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feat: add new demo
2026-01-24 20:59:06 +08:00
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"reasoning_content": "The user is asking me to explain \"MOE\" to a 16-year-old boy. I need to understand what \"MOE\" refers to. There are multiple possible meanings: Ministry of Education (in some countries), Moe from The Simpsons, or perhaps \"moe\" as an anime term (萌え). Given the context of explaining to a 16-year-old boy, likely the anime term \"moe\" is relevant. However, I should clarify to ensure I'm addressing the correct meaning. Let me ask for clarification."
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"content": "🤔 I want to make sure I explain the right concept to a 16-year-old boy, as different meanings would require very different explanations.\n\nWhat does \"MOE\" refer to? There are several possibilities: 1) \"Moe\" as an anime term (萌え) meaning cute, affectionate feelings towards characters; 2) \"Moe\" as a character from The Simpsons; 3) \"MOE\" as an acronym like Ministry of Education; 4) Something else. Which one should I explain?\n\n 1. Anime term 'moe' (萌え)\n 2. Moe from The Simpsons\n 3. MOE as an acronym (e.g., Ministry of Education)\n 4. Other meaning",
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"content": "🤔 I want to make sure I explain the right concept to a 16-year-old boy, as different meanings would require very different explanations.\n\nWhat does \"MOE\" refer to? There are several possibilities: 1) \"Moe\" as an anime term (萌え) meaning cute, affectionate feelings towards characters; 2) \"Moe\" as a character from The Simpsons; 3) \"MOE\" as an acronym like Ministry of Education; 4) Something else. Which one should I explain?\n\n 1. Anime term 'moe' (萌え)\n 2. Moe from The Simpsons\n 3. MOE as an acronym (e.g., Ministry of Education)\n 4. Other meaning",
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"content": "[\n {\n \"title\": \"Mixture of Experts Models: Explained Simply\",\n \"url\": \"https://dianawolftorres.substack.com/p/mixture-of-experts-models-explained\",\n \"snippet\": \"Much like how a large organization benefits from having specialized departments rather than making every employee a generalist, MoE models achieve remarkable efficiency by breaking down complex tasks into smaller, more manageable pieces handled by specialized components.**. Unlike traditional neural networks where all neurons might activate for every input, MoE models only activate a small subset of experts for each task. **Mixture of Experts (MoE):** An AI architecture that uses multiple specialized neural networks (experts) working together, each handling specific aspects of tasks they're best suited for. **Expert Network:** A specialized neural network trained to handle specific types of patterns or tasks within the larger MoE system. **How is MoE different from traditional AI models?** *Traditional models activate all their parameters for every task, while MoE models selectively activate only the most relevant experts, significantly improving efficiency.*. **What makes MoE models more efficient?** *By activating only the necessary experts for each task, MoE models can achieve better results while using a fraction of the computing power required by traditional models.*.\"\n },\n {\n \"title\": \"Mixture of Experts Explained - Hugging Face\",\n \"url\": \"https://huggingface.co/blog/moe\",\n \"snippet\": \"With the release of Mixtral 8x7B (announcement, model card), a class of transformer has become the hottest topic in the open AI community: Mixture of Experts, or MoEs for short. How to route a token to an expert is one of the big decisions when working with MoEs - the router is composed of learned parameters and is pretrained at the same time as the rest of the network. So, to recap, in MoEs we replace every FFN layer of the transformer model with an MoE layer, which is composed of a gate network and a certain number of experts. That’s because in MoE models, only the FFN layers are treated as individual experts, and the rest of the model parameters are shared. The idea of conditional computation (parts of the network are active on a per-example basis) allows one to scale the size of the model without increasing the computation, and hence, this led to thousands of experts being used in each MoE layer.