rearranged docs, added Jan Nano and minor improvements
This commit is contained in:
Ramon Perez
parent
c74e5280e7
commit
ade7c6cb6c
643
docs/.astro/collections/docs.schema.json
Normal file
643
docs/.astro/collections/docs.schema.json
Normal file
@ -0,0 +1,643 @@
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|
||||
"default": {}
|
||||
},
|
||||
"banner": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"content": {
|
||||
"type": "string"
|
||||
}
|
||||
},
|
||||
"required": [
|
||||
"content"
|
||||
],
|
||||
"additionalProperties": false
|
||||
},
|
||||
"pagefind": {
|
||||
"type": "boolean",
|
||||
"default": true
|
||||
},
|
||||
"draft": {
|
||||
"type": "boolean",
|
||||
"default": false
|
||||
},
|
||||
"$schema": {
|
||||
"type": "string"
|
||||
}
|
||||
},
|
||||
"required": [
|
||||
"title"
|
||||
],
|
||||
"additionalProperties": false
|
||||
}
|
||||
},
|
||||
"$schema": "http://json-schema.org/draft-07/schema#"
|
||||
}
|
||||
1
docs/.astro/content-assets.mjs
Normal file
1
docs/.astro/content-assets.mjs
Normal file
@ -0,0 +1 @@
|
||||
export default new Map();
|
||||
1
docs/.astro/content-modules.mjs
Normal file
1
docs/.astro/content-modules.mjs
Normal file
@ -0,0 +1 @@
|
||||
export default new Map();
|
||||
164
docs/.astro/content.d.ts
vendored
Normal file
164
docs/.astro/content.d.ts
vendored
Normal file
@ -0,0 +1,164 @@
|
||||
declare module 'astro:content' {
|
||||
export interface RenderResult {
|
||||
Content: import('astro/runtime/server/index.js').AstroComponentFactory;
|
||||
headings: import('astro').MarkdownHeading[];
|
||||
remarkPluginFrontmatter: Record<string, any>;
|
||||
}
|
||||
interface Render {
|
||||
'.md': Promise<RenderResult>;
|
||||
}
|
||||
|
||||
export interface RenderedContent {
|
||||
html: string;
|
||||
metadata?: {
|
||||
imagePaths: Array<string>;
|
||||
[key: string]: unknown;
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
declare module 'astro:content' {
|
||||
type Flatten<T> = T extends { [K: string]: infer U } ? U : never;
|
||||
|
||||
export type CollectionKey = keyof AnyEntryMap;
|
||||
export type CollectionEntry<C extends CollectionKey> = Flatten<AnyEntryMap[C]>;
|
||||
|
||||
export type ContentCollectionKey = keyof ContentEntryMap;
|
||||
export type DataCollectionKey = keyof DataEntryMap;
|
||||
|
||||
type AllValuesOf<T> = T extends any ? T[keyof T] : never;
|
||||
type ValidContentEntrySlug<C extends keyof ContentEntryMap> = AllValuesOf<
|
||||
ContentEntryMap[C]
|
||||
>['slug'];
|
||||
|
||||
export type ReferenceDataEntry<
|
||||
C extends CollectionKey,
|
||||
E extends keyof DataEntryMap[C] = string,
|
||||
> = {
|
||||
collection: C;
|
||||
id: E;
|
||||
};
|
||||
export type ReferenceContentEntry<
|
||||
C extends keyof ContentEntryMap,
|
||||
E extends ValidContentEntrySlug<C> | (string & {}) = string,
|
||||
> = {
|
||||
collection: C;
|
||||
slug: E;
|
||||
};
|
||||
|
||||
/** @deprecated Use `getEntry` instead. */
|
||||
export function getEntryBySlug<
|
||||
C extends keyof ContentEntryMap,
|
||||
E extends ValidContentEntrySlug<C> | (string & {}),
|
||||
>(
|
||||
collection: C,
|
||||
// Note that this has to accept a regular string too, for SSR
|
||||
entrySlug: E,
|
||||
): E extends ValidContentEntrySlug<C>
|
||||
? Promise<CollectionEntry<C>>
|
||||
: Promise<CollectionEntry<C> | undefined>;
|
