The provided text describes the Hugging Face repository for the Solar-Open-100B large language model developed by Upstage AI, a Korean startup. It is not a traditional research paper but rather metadata and configuration details for the model.

Core Aspects of the Model:

1. Architecture and Scale: Solar-Open-100B is identified as a 100 billion parameter (100B) model, utilizing a Mixture of Experts (MoE) architecture. The safetensors information indicates its parameters are predominantly in BF16 (102,651,793,408 parameters) with a smaller F32 component (6144 parameters). The model weights are sharded.
2. Training and Development: The model is trained by Upstage AI, implying a focus on both English and Korean languages, as suggested by the en and ko tags. Its knowledge cutoff date is stated as 2025-07, indicating recent training or an intended future update.
3. Application and Capabilities: The model is designed for text-generation and conversational tasks. A significant part of the provided information is its chat_template.jinja, which outlines its interaction protocol and capabilities:
   • System Prompt Customization: It supports a default provider system prompt (identifying itself as Solar Open 100B, trained by Upstage AI, with a specific knowledge cutoff and current date) and allows for user-defined system messages.
   • Tool Usage: The model is equipped for tool invocation. The template explicitly defines instructions for tool calls, including:
     • Tool Call Instruction: States that the model can invoke one or more tools, with available tools provided in JSON Schema format.
     • Tool Call Format: Specifies that tool calls should be returned as JSON objects within $<tool_code>$ and $</tool_code>$ tags. Each tool call requires a randomly generated 10-character alphanumeric id (e.g., a1b2c3d4e5).
     • Tool Response Format: Details that tool responses from the environment (tool role) should match the corresponding tool call's id.
   • JSON Response Formatting: The model can be constrained to output responses following a specified JSON schema, enclosed within [Start of schema] and [End of schema] markers.
   • Reasoning Capability: The template includes a reasoning field for the assistant's messages, suggesting that the model can be configured to expose its internal thought process or reasoning steps. This can be rendered either for "all" assistant turns or only for the "lastthink" before generating the final response, controlled by the think_render_option parameter. The reasoning_effort parameter (low, minimal, high) further indicates a configurable level of detail for this reasoning.
4. Technical Implementation:
   • The model integrates with the Hugging Face transformers library, using AutoModelForCausalLM and a custom class modeling_solar_open.SolarOpenForCausalLM.
   • File references indicate the presence of config.json, generation_config.json, and Python files (configuration_solar_open.py, modeling_solar_open.py) that define the model's specific configuration and architecture.
   • The license is specified as Apache License 2.0.

In essence, the provided document details the structural and interactive framework of the Solar-Open-100B model, highlighting its large scale, MoE architecture, multilingual support, advanced tool-use capabilities, and explicit reasoning pathways, all within the Hugging Face ecosystem.