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  ---
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- license: cc-by-3.0
 
 
 
 
 
 
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  ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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+ license: cc-by-sa-3.0
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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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+
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+ # Open LLama 13B Open Instruct
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+ - Model creator: [VMware](https://huggingface.co/VMware)
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+ - Original model: [LLama 13B Open Instruct](https://huggingface.co/VMware/open-llama-13b-open-instruct)
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+
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+ ## Description
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+
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+ This repo contains the GGUF model files for [Open LLama 13B Open Instruct](https://huggingface.co/VMware/open-llama-13b-open-instruct).
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+
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+ These files are compatible with [llama.cpp](https://github.com/ggerganov/llama.cpp).
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+
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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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+
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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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+
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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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+
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+
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+ ## Nomenclature
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+
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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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+
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+ ## Use in Transformers
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+
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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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+
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+ model_name = 'VMware/open-llama-13b-open-instruct'
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+
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+
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+ tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False)
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+
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+ model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, device_map='sequential')
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+
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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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+
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+ prompt = 'Explain in simple terms how the attention mechanism of a transformer model works'
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+
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+
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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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+
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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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+
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+ print(output)
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+ ```
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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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+
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+ <B>TODO</B>
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