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--- |
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license: cc |
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datasets: |
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- VMware/open-instruct-v1-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: text-generation |
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--- |
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This is a 4bit quantised [open-llama-13b-open-instruct](https://huggingface.co/VMware/open-llama-13b-open-instruct) using Oobabooga's [GPTQ for LLaMa](https://github.com/oobabooga/GPTQ-for-LLaMa). |
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Original model readme is below. |
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# VMware/open-llama-13B-open-instruct |
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Instruction-tuned version of the fully trained Open LLama 13B model. The model is open for <b>COMMERCIAL USE</b>. <br> |
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<b> NOTE </b> : The model was trained using the Alpaca prompt template \ |
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<b> NOTE </b> : Fast tokenizer results in incorrect encoding, set the ```use_fast = False``` parameter, when instantiating the tokenizer\ |
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<b> NOTE </b> : The model might struggle with code as the tokenizer merges multiple spaces |
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## License |
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- <b>Commercially Viable </b> |
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- 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 |
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- Language Model, ([openlm-research/open_llama_13b](https://huggingface.co/openlm-research/open_llama_13b)) is under apache-2.0 |
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## Nomenclature |
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- Model : Open-llama |
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- Model Size: 13B parameters |
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- Dataset: Open-instruct-v1 (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-13b-open-instruct' |
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tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False) |
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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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## Finetuning details |
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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) |
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## Evaluation |
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<B>TODO</B> |