||||
|
||||
/** @deprecated Use `getEntry` instead. */
|
||||
export function getDataEntryById<C extends keyof DataEntryMap, E extends keyof DataEntryMap[C]>(
|
||||
collection: C,
|
||||
entryId: E,
|
||||
): Promise<CollectionEntry<C>>;
|
||||
|
||||
export function getCollection<C extends keyof AnyEntryMap, E extends CollectionEntry<C>>(
|
||||
collection: C,
|
||||
filter?: (entry: CollectionEntry<C>) => entry is E,
|
||||
): Promise<E[]>;
|
||||
export function getCollection<C extends keyof AnyEntryMap>(
|
||||
collection: C,
|
||||
filter?: (entry: CollectionEntry<C>) => unknown,
|
||||
): Promise<CollectionEntry<C>[]>;
|
||||
|
||||
export function getEntry<
|
||||
C extends keyof ContentEntryMap,
|
||||
E extends ValidContentEntrySlug<C> | (string & {}),
|
||||
>(
|
||||
entry: ReferenceContentEntry<C, E>,
|
||||
): E extends ValidContentEntrySlug<C>
|
||||
? Promise<CollectionEntry<C>>
|
||||
: Promise<CollectionEntry<C> | undefined>;
|
||||
export function getEntry<
|
||||
C extends keyof DataEntryMap,
|
||||
E extends keyof DataEntryMap[C] | (string & {}),
|
||||
>(
|
||||
entry: ReferenceDataEntry<C, E>,
|
||||
): E extends keyof DataEntryMap[C]
|
||||
? Promise<DataEntryMap[C][E]>
|
||||
: Promise<CollectionEntry<C> | undefined>;
|
||||
export function getEntry<
|
||||
C extends keyof ContentEntryMap,
|
||||
E extends ValidContentEntrySlug<C> | (string & {}),
|
||||
>(
|
||||
collection: C,
|
||||
slug: E,
|
||||
): E extends ValidContentEntrySlug<C>
|
||||
? Promise<CollectionEntry<C>>
|
||||
: Promise<CollectionEntry<C> | undefined>;
|
||||
export function getEntry<
|
||||
C extends keyof DataEntryMap,
|
||||
E extends keyof DataEntryMap[C] | (string & {}),
|
||||
>(
|
||||
collection: C,
|
||||
id: E,
|
||||
): E extends keyof DataEntryMap[C]
|
||||
? string extends keyof DataEntryMap[C]
|
||||
? Promise<DataEntryMap[C][E]> | undefined
|
||||
: Promise<DataEntryMap[C][E]>
|
||||
: Promise<CollectionEntry<C> | undefined>;
|
||||
|
||||
/** Resolve an array of entry references from the same collection */
|
||||
export function getEntries<C extends keyof ContentEntryMap>(
|
||||
entries: ReferenceContentEntry<C, ValidContentEntrySlug<C>>[],
|
||||
): Promise<CollectionEntry<C>[]>;
|
||||
export function getEntries<C extends keyof DataEntryMap>(
|
||||
entries: ReferenceDataEntry<C, keyof DataEntryMap[C]>[],
|
||||
): Promise<CollectionEntry<C>[]>;
|
||||
|
||||
export function render<C extends keyof AnyEntryMap>(
|
||||
entry: AnyEntryMap[C][string],
|
||||
): Promise<RenderResult>;
|
||||
|
||||
export function reference<C extends keyof AnyEntryMap>(
|
||||
collection: C,
|
||||
): import('astro/zod').ZodEffects<
|
||||
import('astro/zod').ZodString,
|
||||
C extends keyof ContentEntryMap
|
||||
? ReferenceContentEntry<C, ValidContentEntrySlug<C>>
|
||||
: ReferenceDataEntry<C, keyof DataEntryMap[C]>
|
||||
>;
|
||||
// Allow generic `string` to avoid excessive type errors in the config
|
||||
// if `dev` is not running to update as you edit.
|
||||
// Invalid collection names will be caught at build time.
|
||||
export function reference<C extends string>(
|
||||
collection: C,
|
||||
): import('astro/zod').ZodEffects<import('astro/zod').ZodString, never>;
|
||||
|
||||
type ReturnTypeOrOriginal<T> = T extends (...args: any[]) => infer R ? R : T;
|
||||
