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https://github.com/mybigday/llama.rn
React Native binding of llama.cpp
https://github.com/mybigday/llama.rn
android ios llama llama-cpp llm react-native
Last synced: 5 days ago
JSON representation
React Native binding of llama.cpp
- Host: GitHub
- URL: https://github.com/mybigday/llama.rn
- Owner: mybigday
- License: mit
- Created: 2023-08-02T04:26:30.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-02-09T02:35:51.000Z (11 days ago)
- Last Synced: 2025-02-13T16:05:29.136Z (6 days ago)
- Topics: android, ios, llama, llama-cpp, llm, react-native
- Language: C++
- Homepage:
- Size: 8.21 MB
- Stars: 413
- Watchers: 9
- Forks: 37
- Open Issues: 17
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
Awesome Lists containing this project
- awesome-repositories - mybigday/llama.rn - React Native binding of llama.cpp (C++)
README
# llama.rn
[](https://github.com/mybigday/llama.rn/actions)
[](https://opensource.org/licenses/MIT)
[](https://www.npmjs.com/package/llama.rn/)
React Native binding of [llama.cpp](https://github.com/ggerganov/llama.cpp).
[llama.cpp](https://github.com/ggerganov/llama.cpp): Inference of [LLaMA](https://arxiv.org/abs/2302.13971) model in pure C/C++
## Installation
```sh
npm install llama.rn
```
#### iOS
Please re-run `npx pod-install` again.
By default, `llama.rn` will use pre-built `rnllama.xcframework` for iOS. If you want to build from source, please set `RNLLAMA_BUILD_FROM_SOURCE` to `1` in your Podfile.
#### Android
Add proguard rule if it's enabled in project (android/app/proguard-rules.pro):
```proguard
# llama.rn
-keep class com.rnllama.** { *; }
```
By default, `llama.rn` will use pre-built libraries for Android. If you want to build from source, please set `rnllamaBuildFromSource` to `true` in `android/gradle.properties`.
## Obtain the model
You can search HuggingFace for available models (Keyword: [`GGUF`](https://huggingface.co/search/full-text?q=GGUF&type=model)).
For get a GGUF model or quantize manually, see [`Prepare and Quantize`](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#prepare-and-quantize) section in llama.cpp.
## Usage
Load model info only:
```js
import { loadLlamaModelInfo } from 'llama.rn'
const modelPath = 'file://'
console.log('Model Info:', await loadLlamaModelInfo(modelPath))
```
Initialize a Llama context & do completion:
```js
import { initLlama } from 'llama.rn'
// Initial a Llama context with the model (may take a while)
const context = await initLlama({
model: modelPath,
use_mlock: true,
n_ctx: 2048,
n_gpu_layers: 99, // number of layers to store in VRAM (Currently only for iOS)
// embedding: true, // use embedding
})
const stopWords = ['', '<|end|>', '<|eot_id|>', '<|end_of_text|>', '<|im_end|>', '<|EOT|>', '<|END_OF_TURN_TOKEN|>', '<|end_of_turn|>', '<|endoftext|>']
// Do chat completion
const msgResult = await context.completion(
{
messages: [
{
role: 'system',
content: 'This is a conversation between user and assistant, a friendly chatbot.',
},
{
role: 'user',
content: 'Hello!',
},
],
n_predict: 100,
stop: stopWords,
// ...other params
},
(data) => {
// This is a partial completion callback
const { token } = data
},
)
console.log('Result:', msgResult.text)
console.log('Timings:', msgResult.timings)
// Or do text completion
const textResult = await context.completion(
{
prompt: 'This is a conversation between user and llama, a friendly chatbot. respond in simple markdown.\n\nUser: Hello!\nLlama:',
n_predict: 100,
stop: [...stopWords, 'Llama:', 'User:'],
// ...other params
},
(data) => {
// This is a partial completion callback
const { token } = data
},
)
console.log('Result:', textResult.text)
console.log('Timings:', textResult.timings)
```
The binding’s deisgn inspired by [server.cpp](https://github.com/ggerganov/llama.cpp/tree/master/examples/server) example in llama.cpp, so you can map its API to LlamaContext:
- `/completion` and `/chat/completions`: `context.completion(params, partialCompletionCallback)`
- `/tokenize`: `context.tokenize(content)`
- `/detokenize`: `context.detokenize(tokens)`
- `/embedding`: `context.embedding(content)`
- Other methods
- `context.loadSession(path)`
- `context.saveSession(path)`
- `context.stopCompletion()`
- `context.release()`
Please visit the [Documentation](docs/API) for more details.
You can also visit the [example](example) to see how to use it.
## Tool Calling
`llama.rn` has universal tool call support by using [minja](https://github.com/google/minja) (as Jinja template parser) and [chat.cpp](https://github.com/ggerganov/llama.cpp/blob/master/common/chat.cpp) in llama.cpp.
