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mistral-7b-v0-3/README.md

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+
+# Mistral LoRA Adaptors
+
+This repository contains a Mistral model with LoRA adaptors.
+
+## Usage
+
+Run the following script to load the model and generate text:
+
+```python
+from mistral_inference.model import Transformer
+from mistral_inference.generate import generate
+from mistral_common.tokens.tokenizers.mistral import MistralTokenizer
+from mistral_common.protocol.instruct.messages import UserMessage
+from mistral_common.protocol.instruct.request import ChatCompletionRequest
+
+def main():
+    tokenizer = MistralTokenizer.from_file("model/tokenizer.model.v3")
+    model = Transformer.from_folder("model")
+    model.load_lora("lora/lora.safetensors")
+
+    completion_request = ChatCompletionRequest(messages=[UserMessage(content="Explain Machine Learning to me in a nutshell.")])
+    tokens = tokenizer.encode_chat_completion(completion_request).tokens
+    out_tokens, _ = generate([tokens], model, max_tokens=64, temperature=0.0, eos_id=tokenizer.instruct_tokenizer.tokenizer.eos_id)
+    result = tokenizer.instruct_tokenizer.tokenizer.decode(out_tokens[0])
+    print(result)
+
+if __name__ == "__main__":
+    main()

mistral-7b-v0-3/lora/lora.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:eeb94a64b55f6408b420ec980a822c2212da24a222eb2d4238237ea527bbb4d9
+size 335594288

mistral-7b-v0-3/script.py

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+
+from mistral_inference.model import Transformer
+from mistral_inference.generate import generate
+from mistral_common.tokens.tokenizers.mistral import MistralTokenizer
+from mistral_common.protocol.instruct.messages import UserMessage
+from mistral_common.protocol.instruct.request import ChatCompletionRequest
+
+def main():
+    tokenizer = MistralTokenizer.from_file("model/tokenizer.model.v3")
+    model = Transformer.from_folder("model")
+    model.load_lora("lora/lora.safetensors")
+
+    completion_request = ChatCompletionRequest(messages=[UserMessage(content="Explain Machine Learning to me in a nutshell.")])
+    tokens = tokenizer.encode_chat_completion(completion_request).tokens
+    out_tokens, _ = generate([tokens], model, max_tokens=64, temperature=0.0, eos_id=tokenizer.instruct_tokenizer.tokenizer.eos_id)
+    result = tokenizer.instruct_tokenizer.tokenizer.decode(out_tokens[0])
+    print(result)
+
+if __name__ == "__main__":
+    main()