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Usage tips Mistral-7B-v0.1 and Mistral-7B-Instruct-v0.1 can be found on the Huggingface Hub These ready-to-use checkpoints can be downloaded and used via the HuggingFace Hub: thon from transformers import AutoModelForCausalLM, AutoTokenizer device = "cuda" # the device to load the model onto model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.1") tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1") prompt = "My favourite condiment is" model_inputs = tokenizer([prompt], return_tensors="pt").to(device) model.to(device) generated_ids = model.generate(**model_inputs, max_new_tokens=100, do_sample=True) tokenizer.batch_decode(generated_ids)[0] "The expected output" Raw weights for Mistral-7B-v0.1 and Mistral-7B-Instruct-v0.1 can be downloaded from: | Model Name | Checkpoint | |----------------------------|-----------------------------------------------------------------------------------------| | Mistral-7B-v0.1 | Raw Checkpoint | | Mistral-7B-Instruct-v0.1 | Raw Checkpoint | To use these raw checkpoints with HuggingFace you can use the convert_mistral_weights_to_hf.py script to convert them to the HuggingFace format: python src/transformers/models/mistral/convert_mistral_weights_to_hf.py \ --input_dir /path/to/downloaded/mistral/weights --model_size 7B --output_dir /output/path You can then load the converted model from the output/path: thon from transformers import MistralForCausalLM, LlamaTokenizer tokenizer = LlamaTokenizer.from_pretrained("/output/path") model = MistralForCausalLM.from_pretrained("/output/path") Combining Mistral and Flash Attention 2 First, make sure to install the latest version of Flash Attention 2 to include the sliding window attention feature. |