Update README.md

README.md

@@ -1,3 +1,101 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- vllm
+---
+
+
+<!-- <p align="center">
+  <a href="https://gpt-oss.com"><strong>Try gpt-oss</strong></a> ·
+  <a href="https://cookbook.openai.com/topic/gpt-oss"><strong>Guides</strong></a> ·
+  <a href="https://arxiv.org/abs/2508.10925"><strong>Model card</strong></a> ·
+  <a href="https://openai.com/index/introducing-gpt-oss/"><strong>OpenAI blog</strong></a>
+</p> -->
+
+<br>
+
+| Tasks | Metric | gpt-oss-20b | **gpt-oss-46b** | Improvement |
+| :--- | :--- | :---: | :---: | :---: |
+| **GSM8K** (0-shot) | Exact Match (flexible) | 0.1638 | **0.2290** | <font color="green">+39.8%</font> |
+| **LAMBADA** (OpenAI) | Accuracy | **0.2668** | 0.2038 | <font color="red">-23.6%</font> |
+
+
+> [!NOTE]
+> This model card is dedicated to the medium `gpt-oss-46b` model. Check out [`gpt-oss-20b`](https://huggingface.co/openai/gpt-oss-20b) for the smaller model. Check out [`gpt-oss-120b`](https://huggingface.co/openai/gpt-oss-120b) for the larger model.
+
+
+# Inference examples
+
+## Transformers
+
+You can use `gpt-oss` with Transformers. If you use the Transformers chat template, it will automatically apply the [harmony response format](https://github.com/openai/harmony). If you use `model.generate` directly, you need to apply the harmony format manually using the chat template or use our [openai-harmony](https://github.com/openai/harmony) package.
+
+To get started, install the necessary dependencies to setup your environment:
+
+```
+pip install -U transformers kernels torch 
+```
+
+Once, setup you can proceed to run the model by running the snippet below:
+
+```py
+from transformers import pipeline
+import torch
+
+model_id = "Jo1uck/gpt-oss-46b"
+
+pipe = pipeline(
+    "text-generation",
+    model=model_id,
+    torch_dtype="auto",
+    device_map="auto",
+)
+
+messages = [
+    {"role": "user", "content": "Explain quantum mechanics clearly and concisely."},
+]
+
+outputs = pipe(
+    messages,
+    max_new_tokens=256,
+)
+print(outputs[0]["generated_text"][-1])
+```
+
+Alternatively, you can run the model via [`Transformers Serve`](https://huggingface.co/docs/transformers/main/serving) to spin up a OpenAI-compatible webserver:
+
+```
+transformers serve
+transformers chat localhost:8000 --model-name-or-path Jo1uck/gpt-oss-46b
+```
+
+[Learn more about how to use gpt-oss with Transformers.](https://cookbook.openai.com/articles/gpt-oss/run-transformers)
+
+## vLLM
+
+vLLM recommends using [uv](https://docs.astral.sh/uv/) for Python dependency management. You can use vLLM to spin up an OpenAI-compatible webserver. The following command will automatically download the model and start the server.
+
+```bash
+uv pip install --pre vllm==0.10.1+gptoss \
+    --extra-index-url https://wheels.vllm.ai/gpt-oss/ \
+    --extra-index-url https://download.pytorch.org/whl/nightly/cu128 \
+    --index-strategy unsafe-best-match
+
+vllm serve Jo1uck/gpt-oss-46b
+```
+
+[Learn more about how to use gpt-oss with vLLM.](https://cookbook.openai.com/articles/gpt-oss/run-vllm)
+
+
+# Highlights
+
+* **Permissive Apache 2.0 license:** Build freely without copyleft restrictions or patent risk—ideal for experimentation, customization, and commercial deployment.  
+* **Configurable reasoning effort:** Easily adjust the reasoning effort (low, medium, high) based on your specific use case and latency needs.  
+* **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.
+* **MXFP4 quantization:** The models were post-trained with MXFP4 quantization of the MoE weights, making `gpt-oss-120b` run on a single 80GB GPU (like NVIDIA H100 or AMD MI300X) and the `gpt-oss-46b` model run within 16GB of memory. All evals were performed with the same MXFP4 quantization.
+
+---