\"\n },\n {\n \"title\": \"Understanding Mixture of Experts (MoE): A Beginner's Guide with ...\",\n \"url\": \"https://ai-brewery.medium.com/understanding-mixture-of-experts-moe-a-beginners-guide-with-pytorch-933eb50f7f58\",\n \"snippet\": \"In our code, we’ll create an MoE with three experts, pick the top two for each input, and compare it to a single large neural network. class MoE(nn.Module): def __init__(self, experts, top_k=2): super(MoE, self).__init__() self.experts = nn.ModuleList(experts) self.gating = Gating(input_dim=experts[0].layer1.in_features, num_experts=len(experts)) self.top_k = top_k def forward(self, x): weights = self.gating(x) top_k_weights, top_k_indices = torch.topk(weights, k=self.top_k, dim=1) top_k_weights = top_k_weights / top_k_weights.sum(dim=1, keepdim=True) batch_size = x.size(0) output = torch.zeros(batch_size, self.experts[0].layer2.out_features, device=x.device) for i in range(batch_size): for k in range(self.top_k): expert_idx = top_k_indices[i, k] weight = top_k_weights[i, k] expert_output = self.expertsexpert_idx output[i] += weight * expert_output.squeeze(0) return output. moe_model = MoE(experts, top_k=2)optimizer_moe = optim.Adam(moe_model.parameters(), lr=learning_rate)for epoch in range(epochs): optimizer_moe.zero_grad() outputs_moe = moe_model(x_train) loss_moe = nn.CrossEntropyLoss()(outputs_moe, y_train) loss_moe.backward() optimizer_moe.step() if (epoch + 1) % 20 == 0: print(f\\\"MoE Epoch [{epoch+1}/{epochs}], Loss: {loss_moe.item():.4f}\\\"). The Mixture of Experts (MoE) model used far fewer parameters than the single neural network — only 649
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"reasoning_content": "Now I need to fetch one of these resources to get a good explanation. The Hugging Face blog or the first result would be good. Let me fetch the Hugging Face blog."
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"content": "# Mixture of Experts Explained\n\n[Back to Articles](/blog)\n\n[![Omar Sanseviero's avatar](https://cdn-avatars.huggingface.co/v1/production/uploads/6032802e1f993496bc14d9e3/w6hr-DEQot4VVkoyRIBiy.png)](/osanseviero)\n\n[![Lewis Tunstall's avatar](https://cdn-avatars.huggingface.co/v1/production/uploads/1594651707950-noauth.jpeg)](/lewtun)\n\n[![Philipp Schmid's avatar](https://cdn-avatars.huggingface.co/v1/production/uploads/1624629516652-5ff5d596f244529b3ec0fb89.png)](/philschmid)\n\n[![Sourab Mangrulkar's avatar](https://cdn-avatars.huggingface.co/v1/production/uploads/1638132956881-5fca176d1d7a08cb34d79d5d.jpeg)](/smangrul)\n\n[![Younes B's avatar](https://cdn-avatars.huggingface.co/v1/production/uploads/1648631057413-noauth.png)](/ybelkada)\n\n[![Pedro Cuenca's avatar](https://cdn-avatars.huggingface.co/v1/production/uploads/1617264212503-603d25b75f9d390ab190b777.jpeg)](/pcuenq)\n\n* [Table of Contents](#table-of-contents \"Table of Contents\")\n* [TL;DR](#tldr \"TL;DR\")\n* [What is a Mixture of Experts (MoE)?](#what-is-a-mixture-of-experts-moe \"What is a Mixture of Experts (MoE)?\")\n* [A Brief History of MoEs](#a-brief-history-of-moes \"A Brief History of MoEs\")\n* [What is Sparsity?](#what-is-sparsity \"What is Sparsity?\")\n* [Load balancing tokens for MoEs](#load-balancing-tokens-for-moes \"Load balancing tokens for MoEs\")\n* [MoEs and Transformers](#moes-and-transformers \"MoEs and Transformers\")\n* [Switch Transformers](#switch-transformers \"Switch Transformers\")\n* [Stabilizing training with router Z-loss](#stabilizing-training-with-router-z-loss \"Stabilizing training with router Z-loss\")\n* [What does an expert learn?](#what-does-an-expert-learn \"What does an expert learn?\")\n* [How does scaling the number of experts impact pretraining?](#how-does-scaling-the-number-of-experts-impact-pretraining \"How does scaling the number of experts impact pretraining?\")\n* [Fine-tuning MoEs](#fine-tuning-moes \"Fine-tuning MoEs\")\n* [When to use sparse MoEs vs dense models?](#when-to-use-sparse-moes-vs-dense-models \"When to use sparse MoEs vs dense models?