Instructions to use Locutusque/Hyperion-1.5-Mistral-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Locutusque/Hyperion-1.5-Mistral-7B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Locutusque/Hyperion-1.5-Mistral-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Locutusque/Hyperion-1.5-Mistral-7B")
model = AutoModelForCausalLM.from_pretrained("Locutusque/Hyperion-1.5-Mistral-7B")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Locutusque/Hyperion-1.5-Mistral-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Locutusque/Hyperion-1.5-Mistral-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Locutusque/Hyperion-1.5-Mistral-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Locutusque/Hyperion-1.5-Mistral-7B

SGLang

How to use Locutusque/Hyperion-1.5-Mistral-7B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Locutusque/Hyperion-1.5-Mistral-7B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Locutusque/Hyperion-1.5-Mistral-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Locutusque/Hyperion-1.5-Mistral-7B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Locutusque/Hyperion-1.5-Mistral-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Locutusque/Hyperion-1.5-Mistral-7B with Docker Model Runner:
```
docker model run hf.co/Locutusque/Hyperion-1.5-Mistral-7B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

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

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

Downloads last month: 101

Safetensors

Model size

7B params

Tensor type

BF16

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

Merges

4 models

Quantizations

4 models

Spaces using Locutusque/Hyperion-1.5-Mistral-7B 9

Evaluation results

normalized accuracy on AI2 Reasoning Challenge (25-Shot)
test set Open LLM Leaderboard

60.490
normalized accuracy on HellaSwag (10-Shot)
validation set Open LLM Leaderboard

83.640
accuracy on MMLU (5-Shot)
test set Open LLM Leaderboard

63.570
mc2 on TruthfulQA (0-shot)
validation set Open LLM Leaderboard

41.780
accuracy on Winogrande (5-shot)
validation set Open LLM Leaderboard

78.610
accuracy on GSM8k (5-shot)
test set Open LLM Leaderboard

40.490