Hyperion-3.0-Mistral-7B-alpha
Model Details
- Model Name: Locutusque/Hyperion-3.0-Mistral-7B-alpha
- Base Model: mistralai/Mistral-7B-v0.1
- Publisher: Locutusque
- Model Type: Question answering, conversational AI, code generation, medical text comprehension, mathematical reasoning, logical reasoning.
- Language: Multi-domain, English language.
- License: Apache-2.0
Model Description
Locutusque/Hyperion-3.0-Mistral-7B-alpha is a state-of-the-art language model fine-tuned on the Hyperion-v3.0 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. This model is designed to greatly outperform its predecessors.
Intended Use
This model is intended for researchers and practitioners looking for a powerful tool to tackle challenging problems in scientific domains. It can be used in the following scenarios:
- AI-driven tutoring systems for science, medicine, mathematics, and computer science.
- Assistive tools for professionals requiring fast and accurate domain-specific information retrieval.
- Platforms that require conversational AI capabilities with a focus on technical and scientific reasoning.
- Automation in code generation and understanding complex programming context.
Training Data
The Locutusque/Hyperion-3.0-Mistral-7B-alpha model was fine-tuned on 200,000 examples of the Hyperion-3.0 dataset, which amalgamates various datasets rich in diversity and complexity, including programming, medical texts, mathematical problems, and reasoning tasks.
Quants
ExLlamaV2: https://huggingface.co/bartowski/Hyperion-3.0-Mistral-7B-alpha-exl2
GGUF: https://huggingface.co/bartowski/Hyperion-3.0-Mistral-7B-alpha-GGUF
Evaluation Results
Zero-shot AGIEval
| Tasks | Version | Filter | n-shot | Metric | Value | Stderr | |
|---|---|---|---|---|---|---|---|
| agieval_nous | N/A | none | None | acc_norm | 0.3500 | ± | 0.0092 |
| none | None | acc | 0.3519 | ± | 0.0093 | ||
| - agieval_aqua_rat | 1 | none | None | acc | 0.2087 | ± | 0.0255 |
| none | None | acc_norm | 0.2165 | ± | 0.0259 | ||
| - agieval_logiqa_en | 1 | none | None | acc | 0.3164 | ± | 0.0182 |
| none | None | acc_norm | 0.3533 | ± | 0.0187 | ||
| - agieval_lsat_ar | 1 | none | None | acc | 0.2348 | ± | 0.0280 |
| none | None | acc_norm | 0.2174 | ± | 0.0273 | ||
| - agieval_lsat_lr | 1 | none | None | acc | 0.3529 | ± | 0.0212 |
| none | None | acc_norm | 0.3647 | ± | 0.0213 | ||
| - agieval_lsat_rc | 1 | none | None | acc | 0.4721 | ± | 0.0305 |
| none | None | acc_norm | 0.4201 | ± | 0.0301 | ||
| - agieval_sat_en | 1 | none | None | acc | 0.6019 | ± | 0.0342 |
| none | None | acc_norm | 0.6117 | ± | 0.0340 | ||
| - agieval_sat_en_without_passage | 1 | none | None | acc | 0.4078 | ± | 0.0343 |
| none | None | acc_norm | 0.3835 | ± | 0.0340 | ||
| - agieval_sat_math | 1 | none | None | acc | 0.3091 | ± | 0.0312 |
| none | None | acc_norm | 0.2364 | ± | 0.0287 |
5 shot CoT MMLU
| Tasks | Version | Filter | n-shot | Metric | Value | Stderr | |
|---|---|---|---|---|---|---|---|
| mmlu_flan_cot_fewshot | N/A | get-answer | 0 | exact_match | 0.5924 | ± | 0.0118 |
| - mmlu_flan_cot_fewshot_humanities | N/A | get-answer | 0 | exact_match | 0.5077 | ± | 0.0206 |
| - mmlu_flan_cot_fewshot_formal_logic | 0 | get-answer | 0 | exact_match | 0.2143 | ± | 0.1138 |
| - mmlu_flan_cot_fewshot_high_school_european_history | 0 | get-answer | 0 | exact_match | 0.6111 | ± | 0.1182 |
| - mmlu_flan_cot_fewshot_high_school_us_history | 0 | get-answer | 0 | exact_match | 0.7727 | ± | 0.0914 |
| - mmlu_flan_cot_fewshot_high_school_world_history | 0 | get-answer | 0 | exact_match | 0.6154 | ± | 0.0973 |
