Fixed readme

README.md

@@ -7,7 +7,7 @@ tags:
 ---
 
 <p align="center">
-  <img alt="gpt-oss-120b" src="https://raw.githubusercontent.com/openai/gpt-oss/main/docs/gpt-oss-120b.svg">
+  <img alt="gpt-oss-20b" src="https://raw.githubusercontent.com/openai/gpt-oss/main/docs/gpt-oss-20b.svg">
 </p>
 
 <p align="center">
@@ -22,14 +22,14 @@ tags:
 Welcome to the gpt-oss series, [OpenAI’s open-weight models](https://openai.com/open-models) designed for powerful reasoning, agentic tasks, and versatile developer use cases.
 
 We’re releasing two flavors of these open models:
-- `gpt-oss-120b` — for production, general purpose, high reasoning use cases that fit into a single H100 GPU (117B parameters with 5.1B active parameters)
+- `gpt-oss-120b` — for production, general purpose, high reasoning use cases that fit into a single 80GB GPU (like NVIDIA H100 or AMD MI300X) (117B parameters with 5.1B active parameters)
 - `gpt-oss-20b` — for lower latency, and local or specialized use cases (21B parameters with 3.6B active parameters)
 
 Both models were trained on our [harmony response format](https://github.com/openai/harmony) and should only be used with the harmony format as it will not work correctly otherwise.
 
 
 > [!NOTE]
-> This model card is dedicated to the larger `gpt-oss-120b` model. Check out [`gpt-oss-20b`](https://huggingface.co/openai/gpt-oss-20b) for the smaller model.
+> This model card is dedicated to the smaller `gpt-oss-20b` model. Check out [`gpt-oss-120b`](https://huggingface.co/openai/gpt-oss-120b) for the larger model.
 
 # Highlights
 
@@ -38,7 +38,7 @@ Both models were trained on our [harmony response format](https://github.com/ope
 * **Full chain-of-thought:** Gain complete access to the model’s reasoning process, facilitating easier debugging and increased trust in outputs. It’s not intended to be shown to end users.  
 * **Fine-tunable:** Fully customize models to your specific use case through parameter fine-tuning.
 * **Agentic capabilities:** Use the models’ native capabilities for function calling, [web browsing](https://github.com/openai/gpt-oss/tree/main?tab=readme-ov-file#browser), [Python code execution](https://github.com/openai/gpt-oss/tree/main?tab=readme-ov-file#python), and Structured Outputs.
-* **Native MXFP4 quantization:** The models are trained with native MXFP4 precision for the MoE layer, making `gpt-oss-120b` run on a single H100 GPU and the `gpt-oss-20b` model run within 16GB of memory.
+* **Native MXFP4 quantization:** The models are trained with native MXFP4 precision for the MoE layer, making `gpt-oss-120b` run on a single 80GB GPU (like NVIDIA H100 or AMD MI300X) and the `gpt-oss-20b` model run within 16GB of memory.
 
 ---
 
@@ -60,7 +60,7 @@ Once, setup you can proceed to run the model by running the snippet below:
 from transformers import pipeline
 import torch
 
-model_id = "openai/gpt-oss-120b"
+model_id = "openai/gpt-oss-20b"
 
 pipe = pipeline(
     "text-generation",
@@ -84,7 +84,7 @@ Alternatively, you can run the model via [`Transformers Serve`](https://huggingf
 
 ```
 transformers serve
-transformers chat localhost:8000 --model-name-or-path openai/gpt-oss-120b
+transformers chat localhost:8000 --model-name-or-path openai/gpt-oss-20b
 ```
 
 [Learn more about how to use gpt-oss with Transformers.](https://cookbook.openai.com/articles/gpt-oss/run-transformers)
@@ -99,7 +99,7 @@ uv pip install --pre vllm==0.10.1+gptoss \
     --extra-index-url https://download.pytorch.org/whl/nightly/cu128 \
     --index-strategy unsafe-best-match
 
-vllm serve openai/gpt-oss-120b
+vllm serve openai/gpt-oss-20b
 ```
 
 [Learn more about how to use gpt-oss with vLLM.](https://cookbook.openai.com/articles/gpt-oss/run-vllm)
@@ -113,9 +113,9 @@ To learn about how to use this model with PyTorch and Triton, check out our [ref
 If you are trying to run gpt-oss on consumer hardware, you can use Ollama by running the following commands after [installing Ollama](https://ollama.com/download).
 
 ```bash
-# gpt-oss-120b
-ollama pull gpt-oss:120b
-ollama run gpt-oss:120b
+# gpt-oss-20b
+ollama pull gpt-oss:20b
+ollama run gpt-oss:20b
 ```
 
 [Learn more about how to use gpt-oss with Ollama.](https://cookbook.openai.com/articles/gpt-oss/run-locally-ollama)
@@ -125,8 +125,8 @@ ollama run gpt-oss:120b
 If you are using [LM Studio](https://lmstudio.ai/) you can use the following commands to download.
 
 ```bash
-# gpt-oss-120b
-lms get openai/gpt-oss-120b
+# gpt-oss-20b
+lms get openai/gpt-oss-20b
 ```
 
 Check out our [awesome list](https://github.com/openai/gpt-oss/blob/main/awesome-gpt-oss.md) for a broader collection of gpt-oss resources and inference partners.
@@ -138,8 +138,8 @@ Check out our [awesome list](https://github.com/openai/gpt-oss/blob/main/awesome
 You can download the model weights from the [Hugging Face Hub](https://huggingface.co/collections/openai/gpt-oss-68911959590a1634ba11c7a4) directly from Hugging Face CLI:
 
 ```shell
-# gpt-oss-120b
-huggingface-cli download openai/gpt-oss-120b --include "original/*" --local-dir gpt-oss-120b/
+# gpt-oss-20b
+huggingface-cli download openai/gpt-oss-20b --include "original/*" --local-dir gpt-oss-20b/
 pip install gpt-oss
 python -m gpt_oss.chat model/
 ```
@@ -165,4 +165,4 @@ The gpt-oss models are excellent for:
 
 Both gpt-oss models can be fine-tuned for a variety of specialized use cases.
 
-This larger model `gpt-oss-120b` can be fine-tuned on a single H100 node, whereas the smaller [`gpt-oss-20b`](https://huggingface.co/openai/gpt-oss-20b) can even be fine-tuned on consumer hardware.
+This smaller model `gpt-oss-20b` can be fine-tuned on consumer hardware, whereas the larger [`gpt-oss-120b`](https://huggingface.co/openai/gpt-oss-120b) can be fine-tuned on a single H100 node.