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
| /** | |
| * Utility functions for markdown processing in MarkdownContent component. | |
| */ | |
| import type { RootContent as HastRootContent } from 'hast'; | |
| /** | |
| * Generates a unique identifier for a HAST node based on its position. | |
| * Used for stable block identification during incremental rendering. | |
| * @param node - The HAST root content node | |
| * @param indexFallback - Fallback index if position is unavailable | |
| * @returns Unique string identifier for the node | |
| */ | |
| export function getHastNodeId(node: HastRootContent, indexFallback: number): string { | |
| const position = node.position; | |
| if (position?.start?.offset != null && position?.end?.offset != null) { | |
| return `hast-${position.start.offset}-${position.end.offset}`; | |
| } | |
| return `${node.type}-${indexFallback}`; | |
| } | |
| /** | |
| * Generates a hash for MDAST node based on its position. | |
| * Used for cache lookup during incremental rendering. | |
| */ | |
| export function getMdastNodeHash(node: unknown, index: number): string { | |
| const n = node as { | |
| type?: string; | |
| position?: { start?: { offset?: number }; end?: { offset?: number } }; | |
| }; | |
| if (n.position?.start?.offset != null && n.position?.end?.offset != null) { | |
| return `${n.type}-${n.position.start.offset}-${n.position.end.offset}`; | |
| } | |
| return `${n.type}-idx${index}`; | |
| } | |
| /** | |
| * Determines if the new content is an append (new content added to existing blocks). | |
| * This is used to optimize cache reuse during streaming updates. | |
| * | |
| * @param newContent - The new markdown content | |
| * @param previousContent - The previous markdown content to check against | |
| * @returns true if the content appears to be an append operation | |
| */ | |
| export function isAppendMode(newContent: string, previousContent: string): boolean { | |
| return previousContent.length > 0 && newContent.startsWith(previousContent); | |
| } | |
| export interface CodeInfo { | |
| rawCode: string; | |
| language: string; | |
| } | |
| /** | |
| * Extracts code information from a button click target within a code block. | |
| * @param target - The clicked button element | |
| * @returns Object with rawCode and language, or null if extraction fails | |
| */ | |
| export function getCodeInfoFromTarget(target: HTMLElement): CodeInfo | null { | |
| const wrapper = target.closest('.code-block-wrapper'); | |
| if (!wrapper) { | |
| console.error('No wrapper found'); | |
| return null; | |
| } | |
| const codeElement = wrapper.querySelector<HTMLElement>('code[data-code-id]'); | |
| if (!codeElement) { | |
| console.error('No code element found in wrapper'); | |
| return null; | |
| } | |
| const rawCode = codeElement.textContent ?? ''; | |
| const languageLabel = wrapper.querySelector<HTMLElement>('.code-language'); | |
| const language = languageLabel?.textContent?.trim() || 'text'; | |
| return { rawCode, language }; | |
| } | |