type InferEntrySchema<C extends keyof AnyEntryMap> = import('astro/zod').infer<
|
||||
ReturnTypeOrOriginal<Required<ContentConfig['collections'][C]>['schema']>
|
||||
>;
|
||||
|
||||
type ContentEntryMap = {
|
||||
|
||||
};
|
||||
|
||||
type DataEntryMap = {
|
||||
"docs": Record<string, {
|
||||
id: string;
|
||||
body?: string;
|
||||
collection: "docs";
|
||||
data: any;
|
||||
rendered?: RenderedContent;
|
||||
filePath?: string;
|
||||
}>;
|
||||
|
||||
};
|
||||
|
||||
type AnyEntryMap = ContentEntryMap & DataEntryMap;
|
||||
|
||||
export type ContentConfig = typeof import("../src/content.config.mjs");
|
||||
}
|
||||
1
docs/.astro/data-store.json
Normal file
1
docs/.astro/data-store.json
Normal file
@ -0,0 +1 @@
|
||||
[["Map",1,2],"meta::meta",["Map",3,4,5,6],"astro-version","5.9.3","astro-config-digest","{\"root\":{},\"srcDir\":{},\"publicDir\":{},\"outDir\":{},\"cacheDir\":{},\"compressHTML\":true,\"base\":\"/\",\"trailingSlash\":\"ignore\",\"output\":\"static\",\"scopedStyleStrategy\":\"attribute\",\"build\":{\"format\":\"directory\",\"client\":{},\"server\":{},\"assets\":\"_astro\",\"serverEntry\":\"entry.mjs\",\"redirects\":true,\"inlineStylesheets\":\"auto\",\"concurrency\":1},\"server\":{\"open\":false,\"host\":false,\"port\":4321,\"streaming\":true,\"allowedHosts\":[]},\"redirects\":{},\"image\":{\"endpoint\":{\"route\":\"/_image\"},\"service\":{\"entrypoint\":\"astro/assets/services/sharp\",\"config\":{}},\"domains\":[],\"remotePatterns\":[],\"experimentalDefaultStyles\":true},\"devToolbar\":{\"enabled\":true},\"markdown\":{\"syntaxHighlight\":{\"type\":\"shiki\",\"excludeLangs\":[\"math\"]},\"shikiConfig\":{\"langs\":[],\"langAlias\":{},\"theme\":\"github-dark\",\"themes\":{},\"wrap\":false,\"transformers\":[]},\"remarkPlugins\":[],\"rehypePlugins\":[],\"remarkRehype\":{},\"gfm\":true,\"smartypants\":true},\"security\":{\"checkOrigin\":true},\"env\":{\"schema\":{},\"validateSecrets\":false},\"experimental\":{\"clientPrerender\":false,\"contentIntellisense\":false,\"responsiveImages\":false,\"headingIdCompat\":false,\"preserveScriptOrder\":false,\"csp\":false},\"legacy\":{\"collections\":false}}"]
|
||||
5
docs/.astro/settings.json
Normal file
5
docs/.astro/settings.json
Normal file
@ -0,0 +1,5 @@
|
||||
{
|
||||
"_variables": {
|
||||
"lastUpdateCheck": 1750832446593
|
||||
}
|
||||
}
|
||||
2
docs/.astro/types.d.ts
vendored
Normal file
2
docs/.astro/types.d.ts
vendored
Normal file
@ -0,0 +1,2 @@
|
||||
/// <reference types="astro/client" />
|
||||
/// <reference path="content.d.ts" />
|
||||
@ -11,6 +11,21 @@
|
||||
"type": "page",
|
||||
"title": "Documentation"
|
||||
},
|
||||
"cortex": {
|
||||
"type": "page",
|
||||
"title": "Cortex",
|
||||
"display": "hidden"
|
||||
},
|
||||
"integrations": {
|
||||
"type": "page",
|
||||
"title": "Integrations",
|
||||
"display": "hidden"
|
||||
},
|
||||
"platforms": {
|
||||
"type": "page",
|
||||
"title": "Platforms",
|
||||
"display": "hidden"
|
||||
},
|
||||
"changelog": {
|
||||
"type": "page",
|
||||
"title": "Changelog",
|
||||
|
||||
BIN
docs/src/pages/docs/_assets/jan-nano-bench.png
Normal file
BIN
docs/src/pages/docs/_assets/jan-nano-bench.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 205 KiB |
BIN
docs/src/pages/docs/_assets/jan-nano-demo.mp4
Normal file
BIN
docs/src/pages/docs/_assets/jan-nano-demo.mp4
Normal file
Binary file not shown.