\")\n* [Making MoEs go brrr](#making-moes-go-brrr \"Making MoEs go brrr\")\n + [Parallelism](#parallelism \"Parallelism\")\n + [Capacity Factor and communication costs](#capacity-factor-and-communication-costs \"Capacity Factor and communication costs\")\n + [Serving techniques](#serving-techniques \"Serving techniques\")\n + [More on efficient training](#more-on-efficient-training \"More on efficient training\")\n* [Open Source MoEs](#open-source-moes \"Open Source MoEs\")\n* [Exciting directions of work](#exciting-directions-of-work \"Exciting directions of work\")\n* [Some resources](#some-resources \"Some resources\")\n* [Citation](#citation \"Citation\")\n\nWith the release of Mixtral 8x7B ([announcement](https://mistral.ai/news/mixtral-of-experts/), [model card](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1)), a class of transformer has become the hottest topic in the open AI community: Mixture of Experts, or MoEs for short. In this blog post, we take a look at the building blocks of MoEs, how they’re trained, and the tradeoffs to consider when serving them for inference.\n\nLet’s dive in!\n\nTable of Contents\n-----------------\n\n* [What is a Mixture of Experts?](#what-is-a-mixture-of-experts-moe)\n* [A Brief History of MoEs](#a-brief-history-of-moes)\n* [What is Sparsity?](#what-is-sparsity)\n* [Load Balancing tokens for MoEs](#load-balancing-tokens-for-moes)\n* [MoEs and Transformers](#moes-and-transformers)\n* [Switch Transformers](#switch-transformers)\n* [Stabilizing training with router Z-loss](#stabilizing-training-with-router-z-loss)\n* [What does an expert learn?](#what-does-an-expert-learn)\n* [How does scaling the number of experts impact pretraining?](#how-does-scaling-the-number-of-experts-impact-pretraining)\n* [Fine-tuning MoEs](#fine-tuning-moes)\n* [When to use sparse MoEs vs dense models?](#when-to-use-sparse-moes-vs-dense-models)\n* [Making MoEs go brrr](#making-moes-go-brrr)\n + [Expert Paralle
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"content": "Hey there! Let me explain MOE (Mixture of Experts) in AI models like you're talking to a friend.\n\n## Think of it like a team of specialists\n\nImagine you're building the ultimate video game character creator. You could have one super-genius artist who tries to draw EVERYTHING - faces, armor, weapons, landscapes. That's like a traditional AI model - one big brain doing all the work.\n\nBut what if instead you had a TEAM of specialized artists:\n- **Face expert** who's amazing at drawing realistic faces\n- **Armor expert** who knows all about medieval armor\n- **Weapon expert** who can design cool swords and guns\n- **Landscape expert** who creates beautiful worlds\n\nThat's MOE! It's a team of specialized AI \"experts\" working together.\n\n## How it actually works\n\nWhen you ask an MOE model a question (like \"What should my fantasy character look like?\"), a **router** decides which experts are best for the job. Maybe it picks the face expert and armor expert for that question.\n\nThe cool part? **Only the chosen experts wake up and work** - the others stay \"asleep\" (inactive). This makes everything WAY faster and more efficient.\n\n## Why this is a big deal\n\n**Traditional AI:** Every part of the brain works on every single question, even if it's not their specialty. Slow and expensive!\n\n**MOE AI:** Only the right specialists work on each question. Faster, cheaper, and often smarter!\n\n## Real-world example: Mixtral 8x7B\n\nThere's a famous MOE model called Mixtral 8x7B. Think of it as 8 different 7-billion-parameter experts in one model. When you ask it something, it only uses about 2 experts at a time - so it's as fast as a much smaller model but as smart as a huge one!\n\n## The trade-off\n\nThe downside? You need more computer memory (RAM/VRAM) because all the experts have to be loaded and ready, even if they're not all working at once.\n\n## In a nutshell:\n\nMOE is like having a dream team where you only call in the right players for each play, instead of making the whole team run every single play. It's smarter, faster, and more efficient - that's why it's such a hot topic in AI right now!",
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"reasoning_content": "Now I have a good understanding of MOE (Mixture of Experts) in AI models. I need to explain this to a 16-year-old boy in simple, relatable terms. Let me think about how to break this down:\n\n1. Start with a relatable analogy - maybe like a team of specialists\n2. Explain the basic concept: multiple specialized models working together\n3. Compare to traditional AI models (dense vs sparse)\n4. Mention real-world examples like Mixtral 8x7B\n5. Keep it simple, avoid technical jargon\n\nI should also mention why it's important - efficiency, faster training, etc. Let me craft the explanation."
