Model Card for gpt-oss-6.0b-specialized-all-pruned-moe-only-7-experts-GGUF

This repository contains multiple quantized versions of the gpt-oss-6.0b-specialized-all-pruned-moe-only-7-experts model in GGUF format.
It is intended for efficient inference on consumer hardware, making large model deployment more accessible.

Model Details

Model Description

• Developed by: leeminwaan
• Funded by [optional]: Independent project
• Shared by [optional]: leeminwaan
• Model type: Decoder-only transformer language model
• Language(s) (NLP): English (primary), multilingual capabilities not benchmarked
• License: Apache-2.0

Model Sources

• Repository: Hugging Face Repo
• Paper [optional]: Not available
• Demo [optional]: To be released

How to Get Started with the Model

from huggingface_hub import hf_hub_download

model_path = hf_hub_download("leeminwaan/gpt-oss-6.0b-specialized-all-pruned-moe-only-7-experts-GGUF", "gpt-oss-6.0b-specialized-all-pruned-moe-only-7-experts-q4_k_m.gguf")
print("Downloaded:", model_path)

Quantized versions available:

• Q2_K, Q3_K_S, Q3_K_M, Q3_K_L
• Q4_0, Q4_1, Q4_K_S, Q4_K_M
• Q5_0, Q5_1, Q5_K_S, Q5_K_M
• Q6_K, Q8_0

Training Details

Training Data

• Based on gpt-oss-6.0b-specialized-all-pruned-moe-only-7-experts pretraining corpus (public large-scale web text, open datasets).
• No additional fine-tuning was performed for this release.

Training Procedure

• Original gpt-oss-6.0b-specialized-all-pruned-moe-only-7-experts → quantized to GGUF formats.

Quantization Results

Quantization	Size (vs. FP16)	Speed	Quality	Recommended For
Q2_K	Smallest	Fastest	Low	Prototyping, minimal RAM/CPU
Q3_K_S	Very Small	Very Fast	Low-Med	Lightweight devices, testing
Q3_K_M	Small	Fast	Med	Lightweight, slightly better quality
Q3_K_L	Small-Med	Fast	Med	Faster inference, fair quality
Q4_0	Medium	Fast	Good	General use, chats, low RAM
Q4_1	Medium	Fast	Good+	Recommended, slightly better quality
Q4_K_S	Medium	Fast	Good+	Recommended, balanced
Q4_K_M	Medium	Fast	Good++	Recommended, best Q4 option
Q5_0	Larger	Moderate	Very Good	Chatbots, longer responses
Q5_1	Larger	Moderate	Very Good+	More demanding tasks
Q5_K_S	Larger	Moderate	Very Good+	Advanced users, better accuracy
Q5_K_M	Larger	Moderate	Excellent	Demanding tasks, high quality
Q6_K	Large	Slower	Near FP16	Power users, best quantized quality
Q8_0	Largest	Slowest	FP16-like	Maximum quality, high RAM/CPU

Note:

• Lower quantization = smaller model, faster inference, but lower output quality.
• Q4_K_M is ideal for most users; Q6_K/Q8_0 offer the highest quality, best for advanced use.
• All quantizations are suitable for consumer hardware—select based on your quality/speed needs.

Technical Specifications

Software

• llama.cpp for quantization
• Python 3.10, huggingface_hub

Citation

BibTeX:

@miscgpt-oss-6.0b-specialized-all-pruned-moe-only-7-experts-GGUF,
  title=gpt-oss-6.0b-specialized-all-pruned-moe-only-7-experts-GGUF Quantized Models},
  author={leeminwaan},
  year={2025},
  howpublished={\url{https://huggingface.co/leeminwaan/gpt-oss-6.0b-specialized-all-pruned-moe-only-7-experts-GGUF}}
}

APA:

leeminwaan. (2025). gpt-oss-6.0b-specialized-all-pruned-moe-only-7-experts-GGUF Quantized Models [Computer software]. Hugging Face. https://huggingface.co/leeminwaan/gpt-oss-6.0b-specialized-all-pruned-moe-only-7-experts-GGUF

Glossary

• Quantization: Reducing precision of weights to lower memory usage.
• GGUF: Optimized format for llama.cpp inference.

More Information

• This project is experimental.
• Expect further updates and quantization benchmarks.

Model Card Authors

• leeminwaan

Model Card Contact

• Hugging Face: leeminwaan