bofenghuang
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README.md
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@@ -11,12 +11,12 @@ inference: false
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<p align="center" width="100%">
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<img src="https://huggingface.co/bofenghuang/vigogne-
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</p>
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# Vigogne-
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Vigogne-
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For more information, please visit the Github repo: https://github.com/bofenghuang/vigogne
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@@ -36,7 +36,7 @@ import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
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from vigogne.preprocess import generate_instruct_prompt
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model_name_or_path = "bofenghuang/vigogne-
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tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, padding_side="right", use_fast=False)
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model = AutoModelForCausalLM.from_pretrained(model_name_or_path, torch_dtype=torch.float16, device_map="auto")
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<p align="center" width="100%">
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<img src="https://huggingface.co/bofenghuang/vigogne-33b-instruct/resolve/main/vigogne_logo.png" alt="Vigogne" style="width: 40%; min-width: 300px; display: block; margin: auto;">
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</p>
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# Vigogne-33B-Instruct: A French Instruction-following LLaMA Model
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Vigogne-33B-Instruct is a LLaMA-33B model fine-tuned to follow the French instructions.
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For more information, please visit the Github repo: https://github.com/bofenghuang/vigogne
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from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
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from vigogne.preprocess import generate_instruct_prompt
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model_name_or_path = "bofenghuang/vigogne-33b-instruct"
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tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, padding_side="right", use_fast=False)
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model = AutoModelForCausalLM.from_pretrained(model_name_or_path, torch_dtype=torch.float16, device_map="auto")
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