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 { config } from '$lib/stores/settings.svelte'; | |
| import { CORS_PROXY_HEADER_PREFIX, REDACTED_HEADERS } from '$lib/constants'; | |
| import { redactValue } from './redact'; | |
| /** | |
| * Get authorization headers for API requests | |
| * Includes Bearer token if API key is configured | |
| */ | |
| export function getAuthHeaders(): Record<string, string> { | |
| const currentConfig = config(); | |
| const apiKey = currentConfig.apiKey?.toString().trim(); | |
| return apiKey ? { Authorization: `Bearer ${apiKey}` } : {}; | |
| } | |
| /** | |
| * Get standard JSON headers with optional authorization | |
| */ | |
| export function getJsonHeaders(): Record<string, string> { | |
| return { | |
| 'Content-Type': 'application/json', | |
| ...getAuthHeaders() | |
| }; | |
| } | |
| /** | |
| * Sanitize HTTP headers by redacting sensitive values. | |
| * Known sensitive headers (from REDACTED_HEADERS) and any extra headers | |
| * specified by the caller are fully redacted. Headers listed in | |
| * `partialRedactHeaders` are partially redacted, showing only the | |
| * specified number of trailing characters. | |
| * | |
| * @param headers - Headers to sanitize | |
| * @param extraRedactedHeaders - Additional header names to fully redact | |
| * @param partialRedactHeaders - Map of header name -> number of trailing chars to keep visible | |
| * @returns Object with header names as keys and (possibly redacted) values | |
| */ | |
| export function sanitizeHeaders( | |
| headers?: HeadersInit, | |
| extraRedactedHeaders?: Iterable<string>, | |
| partialRedactHeaders?: Map<string, number> | |
| ): Record<string, string> { | |
| if (!headers) { | |
| return {}; | |
| } | |
| const normalized = new Headers(headers); | |
| const sanitized: Record<string, string> = {}; | |
| const redactedHeaders = new Set( | |
| Array.from(extraRedactedHeaders ?? [], (header) => header.toLowerCase()) | |
| ); | |
| for (const [key, value] of normalized.entries()) { | |
| const normalizedKey = key.toLowerCase(); | |
| const unproxiedKey = normalizedKey.startsWith(CORS_PROXY_HEADER_PREFIX) | |
| ? normalizedKey.slice(CORS_PROXY_HEADER_PREFIX.length) | |
| : normalizedKey; | |
| const partialChars = | |
| partialRedactHeaders?.get(normalizedKey) ?? partialRedactHeaders?.get(unproxiedKey); | |
| if (partialChars !== undefined) { | |
| sanitized[key] = redactValue(value, partialChars); | |
| } else if ( | |
| REDACTED_HEADERS.has(normalizedKey) || | |
| REDACTED_HEADERS.has(unproxiedKey) || | |
| redactedHeaders.has(normalizedKey) || | |
| redactedHeaders.has(unproxiedKey) | |
| ) { | |
| sanitized[key] = redactValue(value); | |
| } else { | |
| sanitized[key] = value; | |
| } | |
| } | |
| return sanitized; | |
| } | |