Instructions to use SteelStorage/Q2.5-MS-Mistoria-72b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SteelStorage/Q2.5-MS-Mistoria-72b with Transformers:

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

pipe = pipeline("text-generation", model="SteelStorage/Q2.5-MS-Mistoria-72b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("SteelStorage/Q2.5-MS-Mistoria-72b")
model = AutoModelForCausalLM.from_pretrained("SteelStorage/Q2.5-MS-Mistoria-72b", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use SteelStorage/Q2.5-MS-Mistoria-72b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SteelStorage/Q2.5-MS-Mistoria-72b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SteelStorage/Q2.5-MS-Mistoria-72b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/SteelStorage/Q2.5-MS-Mistoria-72b

SGLang

How to use SteelStorage/Q2.5-MS-Mistoria-72b 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 "SteelStorage/Q2.5-MS-Mistoria-72b" \
    --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": "SteelStorage/Q2.5-MS-Mistoria-72b",
		"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 "SteelStorage/Q2.5-MS-Mistoria-72b" \
        --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": "SteelStorage/Q2.5-MS-Mistoria-72b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use SteelStorage/Q2.5-MS-Mistoria-72b with Docker Model Runner:
```
docker model run hf.co/SteelStorage/Q2.5-MS-Mistoria-72b
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Now the cute anime girl has your attention

Creator: SteelSkull

About Mistoria-72b:

Name Legend:
Q2.5 = Qwen 2.5
MS = Model Stock
72B = its 72B

This model is my fist attempt at a 72b model as usual my goal is to merge the robust storytelling of mutiple models while attempting to maintain intelligence.

Use qwen format

Quants: (List of badasses)

- mradermacher: GGUF // Imat-GGUF

Config:

MODEL_NAME = "Q2.5-MS-Mistoria-72b"
base_model: zetasepic/Qwen2.5-72B-Instruct-abliterated-v2
merge_method: model_stock
dtype: bfloat16
models:
  - model: EVA-UNIT-01/EVA-Qwen2.5-72B-v0.1
  - model: ZeusLabs/Chronos-Platinum-72B
  - model: shuttleai/shuttle-3

If you wish to support:

Downloads last month: 17

Safetensors

Model size

73B params

Tensor type

BF16

Model tree for SteelStorage/Q2.5-MS-Mistoria-72b

EVA-UNIT-01/EVA-Qwen2.5-72B-v0.1

ZeusLabs/Chronos-Platinum-72B

shuttleai/shuttle-3

zetasepic/Qwen2.5-72B-Instruct-abliterated-v2

Merge model

this model

Quantizations

3 models