added jan nano to docs, api key example to api server section, and changelog section with 0.6.1
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@ -49,3 +49,4 @@ src-tauri/resources/bin
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# Helper tools
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.opencode
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OpenCode.md
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archive/
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BIN
docs/public/assets/images/changelog/jan-v0.6.1-ui-revamp.png
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@ -57,7 +57,7 @@ const Changelog = () => {
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<p className="text-base mt-2 leading-relaxed">
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Latest release updates from the Jan team. Check out our
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<a
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href="https://github.com/orgs/menloresearch/projects/5/views/52"
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href="https://github.com/orgs/menloresearch/projects/30"
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className="text-blue-600 dark:text-blue-400 cursor-pointer"
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>
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Roadmap
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docs/src/pages/changelog/2025-06-19-jan-ui-revamp.mdx
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docs/src/pages/changelog/2025-06-19-jan-ui-revamp.mdx
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---
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title: "Jan v0.6.1 is here: It's a whole new vibe!"
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version: 0.6.1
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description: "Are you ready for the sexiest UI ever?"
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date: 2025-06-19
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ogImage: "/assets/images/changelog/jan-v0.6.1-ui-revamp.png"
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---
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import ChangelogHeader from "@/components/Changelog/ChangelogHeader"
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<ChangelogHeader title="Jan v0.6.1 is here: It's a whole new vibe!" date="2025-06-19" ogImage="/assets/images/changelog/jan-v0.6.1-ui-revamp.png" />
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## Highlights 🎉
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- Jan's been redesigned to be faster, cleaner, and easier to use.
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- You can now create assistants with custom instructions and settings from a dedicated tab.
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- You can now use Jan with Menlo's models.
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Update your Jan or [download the latest](https://jan.ai/).
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For more details, see the [GitHub release notes](https://github.com/menloresearch/jan/releases/tag/v0.6.1).
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BIN
docs/src/pages/docs/_assets/api-server2.png
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docs/src/pages/docs/_assets/serper-mcp.png
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@ -7,6 +7,7 @@
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"desktop": "Install 👋 Jan",
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"threads": "Start Chatting",
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"manage-models": "Manage Models",
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"menlo-models": "Menlo Models",
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"assistants": "Create Assistants",
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"tutorials-separators": {
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@ -42,7 +42,9 @@ as well after downloading it from [here](https://github.com/ggml-org/llama.cpp).
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2. Add an API Key (it can be anything) or fully configure the server at [Server Settings](/docs/api-server#server-settings)
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3. Click **Start Server** button
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4. Wait for the confirmation message in the logs panel, your server is ready when you see: `JAN API listening at: http://127.0.0.1:1337`
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5. Make sure you add an API key, this can be anything you want, a word like "testing" or even a combination of numbers and letters.
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### Step 2: Test Server
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The easiest way to test your server is through the API Playground:
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@ -50,8 +52,25 @@ The easiest way to test your server is through the API Playground:
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2. Select a model from the dropdown menu in Jan interface
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3. Try a simple request
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4. View the response in real-time
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5. When you send requests from another app, you need to add the API key in the request headers.
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### Step 3: Use the API
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```sh
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curl http://127.0.0.1:1337/v1/chat/completions \
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-H "Content-Type: application/json" \
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-H "Authorization: Bearer testing-something" \ # here you need to add your API key
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-d '{
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"model": "jan-nano-gguf",
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"messages": [
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{
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"role": "user",
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"content": "Write a one-sentence bedtime story about a unicorn."
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}
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]
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}'
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```
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</Steps>
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@ -108,6 +127,8 @@ Enable **Verbose Server Logs** for detailed error messages.
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- Verify your JSON request format is correct
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- Verify firewall settings
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- Look for detailed error messages in the logs
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- Make sure you add an API key, this can be anything you want, a word like "testing" or even a combination of numbers and letters.
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- Use the API Key in the request headers when sending requests from another app.
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**2. CORS Errors in Web Apps**
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- Enable CORS in server settings if using from a webpage
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docs/src/pages/docs/menlo-models/_meta.json
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docs/src/pages/docs/menlo-models/_meta.json
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{
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"overview": {
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"title": "Overview",
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"href": "/docs/menlo-models/overview"
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},
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"jan-nano": {
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"title": "Jan Nano",
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"href": "/docs/menlo-models/jan-nano"
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}
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}
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docs/src/pages/docs/menlo-models/jan-nano.mdx
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docs/src/pages/docs/menlo-models/jan-nano.mdx
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---
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title: Jan Nano
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description: Jan-Nano-Gguf Model
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keywords:
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[
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Jan,
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Jan Models,
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Jan Model,
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Jan Model List,
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Menlo Models,
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Menlo Model,
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Jan-Nano-Gguf,
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ReZero,
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Model Context Protocol,
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MCP,
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]
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---
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import { Callout } from 'nextra/components'
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# Jan Nano
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Jan-Nano is a compact 4-billion parameter language model specifically designed and trained for deep
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research tasks. This model has been optimized to work seamlessly with Model Context Protocol (MCP) servers,
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enabling efficient integration with various research tools and data sources.