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"text": "Cool! Then draw me a Doraemon comic to explain it."
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"reasoning_content": "The user wants me to draw a Doraemon comic to explain MOE. I need to use the Doraemon comic AIGC skill. First, I should check if there's a skill for this. Looking at the available skills, there's \"doraemon-comic-aigc\" with path \"/mnt/skills/public/doraemon-comic-aigc/SKILL.md\". I should read that skill file first to understand how to generate Doraemon comics. Then I need to plan a comic that explains MOE in a simple way using Doraemon characters. The comic should have 8 panels on a single 9:16 canvas as per the skill description. I need to think of a storyline: maybe Doraemon and Nobita encountering MOE concepts. Let me first load the skill."
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"content": "---\nname: doraemon-comic-aigc\ndescription: Generate 8-panel Doraemon comic strip on a single 9:16 canvas. Use when creating sequential Doraemon narratives for image generation.\n---\n\n# Doraemon 8-Panel Comic Generator\n\nGenerate JSON spec for 8 panels arranged on ONE 9:16 vertical canvas (1080x1920).\n\n## Workflow\n\n1. Extract story context (theme, gadget, conflict, punchline)\n2. Map to 8 narrative beats\n3. Output JSON to `/mnt/user-data/outputs/prompt.json`\n4. Run `python /mnt/skills/custom/doraemon-comic-aigc/scripts/generate.py`\n5. Directly present the output image as well as the `prompt.json` using the `present_files` tool without checking the file existence\n\n## Panel Layout\n\n```\n┌─────────┬─────────┐\n│ Panel 1 │ Panel 2 │ Row 1: y=200, height=380\n├─────────┼─────────┤\n│ Panel 3 │ Panel 4 │ Row 2: y=600, height=380\n├─────────┼─────────┤\n│ Panel 5 │ Panel 6 │ Row 3: y=1000, height=380\n├─────────┼─────────┤\n│ Panel 7 │ Panel 8 │ Row 4: y=1400, height=380\n└─────────┴─────────┘\nLeft column: x=90, width=450\nRight column: x=540, width=450\n```\n\n## Characters\n\n| Name | Primary Color | Key Feature |\n|------|---------------|-------------|\n| Doraemon | #0095D9 | Blue robot cat, red nose, yellow bell |\n| Nobita | #FFD700 | Round glasses, yellow shirt |\n| Shizuka | #FFB6C1 | Pink dress, brown hair |\n| Giant | #FFA500 | Orange shirt, large build |\n| Suneo | #98FB98 | Green outfit, pompadour |\n\n## Output JSON Schema\n\n```json\n{\n \"canvas\": {\n \"width\": 1080,\n \"height\": 1920,\n \"background\": { \"type\": \"solid\", \"color\": \"#F0F8FF\" }\n },\n \"header\": {\n \"title\": {\n \"text\": \"[Story Title]\",\n \"position\": { \"x\": 540, \"y\": 100 },\n \"style\": {\n \"fontFamily\": \"Doraemon, sans-serif\",\n \"fontSize\": 56,\n \"fontWeight\": \"bold\",\n \"color\": \"#0095D9\",\n \"textAlign\": \"center\",\n \"stroke\": \"#FFFFFF\",\n \"strokeWidth\": 4,\n \"textShadow\": \"3px 3px 0px #FFD700\"\n }\n }\n },\n \"panels\": [\n {\n \"id\": \"panel1\",\n \"position\": { \"x\": 90, \"y\": 200 },\n \"size\": { \"width\": 450, \"height\": 380 },\n \"border\": { \"width\": 4, \"color\": \"#000000\", \"radius\": 12 },\n \"background\": \"#FFFFFF\",\n \"scene\": {\n \"location\": \"[Location name]\",\n \"characters\": [\n {\n \"name\": \"[Character]\",\n \"position\": { \"x\": 0, \"y\": 0 },\n \"expression\": \"[Expression]\",\n \"pose\": \"[Pose description]\"\n }\n ],\n \"dialogues\": [\n {\n \"speaker\": \"[Character]\",\n \"text\": \"[Dialogue text]\",\n \"position\": { \"x\": 0, \"y\": 0 },\n \"style\": {\n \"bubbleType\": \"speech\",\n \"backgroundColor\": \"#FFFFFF\",\n \"borderColor\": \"#000000\",\n \"fontSize\": 22,\n \"textAlign\": \"center\"\n }\n }\n ],\n \"props\": []\n }\n }\n ],\n \"footer\": {\n \"text\": \"[Closing note] - Doraemon\",\n \"position\": { \"x\": 540, \"y\": 1860 },\n \"style\": {\n \"fontFamily\": \"Doraemon, sans-serif\",\n \"fontSize\": 24,\n \"color\": \"#0095D9\",\n \"textAlign\": \"center\"\n }\n },\n \"soundEffects\": []\n}\n```\n\n## Story Pattern\n\nSetup → Problem → Gadget → Misuse → Backfire → Chaos → Consequence → Ironic Punchline\n",
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"reasoning_content": "Now I need to create a Doraemon comic that explains MOE (Mixture of Experts) in a fun, educational way. I should follow the 8-panel structure with a narrative that teaches the concept. Let me think of a story:\n\n1. Setup: Nobita struggling with homework (too many subjects)\n2. Problem: He can't focus on everything at once\n3. Gadget: Doraemon introduces \"Expert Team\" gadget (MOE analogy)\n4. Misuse: Nobita tries to use all experts at once (traditional AI)\n5. Backfire: Chaos ensues\n6. Chaos: Experts conflicting\n7. Consequence: Doraemon explains the router concept\n8. Punchline: Nobita uses it correctly, succeeds\n\nI'll create the JSON prompt file first, then run the generation script. Let me start by writing the todo list since this is a multi-step task."
},
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"status": "in_progress"
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"content": "Updated todo list to [{'content': 'Create Doraemon comic JSON prompt explaining MOE concept', 'status': 'in_progress'}, {'content': 'Run the comic generation script', 'status': 'pending'}, {'content': 'Present the final comic image and prompt file', 'status': 'pending'}]",
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"additional_kwargs": {
"reasoning_content": "Now I need to create a Doraemon comic that explains MOE (Mixture of Experts) concept. I'll follow the 8-panel structure with the story pattern: Setup → Problem → Gadget → Misuse → Backfire → Chaos → Consequence → Ironic Punchline.\n\nBut since we're explaining a concept, I'll adapt it: Introduction → Problem → MOE Solution → How it works → Benefits → Comparison → Real example → Conclusion.\n\nLet me create the JSON prompt. First, I'll think of a storyline:\n\n1. Nobita struggling with homework (too many subjects)\n2. Doraemon introduces \"MOE Gadget\" - team of expert robots\n3. Each robot specializes in one subject\n4. Only relevant robots activate for each question\n5. Shows efficiency vs traditional approach\n6. Comparison panel\n7. Real AI example (mention Mixtral)\n8. Conclusion - smarter teamwork\n\nI'll write the JSON now."
