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.
- 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: 5,047 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 | import { isSvgMimeType, svgBase64UrlToPngDataURL } from './svg-to-png';
import { isWebpMimeType, webpBase64UrlToPngDataURL } from './webp-to-png';
import { heicFileToJpegDataURL, isHeicMimeType } from './heic-to-jpeg';
import { FileTypeCategory } from '$lib/enums';
import { SETTINGS_KEYS } from '$lib/constants';
import { modelsStore } from '$lib/stores/models.svelte';
import { settingsStore } from '$lib/stores/settings.svelte';
import { toast } from 'svelte-sonner';
import { getFileTypeCategory } from '$lib/utils';
import { convertPDFToText } from './pdf-processing';
/**
* Read a file as a data URL (base64 encoded)
* @param file - The file to read
* @returns Promise resolving to the data URL string
*/
function readFileAsDataURL(file: File): Promise<string> {
return new Promise((resolve, reject) => {
const reader = new FileReader();
reader.onload = () => resolve(reader.result as string);
reader.onerror = () => reject(reader.error);
reader.readAsDataURL(file);
});
}
/**
* Read a file as UTF-8 text
* @param file - The file to read
* @returns Promise resolving to the text content
*/
function readFileAsUTF8(file: File): Promise<string> {
return new Promise((resolve, reject) => {
const reader = new FileReader();
reader.onload = () => resolve(reader.result as string);
reader.onerror = () => reject(reader.error);
reader.readAsText(file);
});
}
/**
* Process uploaded files into ChatUploadedFile format with previews and content
*
* This function processes various file types and generates appropriate previews:
* - Images: Base64 data URLs with format normalization (SVG/WebP → PNG)
* - Text files: UTF-8 content extraction
* - PDFs: Metadata only (processed later in conversion pipeline)
* - Audio: Base64 data URLs for preview
*
* @param files - Array of File objects to process
* @returns Promise resolving to array of ChatUploadedFile objects
*/
export async function processFilesToChatUploaded(
files: File[],
activeModelId?: string
): Promise<ChatUploadedFile[]> {
const results: ChatUploadedFile[] = [];
for (const file of files) {
const id = Date.now().toString() + Math.random().toString(36).substr(2, 9);
const base: ChatUploadedFile = {
id,
name: file.name,
size: file.size,
type: file.type,
file
};
try {
if (getFileTypeCategory(file.type) === FileTypeCategory.IMAGE) {
let preview = await readFileAsDataURL(file);
// Normalize SVG and WebP to PNG, and HEIC to compressed JPEG, in previews
if (isSvgMimeType(file.type)) {
try {
preview = await svgBase64UrlToPngDataURL(preview);
} catch (err) {
console.error('Failed to convert SVG to PNG:', err);
}
} else if (isWebpMimeType(file.type)) {
try {
preview = await webpBase64UrlToPngDataURL(preview);
} catch (err) {
console.error('Failed to convert WebP to PNG:', err);
}
} else if (isHeicMimeType(file.type)) {
try {
preview = await heicFileToJpegDataURL(file);
} catch (err) {
console.error('Failed to convert HEIC to PNG:', err);
continue;
}
}
results.push({ ...base, preview });
} else if (getFileTypeCategory(file.type) === FileTypeCategory.PDF) {
// Extract text content from PDF for preview
try {
const textContent = await convertPDFToText(file);
results.push({ ...base, textContent });
} catch (err) {
console.warn('Failed to extract text from PDF, adding without content:', err);
results.push(base);
}
// Show suggestion toast if vision model is available but PDF as image is disabled
const hasVisionSupport = activeModelId
? modelsStore.modelSupportsVision(activeModelId)
: false;
const currentConfig = settingsStore.config;
if (hasVisionSupport && !currentConfig.pdfAsImage) {
toast.info(`You can enable parsing PDF as images with vision models.`, {
duration: 8000,
action: {
label: 'Enable PDF as Images',
onClick: () => {
settingsStore.updateConfig(SETTINGS_KEYS.PDF_AS_IMAGE, true);
toast.success('PDF parsing as images enabled!', {
duration: 3000
});
}
}
});
}
} else if (getFileTypeCategory(file.type) === FileTypeCategory.AUDIO) {
// Generate preview URL for audio files
const preview = await readFileAsDataURL(file);
results.push({ ...base, preview });
} else if (getFileTypeCategory(file.type) === FileTypeCategory.VIDEO) {
// Generate preview URL for video files
const preview = await readFileAsDataURL(file);
results.push({ ...base, preview });
} else {
// Fallback: treat unknown files as text
try {
const textContent = await readFileAsUTF8(file);
results.push({ ...base, textContent });
} catch (err) {
console.warn('Failed to read file as text, adding without content:', err);
results.push(base);
}
}
} catch (error) {
console.error('Error processing file', file.name, error);
results.push(base);
}
}
return results;
}
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