| - mmlu_flan_cot_fewshot_international_law | 0 | get-answer | 0 | exact_match | 0.9231 | ± | 0.0769 |
| - mmlu_flan_cot_fewshot_jurisprudence | 0 | get-answer | 0 | exact_match | 0.3636 | ± | 0.1521 |
| - mmlu_flan_cot_fewshot_logical_fallacies | 0 | get-answer | 0 | exact_match | 0.7222 | ± | 0.1086 |
| - mmlu_flan_cot_fewshot_moral_disputes | 0 | get-answer | 0 | exact_match | 0.5526 | ± | 0.0817 |
| - mmlu_flan_cot_fewshot_moral_scenarios | 0 | get-answer | 0 | exact_match | 0.3900 | ± | 0.0490 |
| - mmlu_flan_cot_fewshot_philosophy | 0 | get-answer | 0 | exact_match | 0.7647 | ± | 0.0738 |
| - mmlu_flan_cot_fewshot_prehistory | 0 | get-answer | 0 | exact_match | 0.7143 | ± | 0.0775 |
| - mmlu_flan_cot_fewshot_professional_law | 0 | get-answer | 0 | exact_match | 0.3471 | ± | 0.0366 |
| - mmlu_flan_cot_fewshot_world_religions | 0 | get-answer | 0 | exact_match | 0.8947 | ± | 0.0723 |
| - mmlu_flan_cot_fewshot_other | N/A | get-answer | 0 | exact_match | 0.6921 | ± | 0.0240 |
| - mmlu_flan_cot_fewshot_business_ethics | 0 | get-answer | 0 | exact_match | 0.9091 | ± | 0.0909 |
| - mmlu_flan_cot_fewshot_clinical_knowledge | 0 | get-answer | 0 | exact_match | 0.5517 | ± | 0.0940 |
| - mmlu_flan_cot_fewshot_college_medicine | 0 | get-answer | 0 | exact_match | 0.7727 | ± | 0.0914 |
| - mmlu_flan_cot_fewshot_global_facts | 0 | get-answer | 0 | exact_match | 0.6000 | ± | 0.1633 |
| - mmlu_flan_cot_fewshot_human_aging | 0 | get-answer | 0 | exact_match | 0.6522 | ± | 0.1015 |
| - mmlu_flan_cot_fewshot_management | 0 | get-answer | 0 | exact_match | 0.9091 | ± | 0.0909 |
| - mmlu_flan_cot_fewshot_marketing | 0 | get-answer | 0 | exact_match | 0.8400 | ± | 0.0748 |
| - mmlu_flan_cot_fewshot_medical_genetics | 0 | get-answer | 0 | exact_match | 1.0000 | ± | 0.0000 |
| - mmlu_flan_cot_fewshot_miscellaneous | 0 | get-answer | 0 | exact_match | 0.7791 | ± | 0.0450 |
| - mmlu_flan_cot_fewshot_nutrition | 0 | get-answer | 0 | exact_match | 0.6667 | ± | 0.0833 |
| - mmlu_flan_cot_fewshot_professional_accounting | 0 | get-answer | 0 | exact_match | 0.4194 | ± | 0.0901 |
| - mmlu_flan_cot_fewshot_professional_medicine | 0 | get-answer | 0 | exact_match | 0.6774 | ± | 0.0853 |
| - mmlu_flan_cot_fewshot_virology | 0 | get-answer | 0 | exact_match | 0.3889 | ± | 0.1182 |
| - mmlu_flan_cot_fewshot_social_sciences | N/A | get-answer | 0 | exact_match | 0.6973 | ± | 0.0239 |
| - mmlu_flan_cot_fewshot_econometrics | 0 | get-answer | 0 | exact_match | 0.3333 | ± | 0.1421 |
| - mmlu_flan_cot_fewshot_high_school_geography | 0 | get-answer | 0 | exact_match | 0.9091 | ± | 0.0627 |
| - mmlu_flan_cot_fewshot_high_school_government_and_politics | 0 | get-answer | 0 | exact_match | 0.8095 | ± | 0.0878 |
| - mmlu_flan_cot_fewshot_high_school_macroeconomics | 0 | get-answer | 0 | exact_match | 0.6279 | ± | 0.0746 |
| - mmlu_flan_cot_fewshot_high_school_microeconomics | 0 | get-answer | 0 | exact_match | 0.6154 | ± | 0.0973 |
| - mmlu_flan_cot_fewshot_high_school_psychology | 0 | get-answer | 0 | exact_match | 0.9167 | ± | 0.0360 |
| - mmlu_flan_cot_fewshot_human_sexuality | 0 | get-answer | 0 | exact_match | 0.5000 | ± | 0.1508 |
| - mmlu_flan_cot_fewshot_professional_psychology | 0 | get-answer | 0 | exact_match | 0.6667 | ± | 0.0572 |
| - mmlu_flan_cot_fewshot_public_relations | 0 | get-answer | 0 | exact_match | 0.5833 | ± | 0.1486 |
| - mmlu_flan_cot_fewshot_security_studies | 0 | get-answer | 0 | exact_match | 0.4444 | ± | 0.0975 |
| - mmlu_flan_cot_fewshot_sociology | 0 | get-answer | 0 | exact_match | 0.7727 | ± | 0.0914 |
| - mmlu_flan_cot_fewshot_us_foreign_policy | 0 | get-answer | 0 | exact_match | 0.7273 | ± | 0.1408 |