@ -1,4 +1,8 @@
|
||||
{
|
||||
"-- Switcher": {
|
||||
"type": "separator",
|
||||
"title": "Switcher"
|
||||
},
|
||||
"index": "Overview",
|
||||
"how-to-separator": {
|
||||
"title": "HOW TO",
|
||||
@ -6,8 +10,7 @@
|
||||
},
|
||||
"desktop": "Install 👋 Jan",
|
||||
"threads": "Start Chatting",
|
||||
"manage-models": "Manage Models",
|
||||
"menlo-models": "Menlo Models",
|
||||
"jan-models": "Use Jan Models",
|
||||
"assistants": "Create Assistants",
|
||||
|
||||
"tutorials-separators": {
|
||||
@ -16,8 +19,7 @@
|
||||
},
|
||||
"quickstart": "Quickstart",
|
||||
"remote-models": "Connect to Remote Models",
|
||||
"server-examples": "Provide AI to Tools",
|
||||
"mcp": "Model Context Protocol",
|
||||
"server-examples": "Integrations",
|
||||
|
||||
"explanation-separator": {
|
||||
"title": "EXPLANATION",
|
||||
@ -26,18 +28,25 @@
|
||||
"llama-cpp": "Local AI Engine",
|
||||
"api-server": "Server Overview",
|
||||
"data-folder": "Jan Data Folder",
|
||||
"privacy": "Privacy",
|
||||
"privacy-policy": {
|
||||
"type": "page",
|
||||
"display": "hidden",
|
||||
"title": "Privacy Policy"
|
||||
},
|
||||
|
||||
"advanced-separator": {
|
||||
"title": "ADVANCED",
|
||||
"type": "separator"
|
||||
},
|
||||
"manage-models": "Manage Models",
|
||||
"mcp": "Model Context Protocol",
|
||||
|
||||
"reference-separator": {
|
||||
"title": "REFERENCE",
|
||||
"type": "separator"
|
||||
},
|
||||
"settings": "Settings",
|
||||
"troubleshooting": "Troubleshooting",
|
||||
"model-parameters": "Model Parameters"
|
||||
"model-parameters": "Model Parameters",
|
||||
"privacy": "Privacy"
|
||||
}
|
||||
|
||||
139
docs/src/pages/docs/jan-models/jan-nano-128.mdx
Normal file
139
docs/src/pages/docs/jan-models/jan-nano-128.mdx
Normal file
@ -0,0 +1,139 @@
|
||||
---
|
||||
title: Jan Nano 128k
|
||||
description: Jan Models
|
||||
keywords:
|
||||
[
|
||||
Jan,
|
||||
Jan Models,
|
||||
Jan Model,
|
||||
Jan Model List,
|
||||
Menlo Models,
|
||||
Menlo Model,
|
||||
Jan-Nano-Gguf,
|
||||
ReZero,
|
||||
Model Context Protocol,
|
||||
MCP,
|
||||
]
|
||||
---
|
||||
|
||||
import { Callout } from 'nextra/components'
|
||||
|
||||
# Jan-Nano-128k
|
||||
|
||||
> Enabling deeper research through extended context understanding.
|
||||
|
||||
Jan-Nano-128k represents a notable advancement in compact language models for different applications. Building upon the
|
||||
success of Jan-Nano-32k, this enhanced version features a native 128k context window that enables deeper, more comprehensive
|
||||
research capabilities without the performance degradation typically associated with context extension methods.
|
||||
|
||||
You can have a look at all of our models, and download them from the HuggingFace [Menlo Models page](https://huggingface.co/Menlo).
|
||||
|
||||
**Key Improvements:**
|
||||
|
||||
- 🔍 Deeper Research: Extended context allows for processing entire research papers, lengthy documents, and complex multi-turn conversations
|
||||
- ⚡ Native 128k Window: Built to handle long contexts efficiently, maintaining performance across the full context range
|
||||
- 📈 Enhanced Performance: Unlike traditional context extension methods, Jan-Nano-128k's performance remains consistent with longer contexts
|
||||
|
||||
This model maintains full compatibility with Model Context Protocol (MCP) servers while dramatically expanding the scope of research
|
||||
tasks it can handle in a single session.
|
||||
|
||||
|
||||
## Why Jan-Nano-128k?
|
||||
|
||||
Most small models hit a wall at 8-32k tokens. Jan-Nano-128k goes beyond this limitation with a native 128k context window—that's roughly
|
||||
300 pages of text or an entire novel's worth of information processed simultaneously.
|
||||
|
||||
Unlike YaRN or PI methods that retrofit models beyond their limits and degrade performance, Jan-Nano-128k was architecturally rewired for
|
||||
128k contexts from the ground up. The result: an inverse scaling behavior where performance actually improves with longer contexts,
|
||||
maintaining consistent accuracy from 1k to 128k tokens as the model leverages more information for synthesis.
|
||||
|
||||
|
||||
<Callout type="info">
|
||||
**Position Interpolation (PI):** A method that extends a model's context by scaling down position indices to fit within the original context
|
||||
window. For example, to extend a 4k model to 32k, PI compresses the 32k positions into the original 4k range by dividing each position by 8.
|
||||
|
||||
**YaRN (Yet another RoPE extensioN method):** A more sophisticated context extension method that preserves frequently occurring tokens while
|
||||
selectively scaling others. YaRN divides position embeddings into frequency groups and applies different scaling factors to each, resulting
|
||||
in more efficient training and better performance than PI.
|
||||
|
||||
The key difference is that PI applies uniform scaling across all dimensions, while YaRN uses targeted interpolation based on frequency analysis—preserving
|
||||
high-frequency information that's crucial for distinguishing nearby tokens while interpolating lower frequencies more aggressively.
|
||||
</Callout>
|
||||
|
||||
**Applications unlocked:**
|
||||
- **Academic**: Extract key findings from 50+ papers simultaneously
|
||||
- **Legal**: Pinpoint relevant clauses across thousand-page contracts
|
||||
- **Code**: Trace specific functions through massive codebases
|
||||
- **Business**: Distill insights from quarters of financial data
|
||||
- **Content**: Maintain narrative coherence across book-length outputs
|
||||
|
||||
**MCP Usage:** Jan-Nano-128k doesn't memorize, it orchestrates. With MCP integration, it becomes a research conductor that fetches dozens
|
||||
of sources, holds everything in active memory, extracts precisely what's needed, and synthesizes findings across a marathon research session. It's
|
||||
not about understanding every word; it's about finding the needle in a haystack of haystacks.
