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 hljs from 'highlight.js'; | |
| import { | |
| NEWLINE, | |
| DEFAULT_LANGUAGE, | |
| LANG_PATTERN, | |
| AMPERSAND_REGEX, | |
| LT_REGEX, | |
| GT_REGEX, | |
| FENCE_PATTERN | |
| } from '$lib/constants'; | |
| export interface IncompleteCodeBlock { | |
| language: string; | |
| code: string; | |
| openingIndex: number; | |
| } | |
| /** | |
| * Highlights code using highlight.js | |
| * @param code - The code to highlight | |
| * @param language - The programming language | |
| * @returns HTML string with syntax highlighting | |
| */ | |
| export function highlightCode(code: string, language: string): string { | |
| if (!code) return ''; | |
| try { | |
| const lang = language.toLowerCase(); | |
| const isSupported = hljs.getLanguage(lang); | |
| if (isSupported) { | |
| return hljs.highlight(code, { language: lang }).value; | |
| } else { | |
| return hljs.highlightAuto(code).value; | |
| } | |
| } catch { | |
| // Fallback to escaped plain text | |
| return code | |
| .replace(AMPERSAND_REGEX, '&') | |
| .replace(LT_REGEX, '<') | |
| .replace(GT_REGEX, '>'); | |
| } | |
| } | |
| /** | |
| * Detects if markdown ends with an incomplete code block (opened but not closed). | |
| * Returns the code block info if found, null otherwise. | |
| * @param markdown - The raw markdown string to check | |
| * @returns IncompleteCodeBlock info or null | |
| */ | |
| export function detectIncompleteCodeBlock(markdown: string): IncompleteCodeBlock | null { | |
| // Count all code fences in the markdown | |
| // A code block is incomplete if there's an odd number of ``` fences | |
| const fencePattern = new RegExp(FENCE_PATTERN.source, FENCE_PATTERN.flags); | |
| const fences: number[] = []; | |
| let fenceMatch; | |
| while ((fenceMatch = fencePattern.exec(markdown)) !== null) { | |
| // Store the position after the ``` | |
| const pos = fenceMatch[0].startsWith(NEWLINE) ? fenceMatch.index + 1 : fenceMatch.index; | |
| fences.push(pos); | |
| } | |
| // If even number of fences (including 0), all code blocks are closed | |
| if (fences.length % 2 === 0) { | |
| return null; | |
| } | |
| // Odd number means last code block is incomplete | |
| // The last fence is the opening of the incomplete block | |
| const openingIndex = fences[fences.length - 1]; | |
| const afterOpening = markdown.slice(openingIndex + 3); | |
| // Extract language and code content | |
| const langMatch = afterOpening.match(LANG_PATTERN); | |
| const language = langMatch?.[1] || DEFAULT_LANGUAGE; | |
| const codeStartIndex = openingIndex + 3 + (langMatch?.[0]?.length ?? 0); | |
| const code = markdown.slice(codeStartIndex); | |
| return { | |
| language, | |
| code, | |
| openingIndex | |
| }; | |
| } | |