| - mmlu_flan_cot_fewshot_stem | N/A | get-answer | 0 | exact_match | 0.5164 | ± | 0.0265 |
| - mmlu_flan_cot_fewshot_abstract_algebra | 0 | get-answer | 0 | exact_match | 0.4545 | ± | 0.1575 |
| - mmlu_flan_cot_fewshot_anatomy | 0 | get-answer | 0 | exact_match | 0.3571 | ± | 0.1329 |
| - mmlu_flan_cot_fewshot_astronomy | 0 | get-answer | 0 | exact_match | 0.5000 | ± | 0.1291 |
| - mmlu_flan_cot_fewshot_college_biology | 0 | get-answer | 0 | exact_match | 0.5625 | ± | 0.1281 |
| - mmlu_flan_cot_fewshot_college_chemistry | 0 | get-answer | 0 | exact_match | 0.3750 | ± | 0.1830 |
| - mmlu_flan_cot_fewshot_college_computer_science | 0 | get-answer | 0 | exact_match | 0.2727 | ± | 0.1408 |
| - mmlu_flan_cot_fewshot_college_mathematics | 0 | get-answer | 0 | exact_match | 0.2727 | ± | 0.1408 |
| - mmlu_flan_cot_fewshot_college_physics | 0 | get-answer | 0 | exact_match | 0.4545 | ± | 0.1575 |
| - mmlu_flan_cot_fewshot_computer_security | 0 | get-answer | 0 | exact_match | 0.7273 | ± | 0.1408 |
| - mmlu_flan_cot_fewshot_conceptual_physics | 0 | get-answer | 0 | exact_match | 0.6154 | ± | 0.0973 |
| - mmlu_flan_cot_fewshot_electrical_engineering | 0 | get-answer | 0 | exact_match | 0.6875 | ± | 0.1197 |
| - mmlu_flan_cot_fewshot_elementary_mathematics | 0 | get-answer | 0 | exact_match | 0.7317 | ± | 0.0701 |
| - mmlu_flan_cot_fewshot_high_school_biology | 0 | get-answer | 0 | exact_match | 0.7188 | ± | 0.0808 |
| - mmlu_flan_cot_fewshot_high_school_chemistry | 0 | get-answer | 0 | exact_match | 0.3636 | ± | 0.1050 |
| - mmlu_flan_cot_fewshot_high_school_computer_science | 0 | get-answer | 0 | exact_match | 0.6667 | ± | 0.1667 |
| - mmlu_flan_cot_fewshot_high_school_mathematics | 0 | get-answer | 0 | exact_match | 0.4138 | ± | 0.0931 |
| - mmlu_flan_cot_fewshot_high_school_physics | 0 | get-answer | 0 | exact_match | 0.2353 | ± | 0.1060 |
| - mmlu_flan_cot_fewshot_high_school_statistics | 0 | get-answer | 0 | exact_match | 0.4348 | ± | 0.1057 |
| - mmlu_flan_cot_fewshot_machine_learning | 0 | get-answer | 0 | exact_match | 0.3636 | ± | 0.1521 |
| Groups | Version | Filter | n-shot | Metric | Value | Stderr | |
|---|---|---|---|---|---|---|---|
| mmlu_flan_cot_fewshot | N/A | get-answer | 0 | exact_match | 0.5924 | ± | 0.0118 |
| - mmlu_flan_cot_fewshot_humanities | N/A | get-answer | 0 | exact_match | 0.5077 | ± | 0.0206 |
| - mmlu_flan_cot_fewshot_other | N/A | get-answer | 0 | exact_match | 0.6921 | ± | 0.0240 |
| - mmlu_flan_cot_fewshot_social_sciences | N/A | get-answer | 0 | exact_match | 0.6973 | ± | 0.0239 |
| - mmlu_flan_cot_fewshot_stem | N/A | get-answer | 0 | exact_match | 0.5164 | ± | 0.0265 |
How to Use
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "Locutusque/Hyperion-3.0-Mistral-7B-alpha"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
# For a text generation task
input_text = "<|im_start|>user\nWhat are the implications of Einstein's theory of relativity in modern physics?<|im_end|>\n<|im_start|>assistant\n"
input_ids = tokenizer.encode(input_text, return_tensors="pt")
# Generate a response
outputs = 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)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Known Limitations
The diversity of the dataset could lead to inconsistencies in the model's responses due to variations in data formatting and annotation quality.
This model is also very compliant, it will respond to any request. Please make sure to build upon this model with DPO if you plan on using it for enterprise-level deployment.
Licensing Information
This model is released under the Apache-2.0 license.
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