|
||||
|
||||
## Evaluation
|
||||
|
||||
Jan-Nano-128k has been rigorously evaluated on the SimpleQA benchmark using our MCP-based methodology, demonstrating superior performance compared to its predecessor:
|
||||
|
||||
|
||||
|
||||
**Key findings:**
|
||||
- 15% improvement over Jan-Nano-32k on complex multi-document tasks
|
||||
- Consistent performance across all context lengths (no cliff at 64k like other extended models)
|
||||
- Superior citation accuracy when handling 10+ sources simultaneously
|
||||
|
||||
## 🖥️ How to Run Locally
|
||||
|
||||
### Demo
|
||||
|
||||
<video width="100%" controls>
|
||||
<source src="../_assets/jan-nano-demo.mp4" type="video/mp4" />
|
||||
Your browser does not support the video tag.
|
||||
</video>
|
||||
|
||||
### Quick Start Guide
|
||||
|
||||
1. **Download Jan**
|
||||
2. **Download Jan-Nano-128k**
|
||||
3. **Enable MCP**, the serper or the exa MCPs work very well with Jan-Nano-128k
|
||||
4. **Start researching**
|
||||
|
||||
### Usage
|
||||
|
||||
Deploy using VLLM:
|
||||
|
||||
```bash
|
||||
vllm serve Menlo/Jan-nano-128k \
|
||||
--host 0.0.0.0 \
|
||||
--port 1234 \
|
||||
--enable-auto-tool-choice \
|
||||
--tool-call-parser hermes \
|
||||
--rope-scaling '{"rope_type":"yarn","factor":3.2,"original_max_position_embeddings":40960}' --max-model-len 131072
|
||||
```
|
||||
|
||||
Or with `llama-server` from `llama.cpp`:
|
||||
|
||||
```bash
|
||||
llama-server ... --rope-scaling yarn --rope-scale 3.2 --yarn-orig-ctx 40960
|
||||
```
|
||||
|
||||
**Note:** The chat template is included in the tokenizer. For troubleshooting, download the [Non-think chat template](https://qwen.readthedocs.io/en/latest/_downloads/c101120b5bebcc2f12ec504fc93a965e/qwen3_nonthinking.jinja).
|
||||
|
||||
### Recommended Sampling Parameters
|
||||
|
||||
```yaml
|
||||
Temperature: 0.7
|
||||
Top-p: 0.8
|
||||
Top-k: 20
|
||||
Min-p: 0.0
|
||||
```
|
||||
|
||||
### Hardware Requirements
|
||||
- **Minimum**: 16GB RAM for Q4 quantization
|
||||
- **Recommended**: 24GB RAM for Q8 quantization
|
||||
- **Optimal**: 32GB+ RAM for full precision
|
||||
|
||||
## 🤝 Community & Support
|
||||
- **Discussions**: [HuggingFace Community](https://huggingface.co/Menlo/Jan-nano-128k/discussions)
|
||||
- **Issues**: [GitHub Repository](https://github.com/menloresearch/deep-research/issues)
|
||||
- **Discord**: Join our research community for tips and best practices
|
||||
@ -1,5 +1,5 @@
|
||||
---
|
||||
title: Jan Nano
|
||||
title: Jan Nano 32k
|
||||
description: Jan-Nano-Gguf Model
|
||||
keywords:
|
||||
[
|
||||
@ -20,15 +20,26 @@ import { Callout } from 'nextra/components'
|
||||
|
||||
# Jan Nano
|
||||
|
||||
Jan-Nano is a compact 4-billion parameter language model specifically designed and trained for deep
|
||||
research tasks. This model has been optimized to work seamlessly with Model Context Protocol (MCP) servers,
|
||||
enabling efficient integration with various research tools and data sources.
|
||||
|
||||
|
||||
## Why Jan Nano?
|
||||
|
||||
Most language models face a fundamental tradeoff where powerful capabilities require a lot computational resources. Jan
|
||||
Nano breaks this constraint through a focused design philosophy where instead of trying to know everything, it excels at
|
||||
knowing how to find anything.
|
||||
|
||||
|
||||
## What is Jan Nano?
|
||||
|
||||
Jan Nano is a compact 4-billion parameter language model specifically designed and trained for deep research tasks.
|
||||
This model has been optimized to work seamlessly with Model Context Protocol (MCP) servers, enabling efficient integration
|
||||
with various research tools and data sources.
|
||||
|
||||
The model and its different model variants are fully supported by Jan.
