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 { AttachmentType, FileTypeCategory, SpecialFileType } from '$lib/enums'; | |
| import { getFileTypeCategory, getFileTypeCategoryByExtension, isImageFile } from '$lib/utils'; | |
| import type { | |
| AttachmentDisplayItemsOptions, | |
| ChatAttachmentDisplayItem, | |
| ChatUploadedFile | |
| } from '$lib/types'; | |
| /** | |
| * Check if a display item represents an MCP prompt | |
| * (either from attachment type or uploaded file with mcpPrompt metadata) | |
| */ | |
| export function isMcpPrompt(item: ChatAttachmentDisplayItem): boolean { | |
| if (item.attachment?.type === AttachmentType.MCP_PROMPT) { | |
| return true; | |
| } | |
| if (item.uploadedFile?.type === SpecialFileType.MCP_PROMPT && item.uploadedFile.mcpPrompt) { | |
| return true; | |
| } | |
| return false; | |
| } | |
| /** | |
| * Check if a display item represents an MCP resource | |
| */ | |
| export function isMcpResource(item: ChatAttachmentDisplayItem): boolean { | |
| return item.attachment?.type === AttachmentType.MCP_RESOURCE; | |
| } | |
| /** | |
| * Gets the file type category from an uploaded file, checking both MIME type and extension | |
| */ | |
| function getUploadedFileCategory(file: ChatUploadedFile): FileTypeCategory | null { | |
| const categoryByMime = getFileTypeCategory(file.type); | |
| if (categoryByMime) { | |
| return categoryByMime; | |
| } | |
| return getFileTypeCategoryByExtension(file.name); | |
| } | |
| /** | |
| * Creates a unified list of display items from uploaded files and stored attachments. | |
| * Items are returned in reverse order (newest first). | |
| */ | |
| export function getAttachmentDisplayItems( | |
| options: AttachmentDisplayItemsOptions | |
| ): ChatAttachmentDisplayItem[] { | |
| const { uploadedFiles = [], attachments = [] } = options; | |
| const items: ChatAttachmentDisplayItem[] = []; | |
| // Add uploaded files (ChatForm) | |
| for (const file of uploadedFiles) { | |
| items.push({ | |
| id: file.id, | |
| name: file.name, | |
| size: file.size, | |
| preview: file.preview, | |
| isImage: getUploadedFileCategory(file) === FileTypeCategory.IMAGE, | |
| isLoading: file.isLoading, | |
| loadError: file.loadError, | |
| uploadedFile: file, | |
| textContent: file.textContent | |
| }); | |
| } | |
| // Add stored attachments (ChatMessage) | |
| for (const [index, attachment] of attachments.entries()) { | |
| const isImage = isImageFile(attachment); | |
| items.push({ | |
| id: `attachment-${index}`, | |
| name: attachment.name, | |
| size: 'size' in attachment ? attachment.size : undefined, | |
| preview: isImage && 'base64Url' in attachment ? attachment.base64Url : undefined, | |
| isImage, | |
| attachment, | |
| attachmentIndex: index, | |
| textContent: 'content' in attachment ? attachment.content : undefined | |
| }); | |
| } | |
| return items.reverse(); | |
| } | |