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
File size: 2,381 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 | import hljs from 'highlight.js';
import {
NEWLINE,
DEFAULT_LANGUAGE,
LANG_PATTERN,
AMPERSAND_REGEX,
LT_REGEX,
GT_REGEX,
FENCE_PATTERN
} from '$lib/constants';
export interface IncompleteCodeBlock {
language: string;
code: string;
openingIndex: number;
}
/**
* Highlights code using highlight.js
* @param code - The code to highlight
* @param language - The programming language
* @returns HTML string with syntax highlighting
*/
export function highlightCode(code: string, language: string): string {
if (!code) return '';
try {
const lang = language.toLowerCase();
const isSupported = hljs.getLanguage(lang);
if (isSupported) {
return hljs.highlight(code, { language: lang }).value;
} else {
return hljs.highlightAuto(code).value;
}
} catch {
// Fallback to escaped plain text
return code
.replace(AMPERSAND_REGEX, '&')
.replace(LT_REGEX, '<')
.replace(GT_REGEX, '>');
}
}
/**
* Detects if markdown ends with an incomplete code block (opened but not closed).
* Returns the code block info if found, null otherwise.
* @param markdown - The raw markdown string to check
* @returns IncompleteCodeBlock info or null
*/
export function detectIncompleteCodeBlock(markdown: string): IncompleteCodeBlock | null {
// Count all code fences in the markdown
// A code block is incomplete if there's an odd number of ``` fences
const fencePattern = new RegExp(FENCE_PATTERN.source, FENCE_PATTERN.flags);
const fences: number[] = [];
let fenceMatch;
while ((fenceMatch = fencePattern.exec(markdown)) !== null) {
// Store the position after the ```
const pos = fenceMatch[0].startsWith(NEWLINE) ? fenceMatch.index + 1 : fenceMatch.index;
fences.push(pos);
}
// If even number of fences (including 0), all code blocks are closed
if (fences.length % 2 === 0) {
return null;
}
// Odd number means last code block is incomplete
// The last fence is the opening of the incomplete block
const openingIndex = fences[fences.length - 1];
const afterOpening = markdown.slice(openingIndex + 3);
// Extract language and code content
const langMatch = afterOpening.match(LANG_PATTERN);
const language = langMatch?.[1] || DEFAULT_LANGUAGE;
const codeStartIndex = openingIndex + 3 + (langMatch?.[0]?.length ?? 0);
const code = markdown.slice(codeStartIndex);
return {
language,
code,
openingIndex
};
}
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