From 20a1d1f9c34fcc412a333688a4c2e96ad68198fd Mon Sep 17 00:00:00 2001 From: ai-modelscope Date: Sat, 9 Aug 2025 08:11:54 +0800 Subject: [PATCH] Update chat_template.jinja (#85) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - Update chat_template.jinja (41876e41628351e3e23273661c25adf2114bf8af) Co-authored-by: Quentin Gallouédec --- README.md | 4 ++-- chat_template.jinja | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 70f5053..f3240db 100644 --- a/README.md +++ b/README.md @@ -22,7 +22,7 @@ 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. @@ -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. --- diff --git a/chat_template.jinja b/chat_template.jinja index 67a2512..dc7bb11 100644 --- a/chat_template.jinja +++ b/chat_template.jinja @@ -245,9 +245,9 @@ {%- if developer_message %} {{- "# Instructions\n\n" }} {{- developer_message }} + {{- "\n\n" }} {%- endif %} {%- if tools -%} - {{- "\n\n" }} {{- "# Tools\n\n" }} {{- render_tool_namespace("functions", tools) }} {%- endif -%}