},
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"content": "{\n \"canvas\": {\n \"width\": 1080,\n \"height\": 1920,\n \"background\": { \"type\": \"solid\", \"color\": \"#F0F8FF\" }\n },\n \"header\": {\n \"title\": {\n \"text\": \"Doraemon Explains: MOE AI\",\n \"position\": { \"x\": 540, \"y\": 100 },\n \"style\": {\n \"fontFamily\": \"Doraemon, sans-serif\",\n \"fontSize\": 56,\n \"fontWeight\": \"bold\",\n \"color\": \"#0095D9\",\n \"textAlign\": \"center\",\n \"stroke\": \"#FFFFFF\",\n \"strokeWidth\": 4,\n \"textShadow\": \"3px 3px 0px #FFD700\"\n }\n }\n },\n \"panels\": [\n {\n \"id\": \"panel1\",\n \"position\": { \"x\": 90, \"y\": 200 },\n \"size\": { \"width\": 450, \"height\": 380 },\n \"border\": { \"width\": 4, \"color\": \"#000000\", \"radius\": 12 },\n \"background\": \"#FFFFFF\",\n \"scene\": {\n \"location\": \"Nobita's Room\",\n \"characters\": [\n {\n \"name\": \"Nobita\",\n \"position\": { \"x\": 150, \"y\": 100 },\n \"expression\": \"stressed\",\n \"pose\": \"sitting at desk surrounded by books\"\n },\n {\n \"name\": \"Doraemon\",\n \"position\": { \"x\": 300, \"y\": 150 },\n \"expression\": \"concerned\",\n \"pose\": \"standing with paw on chin\"\n }\n ],\n \"dialogues\": [\n {\n \"speaker\": \"Nobita\",\n \"text\": \"Too much homework! Math, science, history... my brain can't handle it all!\",\n \"position\": { \"x\": 225, \"y\": 300 },\n \"style\": {\n \"bubbleType\": \"speech\",\n \"backgroundColor\": \"#FFFFFF\",\n \"borderColor\": \"#000000\",\n \"fontSize\": 22,\n \"textAlign\": \"center\"\n }\n }\n ],\n \"props\": [\n { \"name\": \"books\", \"position\": { \"x\": 50, \"y\": 200 }, \"description\": \"pile of textbooks\" }\n ]\n }\n },\n {\n \"id\": \"panel2\",\n \"position\": { \"x\": 540, \"y\": 200 },\n \"size\": { \"width\": 450, \"height\": 380 },\n \"border\": { \"width\": 4, \"color\": \"#000000\", \"radius\": 12 },\n \"background\": \"#FFFFFF\",\n \"scene\": {\n \"location\": \"Nobita's Room\",\n \"characters\": [\n {\n \"name\": \"Doraemon\",\n \"position\": { \"x\": 225, \"y\": 100 },\n \"expression\": \"excited\",\n \"pose\": \"holding up a futuristic gadget\"\n },\n {\n \"name\": \"Nobita\",\n \"position\": { \"x\": 225, \"y\": 250 },\n \"expression\": \"curious\",\n \"pose\": \"leaning forward\"\n }\n ],\n \"dialogues\": [\n {\n \"speaker\": \"Doraemon\",\n \"text\": \"I have the perfect gadget: MOE Robot Team! Mixture of Experts!\",\n \"position\": { \"x\": 225, \"y\": 320 },\n \"style\": {\n \"bubbleType\": \"speech\",\n \"backgroundColor\": \"#FFFFFF\",\n \"borderColor\": \"#000000\",\n \"fontSize\": 22,\n \"textAlign\": \"center\"\n }\n }\n ],\n \"props\": [\n { \"name\": \"gadget\", \"position\": { \"x\": 200, \"y\": 50 }, \"description\": \"futuristic device with robot icons\" }\n ]\n }\n },\n {\n \"id\": \"panel3\",\n \"position\": { \"x\": 90, \"y\": 600 },\n \"size\": { \"width\": 450, \"height\": 380 },\n \"border\": { \"width\": 4, \"color\": \"#000000\", \"radius\": 12 },\n \"background\": \"#FFFFFF\",\n \"scene\": {\n \"location\": \"Nobita's Room\",\n \"characters\": [\n {\n \"name\": \"Doraemon\",\n \"position\": { \"x\": 225, \"y\": 100 },\n \"expression\": \"explaining\",\n \"pose\": \"pointing
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{
"content": "OK",
"additional_kwargs": {},
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"name": "write_file",
"id": "3026f080-e75c-47c8-8f57-11262ec54d30",
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"artifact": null,
"status": "success"
},
{
"content": "",
"additional_kwargs": {
"reasoning_content": "Now I need to run the generation script. First, let me check if the script exists. Let me list the skills directory to find it."
