Instructions to use decem/Dionysus-Mistral-m3-v5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use decem/Dionysus-Mistral-m3-v5 with Transformers:

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

pipe = pipeline("text-generation", model="decem/Dionysus-Mistral-m3-v5")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("decem/Dionysus-Mistral-m3-v5")
model = AutoModelForCausalLM.from_pretrained("decem/Dionysus-Mistral-m3-v5")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use decem/Dionysus-Mistral-m3-v5 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "decem/Dionysus-Mistral-m3-v5"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "decem/Dionysus-Mistral-m3-v5",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/decem/Dionysus-Mistral-m3-v5

SGLang

How to use decem/Dionysus-Mistral-m3-v5 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 "decem/Dionysus-Mistral-m3-v5" \
    --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": "decem/Dionysus-Mistral-m3-v5",
		"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 "decem/Dionysus-Mistral-m3-v5" \
        --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": "decem/Dionysus-Mistral-m3-v5",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use decem/Dionysus-Mistral-m3-v5 with Docker Model Runner:
```
docker model run hf.co/decem/Dionysus-Mistral-m3-v5
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

decem/Dionysus-Mistral-m3-v5 - A Fine-tuned Language Model

Model Details

Developer: DECEM
Fine-tuning Method: SFT
Language: English

Prompting

Prompt Template for alpaca style

### Instruction:

<prompt> (without the <>)

### Response:

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	63.14
AI2 Reasoning Challenge (25-Shot)	59.56
HellaSwag (10-Shot)	80.99
MMLU (5-Shot)	61.18
TruthfulQA (0-shot)	50.93
Winogrande (5-shot)	75.14
GSM8k (5-shot)	51.02

Downloads last month: 209

Safetensors

Model size

7B params

Tensor type

BF16

Model tree for decem/Dionysus-Mistral-m3-v5

Quantizations

4 models

Spaces using decem/Dionysus-Mistral-m3-v5 9

Evaluation results

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

59.560
normalized accuracy on HellaSwag (10-Shot)
validation set Open LLM Leaderboard

80.990
accuracy on MMLU (5-Shot)
test set Open LLM Leaderboard

61.180
mc2 on TruthfulQA (0-shot)
validation set Open LLM Leaderboard

50.930
accuracy on Winogrande (5-shot)
validation set Open LLM Leaderboard

75.140
accuracy on GSM8k (5-shot)
test set Open LLM Leaderboard

51.020