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olaverse/MIST-1-140B
MIST-1-140B is a text generation model from olaverse. Use it when you need the model to write or continue text. It is set up for transformers. The card lists the license as llama3.1.
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From the Hugging Face model README

MIST-1-140B is the largest model in the MIST model family by olaverse. Created by stacking MIST-1-70B with itself using the Frankenmerge technique — doubling the depth to 158 layers and ~140B parameters.
| 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 |
MIST-1-70B (80 layers — DARE+TIES of 4 best 70B models) ↓ Frankenmerge technique MIST-1-140B (158 layers — ~140B parameters)
Inspired by Samsung's Solar 10.7B which used the same layer stacking technique to beat models twice its size.
| Task | Precision | Speed | Quality |
|---|---|---|---|
| Reasoning | bfloat16 | 32s | ✅ Detailed and conversational |
| Coding | bfloat16 | 32s | ✅ Well documented with docstrings |
| Math | bfloat16 | 32s | ✅ Clear step-by-step |
| General | bfloat16 | 32s | ✅ Rich and engaging |
| Reasoning | 4-bit | ~35s | ✅ Slightly slower, similar quality |
Average: 8 tok/s (bfloat16) — fits on 2x H200
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained(
"olaverse/MIST-1-140B",
torch_dtype="auto",
device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained("olaverse/MIST-1-140B")
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))
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
import torch
quantization_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_compute_dtype=torch.bfloat16,
bnb_4bit_quant_type='nf4'
)
model = AutoModelForCausalLM.from_pretrained(
"olaverse/MIST-1-140B",
quantization_config=quantization_config,
device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained("olaverse/MIST-1-140B")
| Precision | VRAM | Size |
|---|---|---|
| bfloat16 | 280GB (2x H200) | 256GB |
| 4-bit (NF4) | ~70GB (1x H200/H100) | 69GB |