Example:
```js
import { initLlama } from 'llama.rn'
const context = await initLlama({
// ...params
})
const { text, tool_calls } = await context.completion({
// ...params
jinja: true, // Enable Jinja template parser
tool_choice: 'auto',
tools: [
{
type: 'function',
function: {
name: 'ipython',
description:
'Runs code in an ipython interpreter and returns the result of the execution after 60 seconds.',
parameters: {
type: 'object',
properties: {
code: {
type: 'string',
description: 'The code to run in the ipython interpreter.',
},
},
required: ['code'],
},
},
},
],
messages: [
{
role: 'system',
content: 'You are a helpful assistant that can answer questions and help with tasks.',
},
{
role: 'user',
content: 'Test',
},
],
})
console.log('Result:', text)
// If tool_calls is not empty, it means the model has called the tool
if (tool_calls) console.log('Tool Calls:', tool_calls)
```
You can check [chat.cpp](https://github.com/ggerganov/llama.cpp/blob/6eecde3cc8fda44da7794042e3668de4af3c32c6/common/chat.cpp#L7-L23) for models has native tool calling support, or it will fallback to `GENERIC` type tool call.
The generic tool call will be always JSON object as output, the output will be like `{"response": "..."}` when it not decided to use tool call.
## Grammar Sampling
GBNF (GGML BNF) is a format for defining [formal grammars](https://en.wikipedia.org/wiki/Formal_grammar) to constrain model outputs in `llama.cpp`. For example, you can use it to force the model to generate valid JSON, or speak only in emojis.
You can see [GBNF Guide](https://github.com/ggerganov/llama.cpp/tree/master/grammars) for more details.
`llama.rn` provided a built-in function to convert JSON Schema to GBNF:
Example gbnf grammar:
```bnf
root ::= object
value ::= object | array | string | number | ("true" | "false" | "null") ws
object ::=
"{" ws (
string ":" ws value
("," ws string ":" ws value)*
)? "}" ws
array ::=
"[" ws (
value
("," ws value)*
)? "]" ws
string ::=
"\"" (
[^"\\\x7F\x00-\x1F] |
"\\" (["\\bfnrt] | "u" [0-9a-fA-F]{4}) # escapes
)* "\"" ws
number ::= ("-"? ([0-9] | [1-9] [0-9]{0,15})) ("." [0-9]+)? ([eE] [-+]? [0-9] [1-9]{0,15})? ws
# Optional space: by convention, applied in this grammar after literal chars when allowed
ws ::= | " " | "\n" [ \t]{0,20}
```
```js
import { initLlama } from 'llama.rn'
const gbnf = '...'
const context = await initLlama({
// ...params
grammar: gbnf,
})
const { text } = await context.completion({
// ...params
messages: [
{
role: 'system',
content: 'You are a helpful assistant that can answer questions and help with tasks.',
},
{
role: 'user',
content: 'Test',
},
],
})
console.log('Result:', text)
```
Also, this is how `json_schema` works in `response_format` during completion, it converts the json_schema to gbnf grammar.
## Mock `llama.rn`
We have provided a mock version of `llama.rn` for testing purpose you can use on Jest:
```js
jest.mock('llama.rn', () => require('llama.rn/jest/mock'))
```
## NOTE
iOS:
- The [Extended Virtual Addressing](https://developer.apple.com/documentation/bundleresources/entitlements/com_apple_developer_kernel_extended-virtual-addressing) capability is recommended to enable on iOS project.
- Metal:
- We have tested to know some devices is not able to use Metal (GPU) due to llama.cpp used SIMD-scoped operation, you can check if your device is supported in [Metal feature set tables](https://developer.apple.com/metal/Metal-Feature-Set-Tables.pdf), Apple7 GPU will be the minimum requirement.
- It's also not supported in iOS simulator due to [this limitation](https://developer.apple.com/documentation/metal/developing_metal_apps_that_run_in_simulator#3241609), we used constant buffers more than 14.
Android:
- Currently only supported arm64-v8a / x86_64 platform, this means you can't initialize a context on another platforms. The 64-bit platform are recommended because it can allocate more memory for the model.
- No integrated any GPU backend yet.
## Contributing
See the [contributing guide](CONTRIBUTING.md) to learn how to contribute to the repository and the development workflow.
## Apps using `llama.rn`
- [BRICKS](https://bricks.tools): Our product for building interactive signage in simple way. We provide LLM functions as Generator LLM/Assistant.
- [ChatterUI](https://github.com/Vali-98/ChatterUI): Simple frontend for LLMs built in react-native.
- [PocketPal AI](https://github.com/a-ghorbani/pocketpal-ai): An app that brings language models directly to your phone.
## Node.js binding
- [llama.node](https://github.com/mybigday/llama.node): An another Node.js binding of `llama.cpp` but made API same as `llama.rn`.
## License
MIT
---
Made with [create-react-native-library](https://github.com/callstack/react-native-builder-bob)
---
Built and maintained by BRICKS.