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
- 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
File size: 4,813 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 | import {
MS_PER_SECOND,
SECONDS_PER_MINUTE,
SECONDS_PER_HOUR,
SHORT_DURATION_THRESHOLD,
MEDIUM_DURATION_THRESHOLD,
MAX_PREVIEW_LENGTH,
STRIP_MARKDOWN_INLINE_REGEX,
STRIP_MARKDOWN_CAPTURE_PATTERNS,
NEWLINE_SEPARATOR
} from '$lib/constants';
/**
* Formats file size in bytes to human readable format
* Supports Bytes, KB, MB, and GB
*
* @param bytes - File size in bytes (or unknown for safety)
* @returns Formatted file size string
*/
export function formatFileSize(bytes: number | unknown): string {
if (typeof bytes !== 'number') return 'Unknown';
if (bytes === 0) return '0 Bytes';
const k = 1024;
const sizes = ['Bytes', 'KB', 'MB', 'GB'];
const i = Math.floor(Math.log(bytes) / Math.log(k));
return parseFloat((bytes / Math.pow(k, i)).toFixed(2)) + ' ' + sizes[i];
}
/**
* Format parameter count to human-readable format (B, M, K)
*
* @param params - Parameter count
* @returns Human-readable parameter count
*/
export function formatParameters(params: number | unknown): string {
if (typeof params !== 'number') return 'Unknown';
if (params >= 1e9) {
return `${(params / 1e9).toFixed(2)}B`;
}
if (params >= 1e6) {
return `${(params / 1e6).toFixed(2)}M`;
}
if (params >= 1e3) {
return `${(params / 1e3).toFixed(2)}K`;
}
return params.toString();
}
/**
* Format number with locale-specific thousands separators
*
* @param num - Number to format
* @returns Human-readable number
*/
export function formatNumber(num: number | unknown): string {
if (typeof num !== 'number') return 'Unknown';
return num.toLocaleString();
}
/**
* Format JSON string with pretty printing (2-space indentation)
* Returns original string if parsing fails
*
* @param jsonString - JSON string to format
* @returns Pretty-printed JSON string or original if invalid
*/
export function formatJsonPretty(jsonString: string): string {
try {
const parsed = JSON.parse(jsonString);
return JSON.stringify(parsed, null, 2);
} catch {
return jsonString;
}
}
/**
* Format time as HH:MM:SS in 24-hour format
*
* @param date - Date object to format
* @returns Formatted time string (HH:MM:SS)
*/
export function formatTime(date: Date): string {
return date.toLocaleTimeString('en-US', {
hour12: false,
hour: '2-digit',
minute: '2-digit',
second: '2-digit'
});
}
/**
* Formats milliseconds to a human-readable time string for performance metrics.
* Examples: "4h 12min 54s", "12min 34s", "45s", "0.5s"
*
* @param ms - Time in milliseconds
* @returns Formatted time string
*/
export function formatPerformanceTime(ms: number): string {
if (ms < 0) return '0s';
const totalSeconds = ms / MS_PER_SECOND;
if (totalSeconds < SHORT_DURATION_THRESHOLD) {
return `${totalSeconds.toFixed(1)}s`;
}
if (totalSeconds < MEDIUM_DURATION_THRESHOLD) {
return `${totalSeconds.toFixed(1)}s`;
}
const hours = Math.floor(totalSeconds / SECONDS_PER_HOUR);
const minutes = Math.floor((totalSeconds % SECONDS_PER_HOUR) / SECONDS_PER_MINUTE);
const seconds = Math.floor(totalSeconds % SECONDS_PER_MINUTE);
const parts: string[] = [];
if (hours > 0) {
parts.push(`${hours}h`);
}
if (minutes > 0) {
parts.push(`${minutes}min`);
}
if (seconds > 0 || parts.length === 0) {
parts.push(`${seconds}s`);
}
return parts.join(' ');
}
/**
* Formats attachment content for API requests with consistent header style.
* Used when converting message attachments to text content parts.
*
* @param label - Type label (e.g., 'File', 'PDF File', 'MCP Prompt')
* @param name - File or attachment name
* @param content - The actual content to include
* @param extra - Optional extra info to append to name (e.g., server name for MCP)
* @returns Formatted string with header and content
*/
export function formatAttachmentText(
label: string,
name: string,
content: string,
extra?: string
): string {
const header = extra ? `${name} (${extra})` : name;
return `\n\n--- ${label}: ${header} ---\n${content}`;
}
export function formatReasoningPreview(content: string): { preview: string; overflow: number } {
if (!content) return { preview: '', overflow: 0 };
const lines = content.split(NEWLINE_SEPARATOR);
let lastLine = '';
for (let i = lines.length - 1; i >= 0; i--) {
let cleaned = lines[i].trim();
if (!cleaned) continue;
cleaned = cleaned.replace(STRIP_MARKDOWN_INLINE_REGEX, '');
for (const [pattern, replacement] of STRIP_MARKDOWN_CAPTURE_PATTERNS) {
cleaned = cleaned.replace(pattern, replacement);
}
if (cleaned.length > 0) {
lastLine = cleaned;
break;
}
}
const fullLength = lastLine.length;
const overflow = Math.max(0, fullLength - MAX_PREVIEW_LENGTH);
if (fullLength > MAX_PREVIEW_LENGTH) {
lastLine = lastLine.slice(0, MAX_PREVIEW_LENGTH) + '...';
}
return { preview: lastLine, overflow };
}
|