Create README.md

updated model card

README.md

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+---
+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
+---
+
+# Model Card for 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+- Produce factually incorrect or outdated information.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))