license, language, pipeline_tag, library_name, inference, base_model, tags

license	language	pipeline_tag	library_name	inference	base_model	tags
llama3.1	en	text-generation	transformers	true	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	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

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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

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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

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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+)

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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