |
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language: |
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- en |
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library_name: transformers |
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pipeline_tag: conversational |
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--- |
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# VMware/open-llama-0.3T-7B-open-instruct-v1.1 |
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## License |
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- <b>Commercially Viable </b> |
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- 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 |
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- 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 |
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## Nomenclature |
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- Model : Open-llama |
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- Model trained on : 300B or 0.3 T tokens |
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- Model Size: 7B parameters |
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- Dataset: Open-instruct-v1.1 (oasst,dolly, hhrlhf) |
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## Use in Transformers |
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``` |
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import os |
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import torch |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model_name = 'VMware/open-llama-0.3T-7B-open-instruct-v1.1' |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype= torch.float16, device_map = 'sequential') |
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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:" |
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prompt= 'Explain in simple terms how the attention mechanism of a transformer model works' |
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inputt = prompt_template.format(instruction= prompt) |
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input_ids = tokenizer(inputt, return_tensors="pt").input_ids.to("cuda") |
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output1 = model.generate(input_ids, max_length=512) |
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input_length = input_ids.shape[1] |
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output1 = output1[:, input_length:] |
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output= tokenizer.decode(output1[0]) |
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print(output) |
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''' |
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The attention mechanism of a transformer model is designed to help the model understand the relationship between different parts of a sentence. |
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The model uses a weighted attention score to determine how much each input token contributes to the output. |
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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. |
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This way, the model can better understand the relationship between different parts of a sentence and generate more accurate predictions. |
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''' |
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``` |
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## Drawbacks |
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- The model was trained on a partially trained Open-LLaMA checkpoint (300B tokens or 30% training life cycle), there is a huge potential for improvement when trained on fully trained Open-LLaMA checkpoints |
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- From what we have observed, the model strugles with few-shot prompting (We plan on addressing it with future iterations) |
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- When asked for code, it may or maynot output it with markdown format |
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- It doesn't indent python code |
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## Evaluation |
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<B>TODO</B> |
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