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  2. README.md +53 -0
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  ---
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  license: apache-2.0
 
 
 
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  ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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  license: apache-2.0
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+ pipeline_tag: text-generation
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+ tags:
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+ - finetuned
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  ---
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+
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+ # GGUF version of version of Mistral-7B-Instruct-v0.1
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+
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+ GGUF version of version of Mistral-7B-Instruct-v0.1 compatible with [llama.cpp](https://github.com/ggerganov/llama.cpp)
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+
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+ This the unquantized fp16 version of the model.
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+
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+ # Model Card for Mistral-7B-Instruct-v0.1
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+
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+ The Mistral-7B-Instruct-v0.1 Large Language Model (LLM) is a instruct fine-tuned version of the [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) generative text model using a variety of publicly available conversation datasets.
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+
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+ For full details of this model please read our [release blog post](https://mistral.ai/news/announcing-mistral-7b/)
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+
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+ ## Instruction format
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+
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+ In order to leverage instruction fine-tuning, your prompt should be surrounded by `[INST]` and `[\INST]` tokens. The very first instruction should begin with a begin of sentence id. The next instructions should not. The assistant generation will be ended by the end-of-sentence token id.
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+
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+ E.g.
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+
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+ ```python
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+ from transformers import AutoModelForCausalLM, AutoTokenizer
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+
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+ device = "cuda" # the device to load the model onto
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+
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+ model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1")
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+ tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1")
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+
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+ text = "<s>[INST] What is your favourite condiment? [/INST]"
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+ "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!</s> "
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+ "[INST] Do you have mayonnaise recipes? [/INST]"
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+
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+ encodeds = tokenizer(text, return_tensors="pt", add_special_tokens=False)
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+
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+ model_inputs = encodeds.to(device)
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+ model.to(device)
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+
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+ generated_ids = model.generate(**model_inputs, max_new_tokens=1000, do_sample=True)
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+ decoded = tokenizer.batch_decode(generated_ids)
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+ print(decoded[0])
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+ ```
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+
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+ ## Model Architecture
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+ This instruction model is based on Mistral-7B-v0.1, a transformer model with the following architecture choices:
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+ - Grouped-Query Attention
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+ - Sliding-Window Attention
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+ - Byte-fallback BPE tokenizer
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+
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+ ## The Mistral AI Team
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+
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+ Albert Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lélio Renard Lavaud, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed.
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