|
||||
|
||||
<Callout type="info">
|
||||
Jan-Nano can be used by Jan's stable version but its true capabilities shine in Jan's beta version, which
|
||||
offers MCP support. You can download Jan's beta version from [here](https://jan.ai/docs/desktop/beta).
|
||||
To use Jan-Nano, you will need to use a search engine via MCP. You can enable MCP in the **Settings**
|
||||
tab under **Advanced Settings**.
|
||||
</Callout>
|
||||
|
||||
|
||||
@ -45,10 +56,10 @@ The model and its different model variants are fully supported by Jan.
|
||||
- RTX 30/40 series or newer
|
||||
|
||||
|
||||
## Using Jan-Nano
|
||||
## Using Jan-Nano-32k
|
||||
|
||||
### Step 1
|
||||
Download Jan Beta from [here](https://jan.ai/docs/desktop/beta).
|
||||
Download Jan from [here](https://jan.ai/docs/desktop/).
|
||||
|
||||
### Step 2
|
||||
Go to the Hub Tab, search for Jan-Nano-Gguf, and click on the download button to the best model size for your system.
|
||||
@ -1,10 +0,0 @@
|
||||
{
|
||||
"overview": {
|
||||
"title": "Overview",
|
||||
"href": "/docs/menlo-models/overview"
|
||||
},
|
||||
"jan-nano": {
|
||||
"title": "Jan Nano",
|
||||
"href": "/docs/menlo-models/jan-nano"
|
||||
}
|
||||
}
|
||||
@ -1,40 +0,0 @@
|
||||
---
|
||||
title: Overview
|
||||
description: Jan Models
|
||||
keywords:
|
||||
[
|
||||
Jan,
|
||||
Jan Models,
|
||||
Jan Model,
|
||||
Jan Model List,
|
||||
Menlo Models,
|
||||
Menlo Model,
|
||||
Jan-Nano-Gguf,
|
||||
ReZero,
|
||||
Model Context Protocol,
|
||||
MCP,
|
||||
]
|
||||
---
|
||||
|
||||
# Menlo Models
|
||||
|
||||
At Menlo, we have focused on creating a series of models that are optimized for all sorts of tasks, including
|
||||
web search, deep research, robotic control, and using MCPs. Our latest model, Jan-Nano-Gguf, is available in Jan
|
||||
right now providing excellent results on tasks that use MCPs.
|
||||
|
||||
You can have a look at all of our models, and download them from the HuggingFace [Menlo Models page](https://huggingface.co/Menlo).
|
||||
|
||||
## Jan-Nano-Gguf (Available in Jan right now 🚀)
|
||||
|
||||
|
||||
|
||||
Jan-Nano-Gguf is a 4-billion parameter model that is optimized for deep research tasks. It has been trained on a
|
||||
variety of datasets and is designed to be used with the Model Context Protocol (MCP) servers.
|
||||
|
||||
|
||||
## ReZero
|
||||
|
||||
ReZero (Retry-Zero) is a reinforcement learning framework that improves RAG systems by rewarding LLMs for retrying
|
||||
failed queries. Traditional RAG approaches struggle when initial searches fail, but ReZero encourages persistence and
|
||||
alternative strategies. This increases accuracy from 25% to 46.88% in complex information-seeking tasks.
|
||||
|
||||
@ -1,70 +0,0 @@
|
||||
---
|
||||
title: Open Interpreter
|
||||
description: A step-by-step guide on integrating Jan with Open Interpreter.
|
||||
keywords:
|
||||
[
|
||||
Jan,
|
||||
Customizable Intelligence, LLM,
|
||||
local AI,
|
||||
privacy focus,
|
||||
free and open source,
|
||||
private and offline,
|
||||
conversational AI,
|
||||
no-subscription fee,
|
||||
large language models,
|
||||
Open Interpreter integration,
|
||||
Open Interpreter,
|
||||
]
|
||||
---
|
||||
|
||||
import { Callout, Steps } from 'nextra/components'
|
||||
|
||||
# Open Interpreter
|
||||
|
||||
## Integrate Open Interpreter with Jan
|
||||
|
||||
[Open Interpreter](https://github.com/KillianLucas/open-interpreter/) lets LLMs run code (Python, Javascript, Shell, and more) locally. After installing, you can chat with Open Interpreter through a ChatGPT-like interface in your terminal by running `interpreter`. To integrate Open Interpreter with Jan, follow the steps below:
|
||||
|
||||
<Steps>
|
||||
|
||||
### Step 1: Install Open Interpreter
|
||||
|
||||
1. Install Open Interpreter by running:
|
||||
|
||||
```bash
|
||||
pip install open-interpreter
|
||||
```
|
||||
|
||||
2. A Rust compiler is required to install Open Interpreter. If not already installed, run the following command or go to [this page](https://rustup.rs/) if you are running on Windows:
|
||||
|
||||
```bash
|
||||
sudo apt install rustc
|
||||
```
|
||||
|
||||
<Callout type='info'>
|
||||
The Rust compiler is necessary for building some native extensions that Open Interpreter requires.