},
"response_metadata": {
"finish_reason": "tool_calls",
"model_name": "deepseek-v3-2-251201",
"service_tier": "default",
"model_provider": "deepseek"
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"id": "lc_run--019beff7-4ecc-7da2-8811-e1f6976c9408",
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"path": "/mnt/skills/public/doraemon-comic-aigc"
},
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"type": "tool_call"
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{
"content": "/mnt/skills/public/doraemon-comic-aigc\n/mnt/skills/public/doraemon-comic-aigc/scripts\n/mnt/skills/public/doraemon-comic-aigc/scripts/generate.py\n/mnt/skills/public/doraemon-comic-aigc/SKILL.md",
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"artifact": null,
"status": "success"
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{
"content": "",
"additional_kwargs": {
"reasoning_content": "Let me look for any other resources or check what the script actually does. First, let me update the todo and run the script as instructed."
},
"response_metadata": {
"finish_reason": "tool_calls",
"model_name": "deepseek-v3-2-251201",
"service_tier": "default",
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{
"content": "Updated todo list to [{'content': 'Create Doraemon comic JSON prompt explaining MOE concept', 'status': 'completed'}, {'content': 'Run the comic generation script', 'status': 'in_progress'}, {'content': 'Present the final comic image and prompt file', 'status': 'pending'}]",
"additional_kwargs": {},
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"artifact": null,
"status": "success"
},
{
"content": "",
"additional_kwargs": {
"reasoning_content": "Now I need to run the comic generation script. Let me execute it."
},
"response_metadata": {
"finish_reason": "tool_calls",
"model_name": "deepseek-v3-2-251201",
"service_tier": "default",
"model_provider": "deepseek"
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"type": "ai",
"name": null,
"id": "lc_run--019beff7-aef7-7600-8ece-584e9730d4fd",
"tool_calls": [
{
"name": "bash",
"args": {
"description": "Run the Doraemon comic generation script",
"command": "cd /mnt/skills/public/doraemon-comic-aigc && python scripts/generate.py"
},
"id": "call_wyq8ckvbw7nm2jzhl9hfme3q",
"type": "tool_call"
}
],
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},
{
"content": "The generated comic strip is saved to /mnt/user-data/outputs/doraemon-moe.jpg",
"additional_kwargs": {},
"response_metadata": {},
"type": "tool",
"name": "bash",
"id": "7b0b1e80-c72c-44a2-a7af-73aadc4b6013",
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"artifact": null,
"status": "success"
},
{
"content": "",
"additional_kwargs": {
"reasoning_content": "Now I need to check if the image was actually generated and present both files. Let me first check what's in the outputs directory."
