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 { | |
| NEWLINE_SEPARATOR, | |
| SANDBOX_EMPTY_OUTPUT, | |
| SANDBOX_OUTPUT_MAX_CHARS, | |
| SANDBOX_TIMEOUT_MS_DEFAULT, | |
| SANDBOX_TIMEOUT_MS_MAX, | |
| SANDBOX_TOOL_NAME, | |
| SANDBOX_TRUNCATION_NOTICE | |
| } from '$lib/constants'; | |
| import { SANDBOX_HARNESS_HTML } from './sandbox-harness'; | |
| import type { ToolExecutionResult } from '$lib/types'; | |
| interface SandboxReply { | |
| logs?: unknown; | |
| result?: unknown; | |
| error?: unknown; | |
| } | |
| function formatReply(reply: SandboxReply): ToolExecutionResult { | |
| const lines: string[] = []; | |
| if (Array.isArray(reply.logs)) { | |
| for (const line of reply.logs) lines.push(String(line)); | |
| } | |
| if (reply.error != null) { | |
| lines.push(`Error: ${String(reply.error)}`); | |
| } else if (reply.result != null) { | |
| lines.push(`=> ${String(reply.result)}`); | |
| } | |
| let content = lines.join(NEWLINE_SEPARATOR); | |
| if (!content) content = SANDBOX_EMPTY_OUTPUT; | |
| if (content.length > SANDBOX_OUTPUT_MAX_CHARS) { | |
| content = `${content.slice(0, SANDBOX_OUTPUT_MAX_CHARS)}${NEWLINE_SEPARATOR}${SANDBOX_TRUNCATION_NOTICE}`; | |
| } | |
| return { content, isError: reply.error != null }; | |
| } | |
| export class SandboxService { | |
| /** | |
| * Execute a frontend sandbox tool call and return its output. | |
| * One disposable iframe per execution, removed on completion, | |
| * timeout or abort. Removing the iframe terminates the worker | |
| * at the browser level, so runaway code cannot outlive it. | |
| */ | |
| static executeTool( | |
| toolName: string, | |
| params: Record<string, unknown>, | |
| signal?: AbortSignal | |
| ): Promise<ToolExecutionResult> { | |
| if (toolName !== SANDBOX_TOOL_NAME) { | |
| return Promise.resolve({ content: `Unknown frontend tool: ${toolName}`, isError: true }); | |
| } | |
| const code = typeof params.code === 'string' ? params.code : ''; | |
| if (!code) { | |
| return Promise.resolve({ content: 'Missing required parameter: code', isError: true }); | |
| } | |
| const requested = Number(params.timeout_ms); | |
| const timeoutMs = | |
| Number.isFinite(requested) && requested > 0 | |
| ? Math.min(requested, SANDBOX_TIMEOUT_MS_MAX) | |
| : SANDBOX_TIMEOUT_MS_DEFAULT; | |
| return new Promise<ToolExecutionResult>((resolve, reject) => { | |
| const iframe = document.createElement('iframe'); | |
| iframe.setAttribute('sandbox', 'allow-scripts'); | |
| iframe.style.display = 'none'; | |
| iframe.srcdoc = SANDBOX_HARNESS_HTML; | |
| let settled = false; | |
| const cleanup = () => { | |
| settled = true; | |
| clearTimeout(timer); | |
| window.removeEventListener('message', onMessage); | |
| signal?.removeEventListener('abort', onAbort); | |
| iframe.remove(); | |
| }; | |
| const finish = (result: ToolExecutionResult) => { | |
| if (settled) return; | |
| cleanup(); | |
| resolve(result); | |
| }; | |
| const onAbort = () => { | |
| if (settled) return; | |
| cleanup(); | |
| reject(new DOMException('Sandbox execution aborted', 'AbortError')); | |
| }; | |
| const onMessage = (event: MessageEvent) => { | |
| if (event.source !== iframe.contentWindow) return; | |
| finish(formatReply((event.data ?? {}) as SandboxReply)); | |
| }; | |
| const timer = setTimeout( | |
| () => finish({ content: `Execution timed out after ${timeoutMs} ms`, isError: true }), | |
| timeoutMs | |
| ); | |
| window.addEventListener('message', onMessage); | |
| signal?.addEventListener('abort', onAbort); | |
| iframe.onload = () => iframe.contentWindow?.postMessage({ code }, '*'); | |
| document.body.appendChild(iframe); | |
| }); | |
| } | |
| } | |