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Adding code for usage with HuggingFace transformers. (#1)
Browse files- Adding code for usage with HuggingFace transformers. (bb75d42fcd9ad9c15c33e6bc026e37f8a4d67270)
Co-authored-by: Leonard Püttmann <LeonardPuettmann@users.noreply.huggingface.co>
README.md
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@@ -133,6 +133,28 @@ This model uses a specific chat format for optimal performance.
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[The model's response]</s>
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```
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## Risk Disclaimer
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By using this model, you acknowledge that you understand and assume the risks associated with its use. You are solely responsible for ensuring compliance with all applicable laws and regulations. We disclaim any liability for problems arising from the use of this open-source model, including but not limited to direct, indirect, incidental, consequential, or punitive damages. We make no warranties, express or implied, regarding the model's performance, accuracy, or fitness for a particular purpose. Your use of this model is at your own risk, and you agree to hold harmless and indemnify us, our affiliates, and our contributors from any claims, damages, or expenses arising from your use of the model.
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[The model's response]</s>
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```
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## Usage with HuggingFace transformers
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The model can be used with HuggingFace's `transformers` library:
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model = AutoModelForCausalLM.from_pretrained("OuteAI/Lite-Mistral-150M-v2-Instruct")
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tokenizer = AutoTokenizer.from_pretrained("OuteAI/Lite-Mistral-150M-v2-Instruct")
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def generate_response(message):
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# Encode the formatted message as input ids
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input_ids = tokenizer.encode(f"<s>user\n{message}</s>", return_tensors="pt")
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output = model.generate(input_ids, max_length=100, pad_token_id=tokenizer.eos_token_id)
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# Decode the generated output
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generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
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return generated_text
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message = "What is the capital of Spain?"
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response = generate_response(message)
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```
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## Risk Disclaimer
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By using this model, you acknowledge that you understand and assume the risks associated with its use. You are solely responsible for ensuring compliance with all applicable laws and regulations. We disclaim any liability for problems arising from the use of this open-source model, including but not limited to direct, indirect, incidental, consequential, or punitive damages. We make no warranties, express or implied, regarding the model's performance, accuracy, or fitness for a particular purpose. Your use of this model is at your own risk, and you agree to hold harmless and indemnify us, our affiliates, and our contributors from any claims, damages, or expenses arising from your use of the model.
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