thinhlpg
3 months ago
parent
d8e949ec7c
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Cook vllm - sglang - test eval script\n",
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"## OpenAI API for reference\n",
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"- https://platform.openai.com/docs/api-reference/completions/create\n",
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"- https://platform.openai.com/docs/api-reference/completions/create"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Cook vllm\n",
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"- https://docs.vllm.ai/en/latest/getting_started/examples/gradio_webserver.html\n",
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"- https://docs.vllm.ai/en/latest/getting_started/examples/gradio_openai_chatbot_webserver.html\n",
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"- https://docs.vllm.ai/en/latest/getting_started/examples/basic.html#gguf"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"!vllm serve meta-llama/Llama-3.2-1B-Instruct --dtype auto"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Cook sglang\n",
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"- https://docs.sglang.ai/backend/server_arguments.html\n",
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"- > lora_paths: You may provide a list of adapters to your model as a list. Each batch element will get model response with the corresponding lora adapter applied. Currently cuda_graph and radix_attention are not supported with this option so you need to disable them manually. We are still working on through these issues.\n",
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"- > To enable multi-GPU data parallelism, add --dp 2. Data parallelism is better for throughput if there is enough memory."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# %pip install \"sglang[all]>=0.4.5\" --find-links https://flashinfer.ai/whl/cu124/torch2.5/flashinfer-python -q\n",
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"!python3 -m sglang.launch_server --model-path meta-llama/Llama-3.2-1B-Instruct --dp 2 --port 30000"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import requests\n",
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"\n",
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"url = f\"http://localhost:30000/v1/chat/completions\"\n",
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"\n",
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"data = {\n",
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" \"model\": \"meta-llama/Llama-3.2-1B-Instruct\",\n",
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" \"messages\": [{\"role\": \"user\", \"content\": \"Hello em an com chua?\"}],\n",
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"}\n",
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"\n",
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"response = requests.post(url, json=data)\n",
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"print(response.json())"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Cook eval script"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Command to launch SGLang server\n",
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"!python3 -m sglang.launch_server \\\n",
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" --model-path \"janhq/250404-llama-3.2-3b-instruct-grpo-03-s250\" \\\n",
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" --context-length 8192 \\\n",
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" --enable-metrics \\\n",
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" --dtype bfloat16 \\\n",
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" --host 0.0.0.0 \\\n",
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" --port 8002 \\\n",
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" --trust-remote-code \\\n",
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" --disable-overlap \\\n",
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" --disable-radix-cache"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# cd scripts/evaluation\n",
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"!python run_eval.py \\\n",
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" --config_path eval_config.yaml \\\n",
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" --method_name research \\\n",
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" --data_dir {root/path/to/evaluation/data} \\\n",
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" --dataset_name bamboogle \\\n",
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" --split test \\\n",
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" --save_dir {your-save-dir} \\\n",
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" --save_note research_qwen7b_ins\n",
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" --sgl_remote_url {your-launched-sgl-url} \\\n",
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" --remote_retriever_url {your-hosted-retriever-url} \\\n",
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" --generator_model {your-local-model-path} \\\n",
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" --apply_chat True"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "deepsearch-py311",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.11"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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@ -0,0 +1,107 @@
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Getting Familiar\n",
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"\n",
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"https://www.gradio.app/guides/agents-and-tool-usage"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import gradio as gr\n",
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"\n",
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"with gr.Blocks() as demo:\n",
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" chatbot = gr.Chatbot(\n",
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" type=\"messages\",\n",
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" value=[\n",
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" gr.ChatMessage(role=\"user\", content=\"What is the weather in San Francisco?\"),\n",
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" gr.ChatMessage(\n",
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" role=\"assistant\", content=\"I need to use the weather API tool?\", metadata={\"title\": \"🧠 Thinking\"}\n",
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" ),\n",
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" ],\n",
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" )\n",
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"\n",
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"demo.launch()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import gradio as gr\n",
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"from transformers import Tool, ReactCodeAgent # type: ignore\n",
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"from transformers.agents import stream_to_gradio, HfApiEngine # type: ignore\n",
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"\n",
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"# Import tool from Hub\n",
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"image_generation_tool = Tool.from_space(\n",
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" space_id=\"black-forest-labs/FLUX.1-schnell\",\n",
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" name=\"image_generator\",\n",
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" description=\"Generates an image following your prompt. Returns a PIL Image.\",\n",
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" api_name=\"/infer\",\n",
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")\n",
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"\n",
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"llm_engine = HfApiEngine(\"Qwen/Qwen2.5-Coder-32B-Instruct\")\n",
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"# Initialize the agent with both tools and engine\n",
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"agent = ReactCodeAgent(tools=[image_generation_tool], llm_engine=llm_engine)\n",
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"\n",
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"def interact_with_agent(prompt, history):\n",
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" messages = []\n",
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" yield messages\n",
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" for msg in stream_to_gradio(agent, prompt):\n",
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" messages.append(asdict(msg))\n",
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" yield messages\n",
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" yield messages\n",
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"\n",
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"\n",
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"demo = gr.ChatInterface(\n",
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" interact_with_agent,\n",
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" chatbot= gr.Chatbot(\n",
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" label=\"Agent\",\n",
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" type=\"messages\",\n",
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" avatar_images=(\n",
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" None,\n",
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" \"https://em-content.zobj.net/source/twitter/53/robot-face_1f916.png\",\n",
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" ),\n",
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" ),\n",
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" examples=[\n",
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" [\"Generate an image of an astronaut riding an alligator\"],\n",
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" [\"I am writing a children's book for my daughter. Can you help me with some illustrations?\"],\n",
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" ],\n",
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" type=\"messages\",\n",
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")\n",
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"\n",
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"demo.launch()"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "deepsearch-py311-2",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.11"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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@ -0,0 +1,80 @@
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Cook Search APIs\n",
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"- This notebook is used to cook search APIs like Tavily and Serper."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import http.client\n",
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"import json\n",
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"import os\n",
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"\n",
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"from dotenv import load_dotenv\n",
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"\n",
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"load_dotenv(override=True)\n",
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"\n",
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"# https://docs.spaceserp.com/serp-api\n",
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"conn = http.client.HTTPSConnection(\"google.serper.dev\")\n",
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"payload = json.dumps({\"q\": \"apple inc\"})\n",
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"headers = {\n",
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" \"X-API-KEY\": os.getenv(\"SERPER_API_KEY\"),\n",
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" \"Content-Type\": \"application/json\",\n",
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"}\n",
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"conn.request(\"POST\", \"/search\", payload, headers)\n",
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"res = conn.getresponse()\n",
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"data = res.read()\n",
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"print(data.decode(\"utf-8\"))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"\n",
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"from dotenv import load_dotenv\n",
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"from tavily import TavilyClient\n",
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"\n",
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"load_dotenv(override=True)\n",
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"\n",
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"tavily_client = TavilyClient(api_key=os.getenv(\"TAVILY_API_KEY\"))\n",
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"# https://docs.tavily.com/documentation/api-reference/endpoint/search\n",
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"response = tavily_client.search(\n",
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" query=\"Who is Leo Messi?\",\n",
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" search_depth=\"basic\",\n",
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" topic=\"general\",\n",
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" max_results=2,\n",
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" include_answer=True,\n",
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" include_raw_content=False,\n",
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" include_images=False,\n",
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")\n",
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"\n",
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"print(response)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "deepsearch-py311-2",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"name": "python",
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"version": "3.11.11"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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