Safetensors
GGUF
Turkish
llama
Llama-3
instruct
finetune
chatml
gpt4
synthetic data
distillation
function calling
json mode
axolotl
roleplaying
chat
Instructions to use tda45/TdAI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use tda45/TdAI with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="tda45/TdAI", filename="llama.cpp/models/ggml-vocab-aquila.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use tda45/TdAI with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf tda45/TdAI # Run inference directly in the terminal: llama cli -hf tda45/TdAI
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf tda45/TdAI # Run inference directly in the terminal: llama cli -hf tda45/TdAI
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf tda45/TdAI # Run inference directly in the terminal: ./llama-cli -hf tda45/TdAI
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf tda45/TdAI # Run inference directly in the terminal: ./build/bin/llama-cli -hf tda45/TdAI
Use Docker
docker model run hf.co/tda45/TdAI
- LM Studio
- Jan
- Ollama
How to use tda45/TdAI with Ollama:
ollama run hf.co/tda45/TdAI
- Unsloth Studio
How to use tda45/TdAI with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for tda45/TdAI to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for tda45/TdAI to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for tda45/TdAI to start chatting
- Atomic Chat new
- Docker Model Runner
How to use tda45/TdAI with Docker Model Runner:
docker model run hf.co/tda45/TdAI
- Lemonade
How to use tda45/TdAI with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull tda45/TdAI
Run and chat with the model
lemonade run user.TdAI-{{QUANT_TAG}}List all available models
lemonade list
File size: 4,390 Bytes
15c3607 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 | /**
* PDF processing utilities using PDF.js
* Handles PDF text extraction and image conversion in the browser
*/
import { browser } from '$app/environment';
import { MimeTypeApplication, MimeTypeImage } from '$lib/enums';
import * as pdfjs from 'pdfjs-dist';
type TextContent = {
items: Array<{ str: string }>;
};
if (browser) {
// Import worker as text and create blob URL for inline bundling
import('pdfjs-dist/build/pdf.worker.min.mjs?raw')
.then((workerModule) => {
const workerBlob = new Blob([workerModule.default], { type: 'application/javascript' });
pdfjs.GlobalWorkerOptions.workerSrc = URL.createObjectURL(workerBlob);
})
.catch(() => {
console.warn('Failed to load PDF.js worker, PDF processing may not work');
});
}
/**
* Convert a File object to ArrayBuffer for PDF.js processing
* @param file - The PDF file to convert
* @returns Promise resolving to the file's ArrayBuffer
*/
async function getFileAsBuffer(file: File): Promise<ArrayBuffer> {
return new Promise((resolve, reject) => {
const reader = new FileReader();
reader.onload = (event) => {
if (event.target?.result) {
resolve(event.target.result as ArrayBuffer);
} else {
reject(new Error('Failed to read file.'));
}
};
reader.onerror = () => {
reject(new Error('Failed to read file.'));
};
reader.readAsArrayBuffer(file);
});
}
/**
* Extract text content from a PDF file
* @param file - The PDF file to process
* @returns Promise resolving to the extracted text content
*/
export async function convertPDFToText(file: File): Promise<string> {
if (!browser) {
throw new Error('PDF processing is only available in the browser');
}
try {
const buffer = await getFileAsBuffer(file);
const pdf = await pdfjs.getDocument({ data: buffer }).promise;
const numPages = pdf.numPages;
const textContentPromises: Promise<TextContent>[] = [];
for (let i = 1; i <= numPages; i++) {
// eslint-disable-next-line @typescript-eslint/no-explicit-any
textContentPromises.push(pdf.getPage(i).then((page: any) => page.getTextContent()));
}
const textContents = await Promise.all(textContentPromises);
const textItems = textContents.flatMap((textContent: TextContent) =>
textContent.items.map((item) => item.str ?? '')
);
return textItems.join('\n');
} catch (error) {
console.error('Error converting PDF to text:', error);
throw new Error(
`Failed to convert PDF to text: ${error instanceof Error ? error.message : 'Unknown error'}`
);
}
}
/**
* Convert PDF pages to PNG images as data URLs
* @param file - The PDF file to convert
* @param scale - Rendering scale factor (default: 1.5)
* @returns Promise resolving to array of PNG data URLs
*/
export async function convertPDFToImage(file: File, scale: number = 1.5): Promise<string[]> {
if (!browser) {
throw new Error('PDF processing is only available in the browser');
}
try {
const buffer = await getFileAsBuffer(file);
const doc = await pdfjs.getDocument({ data: buffer }).promise;
const pages: Promise<string>[] = [];
for (let i = 1; i <= doc.numPages; i++) {
const page = await doc.getPage(i);
const viewport = page.getViewport({ scale });
const canvas = document.createElement('canvas');
const ctx = canvas.getContext('2d');
canvas.width = viewport.width;
canvas.height = viewport.height;
if (!ctx) {
throw new Error('Failed to get 2D context from canvas');
}
const task = page.render({
canvasContext: ctx,
viewport: viewport,
canvas: canvas
});
pages.push(
task.promise.then(() => {
return canvas.toDataURL(MimeTypeImage.PNG);
})
);
}
return await Promise.all(pages);
} catch (error) {
console.error('Error converting PDF to images:', error);
throw new Error(
`Failed to convert PDF to images: ${error instanceof Error ? error.message : 'Unknown error'}`
);
}
}
/**
* Check if a file is a PDF based on its MIME type
* @param file - The file to check
* @returns True if the file is a PDF
*/
export function isPdfFile(file: File): boolean {
return file.type === MimeTypeApplication.PDF;
}
/**
* Check if a MIME type represents a PDF
* @param mimeType - The MIME type to check
* @returns True if the MIME type is application/pdf
*/
export function isApplicationMimeType(mimeType: string): boolean {
return mimeType === MimeTypeApplication.PDF;
}
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