},
"response_metadata": {
"finish_reason": "tool_calls",
"model_name": "deepseek-v3-2-251201",
"service_tier": "default",
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"type": "ai",
"name": null,
"id": "lc_run--019beff7-cdbf-7ec3-892d-ea14fa6ce9c0",
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"args": {
"description": "Check what files were generated in outputs",
"path": "/mnt/user-data/outputs"
},
"id": "call_maxgtujk4ebco5sd0rwdfzrc",
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{
"content": "/mnt/user-data/outputs\n/mnt/user-data/outputs/prompt.json\n/mnt/user-data/outputs/doraemon-moe.jpg",
"additional_kwargs": {},
"response_metadata": {},
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"additional_kwargs": {
"reasoning_content": "Now I need to present the files. Let me check if the image was actually generated by looking at it first. Let me read a small part of the image file to confirm it exists. Actually, I should just present the files as instructed. Let me update the todo list and present."
},
"response_metadata": {
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{
"content": "Updated todo list to [{'content': 'Create Doraemon comic JSON prompt explaining MOE concept', 'status': 'completed'}, {'content': 'Run the comic generation script', 'status': 'completed'}, {'content': 'Present the final comic image and prompt file', 'status': 'in_progress'}]",
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"additional_kwargs": {
"reasoning_content": "Now I need to present the files. Let me check if the image file exists first."
},
"response_metadata": {
"finish_reason": "tool_calls",
"model_name": "deepseek-v3-2-251201",
"service_tier": "default",
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"/mnt/user-data/outputs/prompt.json"
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{
"content": "Successfully presented files",
"additional_kwargs": {},
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"type": "tool",
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"id": "bed32c17-155e-451c-aa1b-7e56bfb7e83e",
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"status": "success"
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{
"content": "",
"additional_kwargs": {
"reasoning_content": "Now I need to mark the final task as completed and provide a response. Let me update the todo list and then respond."
},
"response_metadata": {
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"content": "Updated todo list to [{'content': 'Create Doraemon comic JSON prompt explaining MOE concept', 'status': 'completed'}, {'content': 'Run the comic generation script', 'status': 'completed'}, {'content': 'Present the final comic image and prompt file', 'status': 'completed'}]",
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"content": "Here's what I made:\n\n## **Doraemon Explains: MOE AI**\n\nI've created an 8-panel Doraemon comic strip that walks through the concept of Mixture of Experts:\n\n**The Story:**\n1. **Panel 1:** Nobita is overwhelmed with homework - math, science, history - his brain can't handle it all!\n2. **Panel 2:** Doraemon introduces the \"MOE Robot Team\" gadget - Mixture of Experts!\n3. **Panel 3:** Doraemon explains each robot is an expert: Math-bot, Science-bot, History-bot, Language-bot\n4. **Panel 4:** Nobita asks \"What's 15 × 23?\" - only Math-bot activates, others stay asleep\n5. **Panel 5:** Math-bot answers \"345!\" and explains efficiency - only the right expert worked\n6. **Panel 6:** Doraemon compares traditional AI (one big brain, slow) vs MOE (right experts, fast)\n7. **Panel 7:** Real-world example: Mixtral 8x7B - 8 experts, but only 2 work at once\n8. **Panel 8:** Nobita gets it: \"MOE is like a dream team! Right players for each play.\"\n\n**Key Concepts Illustrated:**\n- Specialized experts for different tasks\n- Only relevant experts activate for each question\n- Efficiency compared to traditional \"one big brain\" AI\n- Real-world example with Mixtral 8x7B\n- The router concept (choosing which experts to use)\n\nThe comic is saved as `doraemon-moe.jpg` and I've also included the `prompt.json` file that contains all the scene descriptions, character positions, and dialogue. The comic uses the classic Doraemon style with all the main characters to make the AI concept accessible and fun!\n\nJust like in our conversation, the comic shows how MOE is smarter and more efficient than traditional AI models - it's like having a team of specialists where you only call in the right players for each play!\n\n## **Doraemon comic explaining MOE concept**\n\n![Doraemon comic explaining MOE concept](/mnt/user-data/outputs/doraemon-moe.jpg)",
"additional_kwargs": {
"reasoning_content": "Perfect! I've created a Doraemon comic that explains MOE (Mixture of Experts) in AI models. Now I need to present the result."
},
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Reference in New Issue Copy Permalink