---
license: apache-2.0
base_model: openai/gpt-oss-20b
tags:
- multilingual
- reasoning
- thinking
- fine-tuned
- lora
- conversational
language:
- multilingual
- en
- es
- ar
- fr
- de
- zh
- ja
- ko
- hi
- ru
datasets:
- HuggingFaceH4/Multilingual-Thinking
library_name: transformers
pipeline_tag: text-generation
---

# GPT-OSS-NEMO-20B: Multilingual Thinking Model

## Model Description

**GPT-OSS-NEMO-20B** is a fine-tuned version of OpenAI's GPT-OSS-20B model, specifically enhanced for multilingual reasoning and thinking capabilities. This model has been trained using Supervised Fine-Tuning (SFT) on the HuggingFaceH4/Multilingual-Thinking dataset to improve its ability to reason in multiple languages while maintaining strong performance across diverse linguistic contexts.

## Key Features

- 🌍 **Multilingual Reasoning**: Enhanced ability to think and reason in multiple languages
- 🧠 **Chain-of-Thought**: Improved reasoning capabilities with explicit thinking processes
- 💬 **Conversational**: Optimized for interactive dialogue and question-answering
- 🎯 **Cross-lingual**: Can reason in one language and respond in another
- ⚡ **High Performance**: Built on the robust 20B parameter GPT-OSS foundation

## Training Details

### Base Model
- **Model**: [openai/gpt-oss-20b](https://huggingface.co/openai/gpt-oss-20b)
- **Parameters**: 20 billion parameters
- **Architecture**: GPT-OSS (Mixture of Experts)

### Fine-tuning Configuration
- **Method**: LoRA (Low-Rank Adaptation)
- **Rank (r)**: 8
- **Alpha**: 16
- **Target Modules**: All linear layers with specific focus on MoE expert layers
- **Target Parameters**: 
  - Layer 7, 15, 23 MLP experts (gate_up_proj, down_proj)

### Training Infrastructure
- **Hardware**: 4x NVIDIA H100 GPUs
- **Cloud Platform**: Microsoft Azure NC-series instances
- **Training Framework**: TRL (Transformers Reinforcement Learning)
- **Optimization**: AdamW with cosine learning rate scheduling

### Training Hyperparameters
- **Learning Rate**: 2e-4
- **Batch Size**: 4 per device (16 total with 4 GPUs)
- **Gradient Accumulation**: 4 steps
- **Epochs**: 4
- **Max Sequence Length**: 2048 tokens
- **Warmup Ratio**: 3%
- **LR Scheduler**: Cosine with minimum LR (10% of peak)
- **Gradient Checkpointing**: Enabled

### Dataset
- **Name**: [HuggingFaceH4/Multilingual-Thinking](https://huggingface.co/datasets/HuggingFaceH4/Multilingual-Thinking)
- **Purpose**: Multilingual reasoning and thinking enhancement
- **Languages**: Multiple languages including English, Spanish, Arabic, French, German, Chinese, Japanese, Korean, Hindi, Russian
- **Training Split**: Full training set

## Usage

### Quick Start

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "justinj92/gpt-oss-nemo-20b",
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("justinj92/gpt-oss-nemo-20b")

# Example: Multilingual reasoning
messages = [
    {"role": "system", "content": "reasoning language: Arabic"},
    {"role": "user", "content": "¿Cuál es la capital de Australia?"}
]

inputs = tokenizer.apply_chat_template(
    messages, 
    add_generation_prompt=True, 
    return_tensors="pt"
)

outputs = model.generate(
    inputs,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
```

### Advanced Usage with Custom Reasoning Language

```python
# Specify reasoning language in system prompt
reasoning_language = "French"  # Can be any supported language
system_prompt = f"reasoning language: {reasoning_language}"

messages = [
    {"role": "system", "content": system_prompt},
    {"role": "user", "content": "Explain quantum computing in simple terms."}
]
```

## Model Capabilities

### Multilingual Reasoning
The model can:
- Think and reason in a specified language (via system prompt)
- Process questions in one language and reason in another
- Maintain coherent logic across language boundaries
- Provide explanations with explicit reasoning steps

### Language Support
Primary languages include:
- **English** (en)
- **Spanish** (es) 
- **Arabic** (ar)
- **French** (fr)
- **German** (de)
- **Chinese** (zh)
- **Japanese** (ja)
- **Korean** (ko)
- **Hindi** (hi)
- **Russian** (ru)

## Performance

The model demonstrates improved performance in:
- Cross-lingual reasoning tasks
- Multi-step problem solving
- Contextual understanding across languages
- Maintaining coherence in multilingual conversations

## Limitations

- Performance may vary across different languages
- Complex reasoning in low-resource languages may be limited
- Generated content should be verified for factual accuracy
- May exhibit biases present in the training data

## Technical Specifications

- **Model Size**: ~20B parameters
- **Precision**: BF16 (Brain Floating Point 16-bit)
- **Memory Requirements**: ~40GB VRAM for inference
- **Recommended Hardware**: NVIDIA A100/H100 or similar high-memory GPUs
- **Framework Compatibility**: transformers, torch, accelerate

## Citation

If you use this model in your research, please cite:

```bibtex
@misc{gpt-oss-nemo-20b,
  title={GPT-OSS-NEMO-20B: A Multilingual Thinking Model},
  author={justinj92},
  year={2025},
  howpublished={\url{https://huggingface.co/justinj92/gpt-oss-nemo-20b}},
  note={Fine-tuned from openai/gpt-oss-20b using HuggingFaceH4/Multilingual-Thinking}
}
```

## Acknowledgments

- **Base Model**: OpenAI GPT-OSS-20B team
- **Dataset**: HuggingFace H4 team for the Multilingual-Thinking dataset
- **Infrastructure**: Microsoft Azure for cloud computing resources
- **Framework**: Hugging Face transformers and TRL libraries

## License

This model is released under the Apache 2.0 license, following the base model's licensing terms.

---

*Model trained on August 2025 using state-of-the-art multilingual reasoning techniques.*