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RichardErkhov/
Locutusque_-_Hyperion-1.5-Mistral-7B-gguf

Name: Locutusque_-_Hyperion-1.5-Mistral-7B-gguf
Rating: 2.5 (455 reviews)

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Hyperion-1.5-Mistral-7B - GGUF

Model creator: https://huggingface.co/Locutusque/
Original model: https://huggingface.co/Locutusque/Hyperion-1.5-Mistral-7B/

Name	Quant method	Size
Hyperion-1.5-Mistral-7B.Q2_K.gguf	Q2_K	2.53GB
Hyperion-1.5-Mistral-7B.IQ3_XS.gguf	IQ3_XS	2.81GB
Hyperion-1.5-Mistral-7B.IQ3_S.gguf	IQ3_S	2.96GB
Hyperion-1.5-Mistral-7B.Q3_K_S.gguf	Q3_K_S	2.95GB
Hyperion-1.5-Mistral-7B.IQ3_M.gguf	IQ3_M	3.06GB
Hyperion-1.5-Mistral-7B.Q3_K.gguf	Q3_K	3.28GB
Hyperion-1.5-Mistral-7B.Q3_K_M.gguf	Q3_K_M	3.28GB
Hyperion-1.5-Mistral-7B.Q3_K_L.gguf	Q3_K_L	3.56GB
Hyperion-1.5-Mistral-7B.IQ4_XS.gguf	IQ4_XS	3.67GB
Hyperion-1.5-Mistral-7B.Q4_0.gguf	Q4_0	3.83GB
Hyperion-1.5-Mistral-7B.IQ4_NL.gguf	IQ4_NL	3.87GB
Hyperion-1.5-Mistral-7B.Q4_K_S.gguf	Q4_K_S	3.86GB
Hyperion-1.5-Mistral-7B.Q4_K.gguf	Q4_K	4.07GB
Hyperion-1.5-Mistral-7B.Q4_K_M.gguf	Q4_K_M	4.07GB
Hyperion-1.5-Mistral-7B.Q4_1.gguf	Q4_1	4.24GB
Hyperion-1.5-Mistral-7B.Q5_0.gguf	Q5_0	4.65GB
Hyperion-1.5-Mistral-7B.Q5_K_S.gguf	Q5_K_S	4.65GB
Hyperion-1.5-Mistral-7B.Q5_K.gguf	Q5_K	4.78GB
Hyperion-1.5-Mistral-7B.Q5_K_M.gguf	Q5_K_M	4.78GB
Hyperion-1.5-Mistral-7B.Q5_1.gguf	Q5_1	5.07GB
Hyperion-1.5-Mistral-7B.Q6_K.gguf	Q6_K	5.53GB
Hyperion-1.5-Mistral-7B.Q8_0.gguf	Q8_0	7.17GB

Original model description:

license: apache-2.0 library_name: transformers tags:

conversational datasets:
Locutusque/hyperion-v1.5 model-index:
name: Hyperion-1.5-Mistral-7B results:
- task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics:
  - type: acc_norm value: 60.49 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Locutusque/Hyperion-1.5-Mistral-7B name: Open LLM Leaderboard
- task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics:
  - type: acc_norm value: 83.64 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Locutusque/Hyperion-1.5-Mistral-7B name: Open LLM Leaderboard
- task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics:
  - type: acc value: 63.57 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Locutusque/Hyperion-1.5-Mistral-7B name: Open LLM Leaderboard
- task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics:
  - type: mc2 value: 41.78 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Locutusque/Hyperion-1.5-Mistral-7B name: Open LLM Leaderboard
- task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics:
  - type: acc value: 78.61 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Locutusque/Hyperion-1.5-Mistral-7B name: Open LLM Leaderboard
- task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics:
  - type: acc value: 40.49 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Locutusque/Hyperion-1.5-Mistral-7B name: Open LLM Leaderboard

Model Card for Locutusque/Hyperion-1.5-Mistral-7B

Model Details

Model Name: Locutusque/Hyperion-1.5-Mistral-7B Base Model: mistralai/Mistral-7B-v0.1
Publisher: M4-ai
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-1.5-Mistral-7B is a state-of-the-art language model fine-tuned on the Hyperion 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.

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-1.5-Mistral-7B model was fine-tuned on the Hyperion-v1.5 dataset, which amalgamates various datasets rich in diversity and complexity, including programming, medical texts, mathematical problems, and reasoning tasks.

Evaluation Results

Coming soon...

How to Use

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "Locutusque/Hyperion-1.5-Mistral-7B"
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.

Licensing Information

This model is released under the Apache-2.0 license.

Citation Information

If you use Locutusque/Hyperion-1.5-Mistral-7B in your research, please cite the Hyperion dataset as follows:

@misc{sebastian_gabarain_2024,
  title = {Hyperion-1.5: Illuminating the Path to Advanced Reasoning with a High-Quality, Multidisciplinary Question Answering Dataset},
  author = {Sebastian Gabarain},
  publisher = {HuggingFace},
  year = {2024},
  url = {https://huggingface.co/datasets/Locutusque/hyperion-v1.5}
}

Quants

exl2 and GGUF by bartowski - https://huggingface.co/bartowski/Hyperion-1.5-Mistral-7B-exl2 https://huggingface.co/bartowski/Hyperion-1.5-Mistral-7B-GGUF

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	61.43
AI2 Reasoning Challenge (25-Shot)	60.49
HellaSwag (10-Shot)	83.64
MMLU (5-Shot)	63.57
TruthfulQA (0-shot)	41.78
Winogrande (5-shot)	78.61
GSM8k (5-shot)	40.49

RichardErkhov/Locutusque_-_Hyperion-1.5-Mistral-7B-gguf