justinj92
/

gpt-oss-nemo-20b

@@ -1,199 +1,203 @@
 ---
 library_name: transformers
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 ---
+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.*