Instructions to use Goekdeniz-Guelmez/Hyperion-2.0-Mistral-7B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Goekdeniz-Guelmez/Hyperion-2.0-Mistral-7B-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="Goekdeniz-Guelmez/Hyperion-2.0-Mistral-7B-GGUF")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Goekdeniz-Guelmez/Hyperion-2.0-Mistral-7B-GGUF")
model = AutoModelForCausalLM.from_pretrained("Goekdeniz-Guelmez/Hyperion-2.0-Mistral-7B-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Goekdeniz-Guelmez/Hyperion-2.0-Mistral-7B-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Goekdeniz-Guelmez/Hyperion-2.0-Mistral-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Goekdeniz-Guelmez/Hyperion-2.0-Mistral-7B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Goekdeniz-Guelmez/Hyperion-2.0-Mistral-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Goekdeniz-Guelmez/Hyperion-2.0-Mistral-7B-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf Goekdeniz-Guelmez/Hyperion-2.0-Mistral-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Goekdeniz-Guelmez/Hyperion-2.0-Mistral-7B-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf Goekdeniz-Guelmez/Hyperion-2.0-Mistral-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Goekdeniz-Guelmez/Hyperion-2.0-Mistral-7B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Goekdeniz-Guelmez/Hyperion-2.0-Mistral-7B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use Goekdeniz-Guelmez/Hyperion-2.0-Mistral-7B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Goekdeniz-Guelmez/Hyperion-2.0-Mistral-7B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Goekdeniz-Guelmez/Hyperion-2.0-Mistral-7B-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Goekdeniz-Guelmez/Hyperion-2.0-Mistral-7B-GGUF:Q4_K_M

SGLang

How to use Goekdeniz-Guelmez/Hyperion-2.0-Mistral-7B-GGUF 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 "Goekdeniz-Guelmez/Hyperion-2.0-Mistral-7B-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Goekdeniz-Guelmez/Hyperion-2.0-Mistral-7B-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "Goekdeniz-Guelmez/Hyperion-2.0-Mistral-7B-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Goekdeniz-Guelmez/Hyperion-2.0-Mistral-7B-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use Goekdeniz-Guelmez/Hyperion-2.0-Mistral-7B-GGUF with Ollama:
```
ollama run hf.co/Goekdeniz-Guelmez/Hyperion-2.0-Mistral-7B-GGUF:Q4_K_M
```

Unsloth Studio

How to use Goekdeniz-Guelmez/Hyperion-2.0-Mistral-7B-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Goekdeniz-Guelmez/Hyperion-2.0-Mistral-7B-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Goekdeniz-Guelmez/Hyperion-2.0-Mistral-7B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Goekdeniz-Guelmez/Hyperion-2.0-Mistral-7B-GGUF to start chatting

Docker Model Runner
How to use Goekdeniz-Guelmez/Hyperion-2.0-Mistral-7B-GGUF with Docker Model Runner:
```
docker model run hf.co/Goekdeniz-Guelmez/Hyperion-2.0-Mistral-7B-GGUF:Q4_K_M
```

Lemonade

How to use Goekdeniz-Guelmez/Hyperion-2.0-Mistral-7B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Goekdeniz-Guelmez/Hyperion-2.0-Mistral-7B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Hyperion-2.0-Mistral-7B-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

Hyperion-2.0-Mistral-7B

Model Details

Model Name: Locutusque/Hyperion-2.0-Mistral-7B
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-2.0-Mistral-7B is a state-of-the-art language model fine-tuned on the Hyperion-v2.0 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.

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-2.0-Mistral-7B model was fine-tuned on the Hyperion-v2.0 dataset, which amalgamates various datasets rich in diversity and complexity, including programming, medical texts, mathematical problems, and reasoning tasks.

Evaluation Results

0-shot AGIEval

Tasks	Version	Filter	Metric	Value		Stderr
agieval_nous	N/A	none	acc	0.3602	±	0.0929
		none	acc_norm	0.3342	±	0.0764
- agieval_aqua_rat	1	none	acc	0.2402	±	0.0269
		none	acc_norm	0.2441	±	0.0270
- agieval_logiqa_en	1	none	acc	0.2965	±	0.0179
		none	acc_norm	0.3226	±	0.0183
- agieval_lsat_ar	1	none	acc	0.2348	±	0.0280
		none	acc_norm	0.2000	±	0.0264
- agieval_lsat_lr	1	none	acc	0.3667	±	0.0214
		none	acc_norm	0.3373	±	0.0210
- agieval_lsat_rc	1	none	acc	0.4981	±	0.0305
		none	acc_norm	0.4089	±	0.0300
- agieval_sat_en	1	none	acc	0.6359	±	0.0336
		none	acc_norm	0.5777	±	0.0345
- agieval_sat_en_without_passage	1	none	acc	0.3883	±	0.0340
		none	acc_norm	0.3544	±	0.0334
- agieval_sat_math	1	none	acc	0.3500	±	0.0322
		none	acc_norm	0.2682	±	0.0299

Groups	Version	Filter	n-shot	Metric	Value		Stderr
agieval_nous	N/A	none	0	acc	0.3602	±	0.0929
		none	0	acc_norm	0.3342	±	0.0764

5-shot AGIEval 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.

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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Safetensors

Model size

7B params

Tensor type

BF16

Goekdeniz-Guelmez
/

Hyperion-2.0-Mistral-7B-GGUF