AI Models
MIST Mini 8B
MIST family · open weights on Hugging Face.
General-purpose 8B merged LLM built on the Llama 3.1 lineage with advanced model merging.
TransformersSafetensorsLlamaText GenerationMergeDare TiesLlama 3.1MistConversationalEnglishBase: NousResearch/DeepHermes-3-Llama-3-8B-PreviewBase: merge:NousResearch/DeepHermes-3-Llama-3-8B-PreviewBase: NousResearch/Hermes-3-Llama-3.1-8BBase: merge:NousResearch/Hermes-3-Llama-3.1-8BBase: deepseek-ai/DeepSeek-R1-Distill-Llama-8BBase: merge:deepseek-ai/DeepSeek-R1-Distill-Llama-8BBase: nvidia/Llama-3.1-Nemotron-Nano-8B-v1Base: merge:nvidia/Llama-3.1-Nemotron-Nano-8B-v1Licence: llama3.1Text Generation InferenceEndpoints Compatible
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725
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May 2026
Created
/ Model card
Built for production use
Open-weights repository on Hugging Face
Integrated ecosystem protocol tier.
Compatible with Transformers library
Integrated ecosystem protocol tier.
Optimised for low-latency inference
Integrated ecosystem protocol tier.
Community engagement: 4 likes
Integrated ecosystem protocol tier.
/ Quick start
Use MIST Mini 8B, straight from its model card.
example.pyfrom the model card
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))
/ Model card
Official repository README
Dynamically loaded from Hugging Face
Model card metadata is available on Hugging Face.
/ Built with
Transformers PyTorch Python
Ready to try MIST Mini 8B?
General-purpose 8B merged LLM built on the Llama 3.1 lineage with advanced model merging.
/ Model ecosystem