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Locutusque/NeuralHyperion-2.0-Mistral-7B is a state-of-the-art language model fine-tuned on the Hyperion-v2.0 and distilabel-capybara dataset for advanced reasoning across scientific domains. This model is designed to handle complex inquiries and instructions, leveraging the diverse and rich information contained in the Hyperion dataset. Its primary use cases include but are not limited to complex question answering, conversational understanding, code generation, medical text comprehension, mathematical reasoning, and logical reasoning.Locutusque/NeuralHyperion-2.0-Mistral-7B model was fine-tuned on 1,550,000 examples of the Hyperion-v2.0 dataset, which amalgamates various datasets rich in diversity and complexity, including programming, medical texts, mathematical problems, and reasoning tasks. Then, it is further fine-tuned on the Capybara preference data using DPO.1from transformers import AutoModelForCausalLM, AutoTokenizer
2
3model_name = "Locutusque/NeuralHyperion-2.0-Mistral-7B"
4tokenizer = AutoTokenizer.from_pretrained(model_name)
5model = AutoModelForCausalLM.from_pretrained(model_name)
6
7# For a text generation task
8input_text = "<|im_start|>user\nWhat are the implications of Einstein's theory of relativity in modern physics?<|im_end|>\n<|im_start|>assistant\n"
9input_ids = tokenizer.encode(input_text, return_tensors="pt")
10
11# Generate a response
12outputs = model.generate(input_ids, max_length=200, num_return_sequences=1, temperature=0.8, top_p=0.95, top_k=40, repetition_penalty=1.1)
13print(tokenizer.decode(outputs[0], skip_special_tokens=True))