Spaces:
Running
on
Zero
Running
on
Zero
| title: ZeroGPU-LLM-Inference | |
| emoji: 🧠 | |
| colorFrom: pink | |
| colorTo: purple | |
| sdk: gradio | |
| sdk_version: 5.29.0 | |
| app_file: app.py | |
| pinned: false | |
| license: apache-2.0 | |
| short_description: Streaming LLM chat with web search and debug | |
| This Gradio app provides **token-streaming, chat-style inference** on a wide variety of Transformer models—leveraging ZeroGPU for free GPU acceleration on HF Spaces. | |
| Key features: | |
| - **Real-time DuckDuckGo web search** (background thread, configurable timeout) with results injected into the system prompt. | |
| - **Prompt preview panel** for debugging and prompt-engineering insights—see exactly what’s sent to the model. | |
| - **Thought vs. Answer streaming**: any `<think>…</think>` blocks emitted by the model are shown as separate “💭 Thought.” | |
| - **Cancel button** to immediately stop generation. | |
| - **Dynamic system prompt**: automatically inserts today’s date when you toggle web search. | |
| - **Extensive model selection**: over 30 LLMs (from Phi-4 mini to Qwen3-14B, SmolLM2, Taiwan-ELM, Mistral, Meta-Llama, MiMo, Gemma, DeepSeek-R1, etc.). | |
| - **Memory-safe design**: loads one model at a time, clears cache after each generation. | |
| - **Customizable generation parameters**: max tokens, temperature, top-k, top-p, repetition penalty. | |
| - **Web-search settings**: max results, max chars per result, search timeout. | |
| - **Requirements pinned** to ensure reproducible deployment. | |
| ## 🔄 Supported Models | |
| Use the dropdown to select any of these: | |
| | Name | Repo ID | | |
| | ------------------------------------- | -------------------------------------------------- | | |
| | Taiwan-ELM-1_1B-Instruct | liswei/Taiwan-ELM-1_1B-Instruct | | |
| | Taiwan-ELM-270M-Instruct | liswei/Taiwan-ELM-270M-Instruct | | |
| | Qwen3-0.6B | Qwen/Qwen3-0.6B | | |
| | Qwen3-1.7B | Qwen/Qwen3-1.7B | | |
| | Qwen3-4B | Qwen/Qwen3-4B | | |
| | Qwen3-8B | Qwen/Qwen3-8B | | |
| | Qwen3-14B | Qwen/Qwen3-14B | | |
| | Gemma-3-4B-IT | unsloth/gemma-3-4b-it | | |
| | SmolLM2-135M-Instruct-TaiwanChat | Luigi/SmolLM2-135M-Instruct-TaiwanChat | | |
| | SmolLM2-135M-Instruct | HuggingFaceTB/SmolLM2-135M-Instruct | | |
| | SmolLM2-360M-Instruct-TaiwanChat | Luigi/SmolLM2-360M-Instruct-TaiwanChat | | |
| | Llama-3.2-Taiwan-3B-Instruct | lianghsun/Llama-3.2-Taiwan-3B-Instruct | | |
| | MiniCPM3-4B | openbmb/MiniCPM3-4B | | |
| | Qwen2.5-3B-Instruct | Qwen/Qwen2.5-3B-Instruct | | |
| | Qwen2.5-7B-Instruct | Qwen/Qwen2.5-7B-Instruct | | |
| | Phi-4-mini-Reasoning | microsoft/Phi-4-mini-reasoning | | |
| | Phi-4-mini-Instruct | microsoft/Phi-4-mini-instruct | | |
| | Meta-Llama-3.1-8B-Instruct | MaziyarPanahi/Meta-Llama-3.1-8B-Instruct | | |
| | DeepSeek-R1-Distill-Llama-8B | unsloth/DeepSeek-R1-Distill-Llama-8B | | |
| | Mistral-7B-Instruct-v0.3 | MaziyarPanahi/Mistral-7B-Instruct-v0.3 | | |
| | Qwen2.5-Coder-7B-Instruct | Qwen/Qwen2.5-Coder-7B-Instruct | | |
| | Qwen2.5-Omni-3B | Qwen/Qwen2.5-Omni-3B | | |
| | MiMo-7B-RL | XiaomiMiMo/MiMo-7B-RL | | |
| *(…and more can easily be added in `MODELS` in `app.py`.)* | |
| ## ⚙️ Generation & Search Parameters | |
| - **Max Tokens**: 64–16384 | |
| - **Temperature**: 0.1–2.0 | |
| - **Top-K**: 1–100 | |
| - **Top-P**: 0.1–1.0 | |
| - **Repetition Penalty**: 1.0–2.0 | |
| - **Enable Web Search**: on/off | |
| - **Max Results**: integer | |
| - **Max Chars/Result**: integer | |
| - **Search Timeout (s)**: 0.0–30.0 | |
| ## 🚀 How It Works | |
| 1. **User message** enters chat history. | |
| 2. If search is enabled, a background DuckDuckGo thread fetches snippets. | |
| 3. After up to *Search Timeout* seconds, snippets merge into the system prompt. | |
| 4. The selected model pipeline is loaded (bf16→f16→f32 fallback) on ZeroGPU. | |
| 5. Prompt is formatted—any `<think>…</think>` blocks will be streamed as separate “💭 Thought.” | |
| 6. Tokens stream to the Chatbot UI. Press **Cancel** to stop mid-generation. |