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: 5,275 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 | 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})`;
}
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