---
language:
- en
license: mit
tags:
- gpt-oss
- openai
- mxfp4
- mixture-of-experts
- causal-lm
- text-generation
- cpu-gpu-offload
- colab
datasets:
- openai/gpt-oss-training-data   # Placeholder; replace if known
pipeline_tag: text-generation
---

# gpt-oss-20b-offload

This is a CPU+GPU offload‑ready copy of **OpenAI’s GPT‑OSS‑20B** model, an open‑source, Mixture‑of‑Experts large language model released by OpenAI in 2025.  
The model here retains OpenAI’s original **MXFP4 quantization** and is configured for **memory‑efficient loading in Colab or similar GPU environments**.

---

## Model Details

### Model Description

- **Developed by:** OpenAI  
- **Shared by:** saurabh-srivastava (Hugging Face user)  
- **Model type:** Decoder‑only transformer (Mixture‑of‑Experts) for causal language modeling  
- **Active experts per token:** 4 / 32 total experts  
- **Language(s):** English (with capability for multilingual text generation)  
- **License:** MIT (per OpenAI GPT‑OSS release)  
- **Finetuned from model:** `openai/gpt-oss-20b` (no additional fine‑tuning performed)

### Model Sources

- **Original model repository:** [https://huggingface.co/openai/gpt-oss-20b](https://huggingface.co/openai/gpt-oss-20b)  
- **OpenAI announcement:** [https://openai.com/index/introducing-gpt-oss/](https://openai.com/index/introducing-gpt-oss/)  

---

## Uses

### Direct Use
- Text generation, summarization, and question answering.
- Running inference in low‑VRAM environments using CPU+GPU offload.

### Downstream Use
- Fine‑tuning for domain‑specific assistants.
- Integration into chatbots or generative applications.

### Out‑of‑Scope Use
- Generating harmful, biased, or false information.
- Any high‑stakes decision‑making without human oversight.

---

## Bias, Risks, and Limitations

Like all large language models, GPT‑OSS‑20B can:
- Produce factually incorrect or outdated information.
- Reflect biases present in its training data.
- Generate harmful or unsafe content if prompted.

### Recommendations
- Always use with a moderation layer.
- Validate outputs for factual accuracy before use in production.

---

## How to Get Started with the Model

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "your-username/gpt-oss-20b-offload"
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Load with CPU+GPU offload
max_mem = {0: "20GiB", "cpu": "64GiB"}
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto",
    max_memory=max_mem
)

inputs = tokenizer("Explain GPT‑OSS‑20B in one paragraph.", return_tensors="pt").to(0)
outputs = model.generate(**inputs, max_new_tokens=80)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))