SandLogicTechnologies/SOLAR-10.7B-v1.0-GGUF

SOLAR-10.7B (GGUF / Quantized)

SOLAR-10.7B is a large-scale open-weight language model developed by Upstage, built for strong reasoning, instruction-following, and long-context understanding. This repository hosts quantized GGUF variants of the model, enabling efficient inference on local machines and resource-constrained environments.

The provided quantized versions balance performance and memory efficiency, making SOLAR-10.7B suitable for both experimentation and production-style local deployments.

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Model Overview

• Model Name: SOLAR-10.7B
• Base Model: upstage/SOLAR-10.7B-v1.0
• Architecture: Decoder-only Transformer
• Parameter Count: 10.7 Billion
• Context Length: Up to 32K tokens
• Modalities: Text
• Developer: Upstage
• License: Apache 2.0

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Quantization Formats

Q4_K_M

• Approx. 72% size reduction (6.02 GB)
• Significant reduction in memory footprint
• Optimized for CPU-based inference
• Faster token generation on low-VRAM systems
• Suitable for lightweight local usage

Q5_K_M

• Approx. 67% size reduction (7.08 GB)
• Improved numerical precision compared to Q4_k_m
• Better stability for reasoning-heavy prompts
• Recommended for general-purpose workloads

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Training Background (Original Model)

SOLAR-10.7B is trained with a focus on long-context comprehension and high-quality reasoning, enabling it to perform well on complex instructions and extended documents.

Pretraining

• Trained on a large and diverse corpus of English text
• Optimized using autoregressive language modeling objectives
• Emphasis on coherence, factual consistency, and context retention

Instruction Tuning

• Further refined to improve instruction adherence
• Designed for structured outputs and conversational clarity
• Enhanced performance on reasoning and multi-step tasks

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Key Capabilities

• Long-context understanding
  Handles extended prompts, documents, and multi-turn dialogue effectively.

• Instruction-following
  Produces clear and task-aligned responses to user prompts.

• Reasoning and analysis
  Performs well on logical reasoning, explanation, and problem-solving tasks.

• Conversational use
  Maintains context and tone across multi-turn interactions.

• Efficient local inference
  GGUF format supports fast execution via llama.cpp and compatible runtimes.

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Usage Example

llama.cpp

./llama-cli \
  -m SandLogicTechnologies\solar-10.7b_Q4_K_M.gguf \
  -p "What are the main limitations of large language models?"

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Recommended Applications

• Local chat assistants Run a capable conversational model without cloud dependencies.

• Document analysis Useful for summarization and reasoning over long texts.

• Research and evaluation Explore long-context prompting and reasoning benchmarks.

• Privacy-sensitive workflows Keep all data and inference fully offline.

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Acknowledgments

This repository is based on the original SOLAR-10.7B model released by Upstage.

Thanks to:

• The Upstage team for releasing a high-quality open-weight model
• The llama.cpp community for enabling efficient GGUF inference

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Contact

For questions, feedback, or support, please reach out atsupport@sandlogic.com or visit https://www.sandlogic.com/