|
||||
</Callout>
|
||||
|
||||
### Step 2: Configure Jan's Local API Server
|
||||
|
||||
Before using Open Interpreter, configure the model in `Settings` > `My Model` for Jan and activate its local API server.
|
||||
|
||||
#### Enabling Jan API Server
|
||||
|
||||
1. Click the `<>` button to access the **Local API Server** section in Jan.
|
||||
|
||||
2. Configure the server settings, including **IP Port**, **Cross-Origin-Resource-Sharing (CORS)**, and **Verbose Server Logs**.
|
||||
|
||||
3. Click **Start Server**.
|
||||
|
||||
### Step 3: Set the Open Interpreter Environment
|
||||
|
||||
1. For integration, provide the API Base (`http://localhost:1337/v1`) and the model ID (e.g., `mistral-ins-7b-q4`) when running Open Interpreter. For example, see the code below:
|
||||
|
||||
```zsh
|
||||
interpreter --api_base http://localhost:1337/v1 --model mistral-ins-7b-q4
|
||||
```
|
||||
|
||||
> **Open Interpreter is now ready for use!**
|
||||
|
||||
</Steps>
|
||||
9
docs/src/pages/platforms/_meta.json
Normal file
9
docs/src/pages/platforms/_meta.json
Normal file
@ -0,0 +1,9 @@
|
||||
{
|
||||
"-- Switcher": {
|
||||
"type": "separator",
|
||||
"title": "Switcher"
|
||||
},
|
||||
"index": {
|
||||
"display": "hidden"
|
||||
}
|
||||
}
|
||||
88
docs/src/pages/platforms/index.mdx
Normal file
88
docs/src/pages/platforms/index.mdx
Normal file
@ -0,0 +1,88 @@
|
||||
---
|
||||
title: Coming Soon
|
||||
description: Exciting new features and platforms are on the way. Stay tuned for Jan Web, Jan Mobile, and our API Platform.
|
||||
keywords:
|
||||
[
|
||||
Jan,
|
||||
Customizable Intelligence, LLM,
|
||||
local AI,
|
||||
privacy focus,
|
||||
free and open source,
|
||||
private and offline,
|
||||
conversational AI,
|
||||
no-subscription fee,
|
||||
large language models,
|
||||
coming soon,
|
||||
Jan Web,
|
||||
Jan Mobile,
|
||||
API Platform,
|
||||
]
|
||||
---
|
||||
|
||||
import { Callout } from 'nextra/components'
|
||||
import { Cards, Card } from 'nextra/components'
|
||||
|
||||
<div className="text-center py-12">
|
||||
<div className="mb-8">
|
||||
<h1 className="text-4xl font-bold bg-gradient-to-r from-blue-600 to-purple-600 bg-clip-text text-transparent mb-4 py-2">
|
||||
🚀 Coming Soon
|
||||
</h1>
|
||||
<p className="text-xl text-gray-600 dark:text-gray-300 max-w-2xl mx-auto">
|
||||
We're working on the next stage of Jan - making our local assistant more powerful and available in more platforms.
|
||||
</p>
|
||||
</div>
|
||||
|
||||
<div className="grid grid-cols-1 md:grid-cols-3 gap-6 max-w-4xl mx-auto mb-12">
|
||||
<div className="p-6 border border-gray-200 dark:border-gray-700 rounded-lg bg-gradient-to-br from-blue-50 to-indigo-50 dark:from-blue-900/20 dark:to-indigo-900/20">
|
||||
<div className="text-3xl mb-3">🌐</div>
|
||||
<h3 className="text-lg font-semibold mb-2">Jan Web</h3>
|
||||
<p className="text-sm text-gray-600 dark:text-gray-400">
|
||||
Access Jan directly from your browser with our powerful web interface
|
||||
</p>
|
||||
</div>
|
||||
|
||||
<div className="p-6 border border-gray-200 dark:border-gray-700 rounded-lg bg-gradient-to-br from-green-50 to-emerald-50 dark:from-green-900/20 dark:to-emerald-900/20">
|
||||
<div className="text-3xl mb-3">📱</div>
|
||||
<h3 className="text-lg font-semibold mb-2">Jan Mobile</h3>
|
||||
<p className="text-sm text-gray-600 dark:text-gray-400">
|
||||
Take Jan on the go with our native mobile applications
|
||||
</p>
|
||||
</div>
|
||||
|
||||
<div className="p-6 border border-gray-200 dark:border-gray-700 rounded-lg bg-gradient-to-br from-purple-50 to-pink-50 dark:from-purple-900/20 dark:to-pink-900/20">
|
||||
<div className="text-3xl mb-3">⚡</div>
|
||||
<h3 className="text-lg font-semibold mb-2">API Platform</h3>
|
||||
<p className="text-sm text-gray-600 dark:text-gray-400">
|
||||
Integrate Jan's capabilities into your applications with our API
|
||||
</p>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<Callout type="info">
|
||||
**Stay Updated**: Follow our [GitHub repository](https://github.com/menloresearch/jan) and join our [Discord community](https://discord.com/invite/FTk2MvZwJH) for the latest updates on these exciting releases!
