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 { base } from '$app/paths'; | |
| import { getJsonHeaders, getAuthHeaders } from './api-headers'; | |
| import { UrlProtocol } from '$lib/enums'; | |
| import { ERROR_MESSAGES, HTTP_CODE_TO_STRING } from '$lib/constants/error'; | |
| /** | |
| * API Fetch Utilities | |
| * | |
| * Provides common fetch patterns used across services: | |
| * - Automatic JSON headers | |
| * - Error handling with proper error messages | |
| * - Base path resolution | |
| */ | |
| export interface ApiFetchOptions extends Omit<RequestInit, 'headers'> { | |
| /** | |
| * Use auth-only headers (no Content-Type). | |
| * Default: false (uses JSON headers with Content-Type: application/json) | |
| */ | |
| authOnly?: boolean; | |
| /** | |
| * Additional headers to merge with default headers. | |
| */ | |
| headers?: Record<string, string>; | |
| } | |
| /** | |
| * Fetch JSON data from an API endpoint with standard headers and error handling. | |
| * | |
| * @param path - API path (will be prefixed with base path) | |
| * @param options - Fetch options with additional authOnly flag | |
| * @returns Parsed JSON response | |
| * @throws Error with formatted message on failure | |
| * | |
| * @example | |
| * ```typescript | |
| * // GET request | |
| * const models = await apiFetch<ApiModelListResponse>('/v1/models'); | |
| * | |
| * // POST request | |
| * const result = await apiFetch<ApiResponse>('/models/load', { | |
| * method: 'POST', | |
| * body: JSON.stringify({ model: 'gpt-4' }) | |
| * }); | |
| * ``` | |
| */ | |
| export async function apiFetch<T>(path: string, options: ApiFetchOptions = {}): Promise<T> { | |
| const { authOnly = false, headers: customHeaders, ...fetchOptions } = options; | |
| const baseHeaders = authOnly ? getAuthHeaders() : getJsonHeaders(); | |
| const headers = { ...baseHeaders, ...customHeaders }; | |
| const url = | |
| path.startsWith(UrlProtocol.HTTP) || path.startsWith(UrlProtocol.HTTPS) | |
| ? path | |
| : `${base}${path}`; | |
| let response; | |
| try { | |
| response = await fetch(url, { | |
| ...fetchOptions, | |
| headers | |
| }); | |
| } catch (e) { | |
| throw new Error(beautifyNetworkError(e)); | |
| } | |
| if (!response.ok) { | |
| const errorMessage = await parseErrorMessage(response); | |
| throw new Error(errorMessage); | |
| } | |
| return response.json() as Promise<T>; | |
| } | |
| /** | |
| * Fetch with URL constructed from base URL and query parameters. | |
| * | |
| * @param basePath - Base API path | |
| * @param params - Query parameters to append | |
| * @param options - Fetch options | |
| * @returns Parsed JSON response | |
| * | |
| * @example | |
| * ```typescript | |
| * const props = await apiFetchWithParams<ApiProps>('./props', { | |
| * model: 'gpt-4', | |
| * autoload: 'false' | |
| * }); | |
| * ``` | |
| */ | |
| export async function apiFetchWithParams<T>( | |
| basePath: string, | |
| params: Record<string, string>, | |
| options: ApiFetchOptions = {} | |
| ): Promise<T> { | |
| const url = new URL(basePath, window.location.href); | |
| for (const [key, value] of Object.entries(params)) { | |
| if (value !== undefined && value !== null) { | |
| url.searchParams.set(key, value); | |
| } | |
| } | |
| const { authOnly = false, headers: customHeaders, ...fetchOptions } = options; | |
| const baseHeaders = authOnly ? getAuthHeaders() : getJsonHeaders(); | |
| const headers = { ...baseHeaders, ...customHeaders }; | |
| let response; | |
| try { | |
| response = await fetch(url.toString(), { | |
| ...fetchOptions, | |
| headers | |
| }); | |
| } catch (e) { | |
| throw new Error(beautifyNetworkError(e)); | |
| } | |
| if (!response.ok) { | |
| const errorMessage = await parseErrorMessage(response); | |
| throw new Error(errorMessage); | |
| } | |
| return response.json() as Promise<T>; | |
| } | |
| /** | |
| * POST JSON data to an API endpoint. | |
| * | |
| * @param path - API path | |
| * @param body - Request body (will be JSON stringified) | |
| * @param options - Additional fetch options | |
| * @returns Parsed JSON response | |
| */ | |
| export async function apiPost<T, B = unknown>( | |
| path: string, | |
| body: B, | |
| options: ApiFetchOptions = {} | |
| ): Promise<T> { | |
| return apiFetch<T>(path, { | |
| method: 'POST', | |
| body: JSON.stringify(body), | |
| ...options | |
| }); | |
| } | |
| /** | |
| * Parse error message from a failed response. | |
| * Tries to extract error message from JSON body, falls back to status text. | |
| */ | |
| async function parseErrorMessage(response: Response): Promise<string> { | |
| try { | |
| const errorData = await response.json(); | |
| if (errorData?.error?.message) { | |
| return errorData.error.message; | |
| } | |
| if (errorData?.error && typeof errorData.error === 'string') { | |
| return errorData.error; | |
| } | |
| if (errorData?.message) { | |
| return errorData.message; | |
| } | |
| } catch { | |
| // JSON parsing failed, use status text | |
| } | |
| const httpErrorStr = HTTP_CODE_TO_STRING[response.status]; | |
| if (httpErrorStr) { | |
| return httpErrorStr; | |
| } | |
| return `${ERROR_MESSAGES.HTTP.GENERIC}: ${response.status} ${response.statusText}`; | |
| } | |
| /** | |
| * Converts a network issue into a human-readable message. | |
| * @param throwable - The throwable raised during fetch operation | |
| * @returns Error in an human-readable format | |
| */ | |
| function beautifyNetworkError(throwable: unknown): string { | |
| let message; | |
| if (throwable instanceof Error) { | |
| message = throwable.message; | |
| if (throwable.name === 'TypeError' && message.includes('fetch')) { | |
| return ERROR_MESSAGES.NETWORK.UNREACHABLE; | |
| } | |
| } else { | |
| message = String(throwable); | |
| } | |
| if (message.includes('ECONNREFUSED')) { | |
| return ERROR_MESSAGES.NETWORK.REFUSED; | |
| } else if (message.includes('ENOTFOUND')) { | |
| return ERROR_MESSAGES.NETWORK.NXDOMAIN; | |
| } else if (message.includes('ETIMEDOUT')) { | |
| return ERROR_MESSAGES.NETWORK.TIMEOUT; | |
| } | |
| return `${ERROR_MESSAGES.NETWORK.GENERIC} (${message})`; | |
| } | |