Base model: Solar-Open-100B
Quantization: FP8 (weight: per-channel/static, activation: per-token/dynamic)
Hardware Requirements:
- Minimum: 2 x NVIDIA A100 (80GB)

License

This repository contains both model weights and code, which are licensed under different terms:

MODEL WEIGHTS (*.safetensors) Licensed under Upstage Solar License See: https://huggingface.co/upstage/Solar-Open-100B/blob/main/LICENSE
CODE (*.py, *.json, *.jinja files) Licensed under Apache License 2.0 See: https://www.apache.org/licenses/LICENSE-2.0

Performance

English

	Solar-Open-100B	Nota FP8 (Ours)
PPL (WikiText-2)↓	6.06	6.06
PPL (C4)↓	20.37	20.62
PIQA↑	82.37	81.94
BoolQ↑	84.89	85.14
ARC-E↑	87.25	87.08
ARC-C↑	61.43	61.60
TruthfulQA↑	59.25	59.29
WinoGrande↑	76.09	76.01

Korean

	Solar-Open-100B	Nota FP8 (Ours)
HRM8K↑	81.52	81.54
MMLU-ProX-Lite↑	55.44	54.86
KoBEST↑	62.00	62.00
CLiCK↑	71.33	71.28

Model weigth memory footprint

Solar-Open-100B	Nota MoE Quantization (Ours)
191.2 GB	97.2 GB

Note
- ↑ / ↓ denote the direction of improvement: higher is better (↑), lower is better (↓).
- Because we used a smaller thinking budget, the results for HRM8K and CLiCK are slightly lower than the numbers reported in the original Solar-Open-100B repository.
- Memory refers to the pure VRAM footprint occupied only by the model weights.

Inference

Transformers

Install the required dependencies:

pip install -U transformers kernels torch accelerate

Run inference with the following code:

python

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

MODEL_ID = "nota-ai/Solar-Open-100B-Nota-FP8"

# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)

model = AutoModelForCausalLM.from_pretrained(
    pretrained_model_name_or_path=MODEL_ID,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True,
)

# Prepare input
messages = [{"role": "user", "content": "who are you?"}]
inputs = tokenizer.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_dict=True,
    return_tensors="pt",
)
inputs = inputs.to(model.device)

# Generate response
generated_ids = model.generate(
    **inputs,
    max_new_tokens=4096,
    temperature=0.8,
    top_p=0.95,
    top_k=50,
    do_sample=True,
)
generated_text = tokenizer.decode(generated_ids[0][inputs.input_ids.shape[1] :])
print(generated_text)

vLLM

Create and activate a Python virtual environment

bash

uv venv --python 3.12 --seed
source .venv/bin/activate

Install Solar Open's optimized vLLM

bash

VLLM_PRECOMPILED_WHEEL_LOCATION="https://github.com/vllm-project/vllm/releases/download/v0.12.0/vllm-0.12.0-cp38-abi3-manylinux_2_31_x86_64.whl" \
VLLM_USE_PRECOMPILED=1 \
uv pip install git+https://github.com/UpstageAI/vllm.git@v0.12.0-solar-open

Start the vLLM server (For 2 GPUs)

bash

PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True
vllm serve nota-ai/Solar-Open-100B-Nota-FP8 \
    --trust-remote-code \
    --enable-auto-tool-choice \
    --tool-call-parser solar_open \
    --reasoning-parser solar_open \
    --logits-processors vllm.model_executor.models.parallel_tool_call_logits_processor:ParallelToolCallLogitsProcessor \
    --logits-processors vllm.model_executor.models.solar_open_logits_processor:SolarOpenTemplateLogitsProcessor \
    --tensor-parallel-size 2 \
    --max-num-seqs 64 \
    --gpu-memory-utilization 0.8