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
| import { convertPDFToImage, convertPDFToText } from './pdf-processing'; | |
| import { isSvgMimeType, svgBase64UrlToPngDataURL } from './svg-to-png'; | |
| import { isWebpMimeType, webpBase64UrlToPngDataURL } from './webp-to-png'; | |
| import { FileTypeCategory, AttachmentType, SpecialFileType } from '$lib/enums'; | |
| import { SETTINGS_KEYS } from '$lib/constants'; | |
| import { config, settingsStore } from '$lib/stores/settings.svelte'; | |
| import { modelsStore } from '$lib/stores/models.svelte'; | |
| import { getFileTypeCategory } from '$lib/utils'; | |
| import { readFileAsText, isLikelyTextFile } from './text-files'; | |
| import { toast } from 'svelte-sonner'; | |
| import type { FileProcessingResult, ChatUploadedFile, DatabaseMessageExtra } from '$lib/types'; | |
| function readFileAsBase64(file: File): Promise<string> { | |
| return new Promise((resolve, reject) => { | |
| const reader = new FileReader(); | |
| reader.onload = () => { | |
| // Extract base64 data without the data URL prefix | |
| const dataUrl = reader.result as string; | |
| const base64 = dataUrl.split(',')[1]; | |
| resolve(base64); | |
| }; | |
| reader.onerror = () => reject(reader.error); | |
| reader.readAsDataURL(file); | |
| }); | |
| } | |
| export async function parseFilesToMessageExtras( | |
| files: ChatUploadedFile[], | |
| activeModelId?: string | |
| ): Promise<FileProcessingResult> { | |
| const extras: DatabaseMessageExtra[] = []; | |
| const emptyFiles: string[] = []; | |
| for (const file of files) { | |
| if (file.type === SpecialFileType.MCP_PROMPT && file.mcpPrompt) { | |
| extras.push({ | |
| type: AttachmentType.MCP_PROMPT, | |
| name: file.name, | |
| size: file.size, | |
| serverName: file.mcpPrompt.serverName, | |
| promptName: file.mcpPrompt.promptName, | |
| content: file.textContent ?? '', | |
| arguments: file.mcpPrompt.arguments | |
| }); | |
| continue; | |
| } | |
| if (getFileTypeCategory(file.type) === FileTypeCategory.IMAGE) { | |
| if (file.preview) { | |
| let base64Url = file.preview; | |
| if (isSvgMimeType(file.type)) { | |
| try { | |
| base64Url = await svgBase64UrlToPngDataURL(base64Url); | |
| } catch (error) { | |
| console.error('Failed to convert SVG to PNG for database storage:', error); | |
| } | |
| } else if (isWebpMimeType(file.type)) { | |
| try { | |
| base64Url = await webpBase64UrlToPngDataURL(base64Url); | |
| } catch (error) { | |
| console.error('Failed to convert WebP to PNG for database storage:', error); | |
| } | |
| } | |
| extras.push({ | |
| type: AttachmentType.IMAGE, | |
| name: file.name, | |
| size: file.size, | |
| base64Url | |
| }); | |
| } | |
| } else if (getFileTypeCategory(file.type) === FileTypeCategory.AUDIO) { | |
| // Process audio files (MP3 and WAV) | |
| try { | |
| const base64Data = await readFileAsBase64(file.file); | |
| extras.push({ | |
| type: AttachmentType.AUDIO, | |
| name: file.name, | |
| size: file.size, | |
| base64Data: base64Data, | |
| mimeType: file.type | |
| }); | |
| } catch (error) { | |
| console.error(`Failed to process audio file ${file.name}:`, error); | |
| } | |
| } else if (getFileTypeCategory(file.type) === FileTypeCategory.VIDEO) { | |
| // Process video files (MP4, etc) | |
| try { | |
| const base64Data = await readFileAsBase64(file.file); | |
| extras.push({ | |
| type: AttachmentType.VIDEO, | |
| name: file.name, | |
| size: file.size, | |
| base64Data: base64Data, | |
| mimeType: file.type | |
| }); | |
| } catch (error) { | |
