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.
- 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
- 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
- Atomic Chat
File size: 3,280 Bytes
15c3607 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 | 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);
});
}
}
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