Create README.md

README.md

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+---
+language:
+- en
+- ko
+license: other
+license_name: solar-apache-2.0
+tags:
+- upstage
+- solar
+- moe
+- 100b
+- llm
+---
+
+<p align="center">
+  <img src="./Solar-Open-69B-REAP.png" alt="Solar Open Model" width="100%">
+</p>
+
+# **Solar Open to 69B Reap**
+
+**Solar Open** is Upstage's flagship **102B-parameter** and has been REAP'ed to a 69B Model using a modified Repository of Cerebras's REAP Repo on github
+
+## Links to Quants:
+
+- Coming soon
+
+# **Solar Open**
+
+**Solar Open** is Upstage's flagship **102B-parameter** large language model, trained **entirely from scratch** and released under the **Solar-Apache License 2.0** (see [LICENSE](#license) for details). As a **Mixture-of-Experts (MoE)** architecture, it delivers enterprise-grade performance in reasoning, instruction-following, and agentic capabilities—all while prioritizing transparency and customization for the open-source community.
+
+## Highlights
+
+* **MoE Architecture (102B / 12B):** Built on a Mixture-of-Experts architecture with **102B total / 12B active parameters**. This design delivers the knowledge depth of a massive model with the inference speed and cost-efficiency of a much smaller model.
+* **Massive Training Scale:** Pre-trained on **19.7 trillion tokens**, ensuring broad knowledge coverage and robust reasoning capabilities across various domains.
+
+## Model Overview
+
+* **Model Name:** Solar Open 100B
+* **Hugging Face ID:** Upstage/Solar-Open-100B
+* **Architecture:** Mixture-of-Experts (MoE)
+    * **Total Parameters:** 102.6B
+    * **Active Parameters:** 12B (per token)
+    * **Experts:** 129 Experts (top 8 among 128 Routed + 1 Shared)
+* **Pre-training Tokens:** 19.7 Trillion
+* **Context Length:** 128k
+* **Training Hardware:** NVIDIA B200 GPUs
+* **License:** **Solar-Apache License 2.0** (See [LICENSE](./LICENSE))
+* **Hardware Requirements:**
+    * **Minimum:** 4x NVIDIA A100 (80GB)
+
+## License
+This repository contains both model weights and code,
+which are licensed under different terms:
+
+1. MODEL WEIGHTS (*.safetensors)
+   Licensed under **Solar-Apache License 2.0**
+   See: https://huggingface.co/upstage/Solar-Open-100B/blob/main/LICENSE
+
+2. CODE (*.py, *.json, *.jinja files)
+   Licensed under **Apache License 2.0**
+   See: https://www.apache.org/licenses/LICENSE-2.0
+
+## Performance
+
+TBA
+
+## Inference Quickstart
+
+We recommend using the following generation parameters:
+
+```
+temperature=0.8
+top_p=0.95
+top_k=50
+```
+
+### Transformers
+
+Install the required dependencies:
+
+```bash
+pip install -U transformers kernels torch accelerate
+```
+
+Run inference with the following code:
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+MODEL_ID = "upstage/Solar-Open-100B"
+
+# 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
+
+#### Option 1: Using Docker (Highly Recommended)
+Docker is the **recommended deployment method** for running `Solar-Open-100B`.
+
+```bash
+# For 8 GPUs
+docker run --gpus all \
+    --ipc=host \
+    -p 8000:8000 \
+    upstage/vllm-solar-open:latest \
+    upstage/Solar-Open-100B \
+    --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 8
+```
+
+#### Option 2: Installing from Source
+For development, debugging, custom modifications or offline inference, Solar Open can also be run
+using a source installation of vLLM. We recommend using **[uv](https://docs.astral.sh/uv/)** for environment
+management and dependency resolution.
+
+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 8 GPUs)
+```bash
+vllm serve upstage/Solar-Open-100B \
+    --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 8
+```
+
+## Public API Access
+
+The official API service for Solar Open is scheduled to launch publicly on **January**.
+
+* **Access:** Upstage Console (TBA)
+* **Documentation:** Upstage Console (TBA)
+
+## Citation
+
+If you use Solar Open in your research, please cite:
+
+```bibtex
+@misc{solar-open-2025,
+    title={Solar Open: Scaling Upstage's LLM Capabilities with MoE},
+    author={Upstage AI},
+    year={2025},
+    url={https://huggingface.co/Upstage/Solar-Open-100B}
+}
+```