| console.error(`Failed to process video file ${file.name}:`, error); | |
| } | |
| } else if (getFileTypeCategory(file.type) === FileTypeCategory.PDF) { | |
| try { | |
| // Always get base64 data for preview functionality | |
| const base64Data = await readFileAsBase64(file.file); | |
| const currentConfig = config(); | |
| // Use per-model vision check for router mode | |
| const hasVisionSupport = activeModelId | |
| ? modelsStore.modelSupportsVision(activeModelId) | |
| : false; | |
| // Force PDF-to-text for non-vision models | |
| let shouldProcessAsImages = Boolean(currentConfig.pdfAsImage) && hasVisionSupport; | |
| // If user had pdfAsImage enabled but model doesn't support vision, update setting and notify | |
| if (currentConfig.pdfAsImage && !hasVisionSupport) { | |
| console.log('Non-vision model detected: forcing PDF-to-text mode and updating settings'); | |
| // Update the setting in localStorage | |
| settingsStore.updateConfig(SETTINGS_KEYS.PDF_AS_IMAGE, false); | |
| // Show toast notification to user | |
| toast.warning( | |
| 'PDF setting changed: Non-vision model detected, PDFs will be processed as text instead of images.', | |
| { | |
| duration: 5000 | |
| } | |
| ); | |
| shouldProcessAsImages = false; | |
| } | |
| if (shouldProcessAsImages) { | |
| // Process PDF as images (only for vision models) | |
| try { | |
| const images = await convertPDFToImage(file.file); | |
| // Show success toast for PDF image processing | |
| toast.success( | |
| `PDF "${file.name}" processed as ${images.length} images for vision model.`, | |
| { | |
| duration: 3000 | |
| } | |
| ); | |
| extras.push({ | |
| type: AttachmentType.PDF, | |
| name: file.name, | |
| size: file.size, | |
| content: `PDF file with ${images.length} pages`, | |
| images: images, | |
| processedAsImages: true, | |
| base64Data: base64Data | |
| }); | |
| } catch (imageError) { | |
| console.warn( | |
| `Failed to process PDF ${file.name} as images, falling back to text:`, | |
| imageError | |
| ); | |
| // Fallback to text processing | |
| const content = await convertPDFToText(file.file); | |
| extras.push({ | |
| type: AttachmentType.PDF, | |
| name: file.name, | |
| size: file.size, | |
| content: content, | |
| processedAsImages: false, | |
| base64Data: base64Data | |
| }); | |
| } | |
| } else { | |
| // Process PDF as text (default or forced for non-vision models) | |
| const content = await convertPDFToText(file.file); | |
| // Show success toast for PDF text processing | |
| toast.success(`PDF "${file.name}" processed as text content.`, { | |
| duration: 3000 | |
| }); | |
| extras.push({ | |
| type: AttachmentType.PDF, | |
| name: file.name, | |
| size: file.size, | |
| content: content, | |
| processedAsImages: false, | |
| base64Data: base64Data | |
| }); | |
| } | |
| } catch (error) { | |
| console.error(`Failed to process PDF file ${file.name}:`, error); | |
| } | |
| } else { | |
| try { | |
| const content = await readFileAsText(file.file); | |
| // Check if file is empty | |
| if (content.trim() === '') { | |
| console.warn(`File ${file.name} is empty and will be skipped`); | |
| emptyFiles.push(file.name); | |
| } else if (isLikelyTextFile(content)) { | |
| extras.push({ | |
| type: AttachmentType.TEXT, | |
| name: file.name, | |
| size: file.size, | |
| content: content | |
| }); | |
| } else { | |
| console.warn(`File ${file.name} appears to be binary and will be skipped`); | |
| } | |
| } catch (error) { | |
| console.error(`Failed to read file ${file.name}:`, error); | |
| } | |
| } | |
| } | |
| return { extras, emptyFiles }; | |
| } | |