bingbangboom/holmes
TEXT GENERATIONConcurrency Cost:1Model Size:0.8BQuant:BF16Ctx Length:32kPublished:Mar 25, 2026Architecture:Transformer Warm
bingbangboom/holmes is a 0.8 billion parameter language model, fine-tuned and converted to GGUF format using Unsloth. This model is designed for efficient deployment and usage with tools like llama.cpp and Ollama. Its small parameter count and GGUF format make it suitable for local inference on resource-constrained devices.
Loading preview...
Model Overview
bingbangboom/holmes is a compact 0.8 billion parameter language model, specifically prepared for efficient local deployment. It has been fine-tuned and converted into the GGUF format, leveraging the Unsloth framework, which is noted for accelerating training processes.
Key Characteristics
- Parameter Count: At 0.8 billion parameters, it is a lightweight model, ideal for edge devices or environments with limited computational resources.
- GGUF Format: Provided in GGUF format, ensuring compatibility with
llama.cppand similar inference engines. - Ollama Support: Includes an Ollama Modelfile for streamlined integration and deployment within the Ollama ecosystem.
- Unsloth Optimization: The model's training benefited from Unsloth, indicating potential for faster fine-tuning and efficient resource utilization during its development.
Good For
- Local Inference: Excellent for running language model tasks directly on personal hardware without requiring powerful GPUs or cloud services.
- Resource-Constrained Environments: Its small size makes it suitable for applications where memory and processing power are limited.
- Rapid Prototyping: The ease of deployment with Ollama and GGUF format facilitates quick experimentation and development.