Model Card for gpt-oss-10b-quants

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
• Finetuned from model [optional]: openai/gpt-oss-20b (via pruning and expert selection)

Model Sources

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

Uses

Direct Use

• Text generation
• Experimentation with quantization formats
• Running benchmarks on low-resource hardware

Downstream Use

• Fine-tuning for chatbots, classification, summarization, or research projects
• Integration into lightweight inference pipelines

Out-of-Scope Use

• High-stakes decision making (medical, legal, financial)
• Content moderation without further fine-tuning
• Applications requiring guaranteed factual accuracy

Bias, Risks, and Limitations

• May reproduce societal biases from training data
• Limited evaluation on multilingual or domain-specific text
• Quantization may degrade accuracy slightly compared to full precision

Recommendations

• Run evaluations before production deployment
• Do not use outputs as factual truth without human verification

How to Get Started with the Model

• 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-20B pretraining corpus (public large-scale web text, open datasets).
• No additional fine-tuning was performed for this release.

Training Procedure

• Original GPT-OSS-20B → pruned to 10B experts → quantized to GGUF formats.

Preprocessing

• Standard tokenization, no special preprocessing for quantization.

Training Hyperparameters

• Quantization only; no gradient updates performed.
• Storage optimized for GGUF inference.

Speeds, Sizes, Times

• Full FP16 checkpoint size ~20B → reduced to ~10B experts → GGUF quantizations ranging from ~3GB to ~7GB.

Evaluation

Testing Data

• No dedicated evaluation dataset; informal testing on open prompts.

Factors

• Quantization level strongly affects perplexity and memory footprint.

Metrics

• Perplexity (approximate, not benchmarked formally).
• Memory usage on consumer GPUs/CPUs.

Results

• Q8_0 maintains near full precision quality.
• Q4_K_M, Q5_K_M provide good trade-off between performance and quality.

Summary

Model Examination

• No interpretability analysis yet.

Technical Specifications

Model Architecture and Objective

• Decoder-only Transformer
• Optimized for text generation

Compute Infrastructure

Hardware

• Single RTX 3090 (24GB VRAM) for quantization tasks

Software

• llama.cpp for quantization
• Python 3.10, huggingface_hub

Citation

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