|
||||
</Callout>
|
||||
|
||||
<div className="mt-12">
|
||||
<h2 className="text-2xl font-semibold mb-6">What to Expect</h2>
|
||||
<div className="text-left max-w-2xl mx-auto space-y-4">
|
||||
<div className="flex items-start gap-3">
|
||||
<span className="text-green-500 text-xl">✓</span>
|
||||
<div>
|
||||
<strong>Seamless Experience:</strong> Unified interface across all platforms
|
||||
</div>
|
||||
</div>
|
||||
<div className="flex items-start gap-3">
|
||||
<span className="text-green-500 text-xl">✓</span>
|
||||
<div>
|
||||
<strong>Privacy First:</strong> Same privacy-focused approach you trust
|
||||
</div>
|
||||
</div>
|
||||
<div className="flex items-start gap-3">
|
||||
<span className="text-green-500 text-xl">✓</span>
|
||||
<div>
|
||||
<strong>Developer Friendly:</strong> Robust APIs and comprehensive documentation
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
@ -65,6 +65,54 @@ const config: DocsThemeConfig = {
|
||||
</div>
|
||||
),
|
||||
},
|
||||
sidebar: {
|
||||
titleComponent: ({ type, title }) => {
|
||||
// eslint-disable-next-line react-hooks/rules-of-hooks
|
||||
const { asPath } = useRouter()
|
||||
if (type === 'separator' && title === 'Switcher') {
|
||||
return (
|
||||
<div className="-mx-2 hidden md:block">
|
||||
{[
|
||||
{ title: 'Jan', path: '/docs', Icon: LibraryBig },
|
||||
{
|
||||
title: 'Jan Web',
|
||||
path: '/platforms',
|
||||
Icon: BrainCircuit,
|
||||
},
|
||||
{ title: 'Jan Mobile', path: '/platforms', Icon: Blocks },
|
||||
{
|
||||
title: 'API Platform',
|
||||
path: '/platforms',
|
||||
Icon: Computer,
|
||||
},
|
||||
].map((item) =>
|
||||
asPath.startsWith(item.path) ? (
|
||||
<div
|
||||
key={item.path}
|
||||
className="group mb-3 flex flex-row items-center gap-3 nx-text-primary-800 dark:nx-text-primary-600"
|
||||
>
|
||||
<item.Icon className="w-7 h-7 p-1 border border-gray-200 dark:border-gray-700 rounded nx-bg-primary-100 dark:nx-bg-primary-400/10" />
|
||||
{item.title}
|
||||
</div>
|
||||
) : (
|
||||
<Link
|
||||
href={item.path}
|
||||
key={item.path}
|
||||
className="group mb-3 flex flex-row items-center gap-3 text-gray-500 hover:text-primary/100"
|
||||
>
|
||||
<item.Icon className="w-7 h-7 p-1 border rounded border-gray-200 dark:border-gray-700" />
|
||||
{item.title}
|
||||
</Link>
|
||||
)
|
||||
)}
|
||||
</div>
|
||||
)
|
||||
}
|
||||
return title
|
||||
},
|
||||
defaultMenuCollapseLevel: 1,
|
||||
toggleButton: true,
|
||||
},
|
||||
toc: {
|
||||
backToTop: true,
|
||||
},
|
||||
@ -83,14 +131,14 @@ const config: DocsThemeConfig = {
|
||||
name="description"
|
||||
content={
|
||||
frontMatter?.description ||
|
||||
`Run LLMs like Mistral or Llama2 locally and offline on your computer, or connect to remote AI APIs like OpenAI’s GPT-4 or Groq.`
|
||||
`Run LLMs like Mistral or Llama2 locally and offline on your computer, or connect to remote AI APIs like OpenAI's GPT-4 or Groq.`
|
||||
}
|
||||
/>
|
||||
<meta
|
||||
name="og:description"
|
||||
content={
|
||||
frontMatter?.description ||
|
||||
`Run LLMs like Mistral or Llama2 locally and offline on your computer, or connect to remote AI APIs like OpenAI’s GPT-4 or Groq.`
|
||||
`Run LLMs like Mistral or Llama2 locally and offline on your computer, or connect to remote AI APIs like OpenAI's GPT-4 or Groq.`
|
||||
}
|
||||
/>
|
||||
<link
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user