Instructions to use olaverse/MIST-Mini-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use olaverse/MIST-Mini-8B with Transformers:

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

pipe = pipeline("text-generation", model="olaverse/MIST-Mini-8B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("olaverse/MIST-Mini-8B")
model = AutoModelForCausalLM.from_pretrained("olaverse/MIST-Mini-8B")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use olaverse/MIST-Mini-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "olaverse/MIST-Mini-8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "olaverse/MIST-Mini-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/olaverse/MIST-Mini-8B

SGLang

How to use olaverse/MIST-Mini-8B 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 "olaverse/MIST-Mini-8B" \
    --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": "olaverse/MIST-Mini-8B",
		"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 "olaverse/MIST-Mini-8B" \
        --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": "olaverse/MIST-Mini-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use olaverse/MIST-Mini-8B with Docker Model Runner:
```
docker model run hf.co/olaverse/MIST-Mini-8B
```

MIST-Mini-8B / README.md

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metadata

license: llama3.1
language:
  - en
pipeline_tag: text-generation
library_name: transformers
inference: true
base_model:
  - NousResearch/Hermes-3-Llama-3.1-8B
  - NousResearch/DeepHermes-3-Llama-3-8B-Preview
  - nvidia/Llama-3.1-Nemotron-Nano-8B-v1
  - deepseek-ai/DeepSeek-R1-Distill-Llama-8B
tags:
  - merge
  - dare_ties
  - llama
  - llama-3.1
  - mist

MIST-1-8B

MIST-1-8B (formerly MIST-Mini) is the smallest and fastest model in the MIST model family by olaverse. Built by blending 4 specialized Llama 3.1 8B models using DARE+TIES — delivering strong performance at maximum speed. fast, thorough, great for everyday use

MIST Model Family

Model	Params	Speed	Status
MIST-1-8B	8B	~63 tok/s	✅ Available
MIST-1-70B	70B	~23 tok/s	✅ Available
MIST-1-140B	140B	~8 tok/s	✅ Available

Key Strengths

⚡ Fastest — 63 tok/s on H200, great for real-time applications
🧠 Strong Reasoning — DeepSeek R1 distillation
💻 Clean Code — production-ready with comments
📐 Math — accurate step-by-step solving
🤝 Helpful — low refusal rate
📦 Lightweight — 15GB, runs on consumer GPUs

Benchmark Results

Task	Speed	Quality
Reasoning	4.5s	✅ Correct
Coding	4.0s	✅ Clean code
Math	4.0s	✅ Step-by-step
General	4.0s	✅ Accurate
Instruction	4.0s	✅ Precise

Average: 63 tok/s

How to Use

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "olaverse/MIST-Mini-8B",
    torch_dtype="auto",
    device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained("olaverse/MIST-Mini-8B")

messages = [{"role": "user", "content": "Your question here"}]
text = tokenizer.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True
)
inputs = tokenizer(text, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Hardware Requirements

Precision	VRAM Required
bfloat16	16GB (RTX 3090/4090)
4-bit	6GB (RTX 3060+)

Recommended Generation Settings

These settings were verified through testing. Without repetition_penalty and min_p the model will ramble and not stop cleanly.

outputs = model.generate(
    **inputs,
    max_new_tokens=1024,
    do_sample=True,
    temperature=0.7,
    top_p=0.95,
    min_p=0.05,
    repetition_penalty=1.5,
    eos_token_id=[128040, 128009, 128001],
    pad_token_id=128001,
)

Stop Tokens

This model's ChatML parents (<|im_end|>) survived the DARE+TIES merge alongside Llama 3.1 native tokens. Use all three:

Token	ID	Source
`<\|im_end\|>`	128040	Hermes/Nemotron parents
`<\|eot_id\|>`	128009	Llama 3.1 native
`<\|end_of_text\|>`	128001	Llama 3.1 native

License

Llama 3.1 Community License