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The model and its different model variants are fully supported by Jan.
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<Callout type="info">
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Jan-Nano can be use by Jan's stable version but its true capabilities shine in Jan's beta version, which
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offers MCP support. You can download Jan's beta version from [here](https://jan.ai/docs/desktop/beta).
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</Callout>
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## System Requirements
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- Minimum Requirements:
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- 8GB RAM (with iQ4_XS quantization)
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- 12GB VRAM (for Q8 quantization)
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- CUDA-compatible GPU
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- Recommended Setup:
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- 16GB+ RAM
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- 16GB+ VRAM
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- Latest CUDA drivers
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- RTX 30/40 series or newer
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## Using Jan-Nano
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### Step 1
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Download Jan Beta from [here](https://jan.ai/docs/desktop/beta).
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### Step 2
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Go to the Hub Tab, search for Jan-Nano-Gguf, and click on the download button to the best model size for your system.
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### Step 3
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Go to **Settings** > **Model Providers** > **Llama.cpp** click on the pencil icon and enable tool use for Jan-Nano-Gguf.
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### Step 4
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To take advantage of Jan-Nano's full capabilities, you need to enable MCP support. We're going to use it with Serper's
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API. You can get a free API key from [here](https://serper.dev/). Sign up and they will immediately generate one for you.
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### Step 5
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Add the serper MCP to Jan via the **Settings** > **MCP Servers** tab.
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### Step 6
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Open up a new chat and ask Jan-Nano to search the web for you.
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## Queries to Try
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Here are some example queries to showcase Jan-Nano's web search capabilities:
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1. **Current Events**: What are the latest developments in renewable energy adoption in Germany and Denmark?
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2. **International Business**: What is the current status of Tesla's Gigafactory in Berlin and how has it impacted the local economy?
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3. **Technology Trends**: What are the newest AI developments from Google, Microsoft, and Meta that were announced this week?
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4. **Global Weather**: What's the current weather forecast for Tokyo, Japan for the next 5 days?
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5. **Stock Market**: What are the current stock prices for Apple, Samsung, and Huawei, and how have they performed this month?
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6. **Sports Updates**: What are the latest results from the Premier League matches played this weekend?
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7. **Scientific Research**: What are the most recent findings about climate change impacts in the Arctic region?
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8. **Cultural Events**: What major music festivals are happening in Europe this summer and who are the headliners?
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9. **Health & Medicine**: What are the latest developments in mRNA vaccine technology and its applications beyond COVID-19?
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10. **Space Exploration**: What are the current missions being conducted by NASA, ESA, and China's space program?
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40
docs/src/pages/docs/menlo-models/overview.mdx
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---
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title: Overview
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description: Jan Models
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keywords:
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[
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Jan,
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Jan Models,
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Jan Model,
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Jan Model List,
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Menlo Models,
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Menlo Model,
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Jan-Nano-Gguf,
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ReZero,
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Model Context Protocol,
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MCP,
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]
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---
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# Menlo Models
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At Menlo, we have focused on creating a series of models that are optimized for all sorts of tasks, including
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web search, deep research, robotic control, and using MCPs. Our latest model, Jan-Nano-Gguf, is available in Jan
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right now providing excellent results on taks that use MCPs.
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You can have a look at all of our models, and download them from the HuggingFace [Menlo Models page](https://huggingface.co/Menlo).
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## Jan-Nano-Gguf (Available in Jan right now 🚀)
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Jan-Nano-Gguf is a 4-billion parameter model that is optimized for deep research tasks. It has been trained on a
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variety of datasets and is designed to be used with the Model Context Protocol (MCP) servers.
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## ReZero
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ReZero (Retry-Zero) is a reinforcement learning framework that improves RAG systems by rewarding LLMs for retrying
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failed queries. Traditional RAG approaches struggle when initial searches fail, but ReZero encourages persistence and
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alternative strategies. This increases accuracy from 25% to 46.88% in complex information-seeking tasks.
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