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: 1,558 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 | /** Sentinel value returned by `indexOf` when a substring is not found. */
export const MODEL_ID_NOT_FOUND = -1;
/** Separates `<org>` from `<model>` in a model ID, e.g. `org/ModelName`. */
export const MODEL_ID_ORG_SEPARATOR = '/';
/** Separates named segments within the model path, e.g. `ModelName-7B-GGUF`. */
export const MODEL_ID_SEGMENT_SEPARATOR = '-';
/** Separates the model path from the quantization tag, e.g. `model:Q4_K_M`. */
export const MODEL_ID_QUANTIZATION_SEPARATOR = ':';
/**
* Matches a quantization/precision segment, e.g. `Q4_K_M`, `IQ4_XS`, `F16`, `BF16`, `MXFP4`.
* Case-insensitive to handle both uppercase and lowercase inputs.
*/
export const MODEL_QUANTIZATION_SEGMENT_RE =
/^(I?Q\d+(_[A-Z0-9]+)*|F\d+|BF\d+|MXFP\d+(_[A-Z0-9]+)*)$/i;
/**
* Matches prefix for custom quantization types, e.g. `UD-Q8_K_XL`.
*/
export const MODEL_CUSTOM_QUANTIZATION_PREFIX_RE = /^UD$/i;
/**
* Matches a parameter-count segment, e.g. `7B`, `1.5b`, `120M`.
*/
export const MODEL_PARAMS_RE = /^\d+(\.\d+)?[BbMmKkTt]$/;
/**
* Matches an activated-parameter-count segment, e.g. `A10B`, `a2.4b`.
* The leading `A`/`a` distinguishes it from a regular params segment.
*/
export const MODEL_ACTIVATED_PARAMS_RE = /^[Aa]\d+(\.\d+)?[BbMmKkTt]$/;
/**
* Container format segments to exclude from tags (every model uses these).
*/
export const MODEL_IGNORED_SEGMENTS = new Set(['GGUF', 'GGML']);
/**
* Matches a trailing weight file extension, e.g. `model.gguf` -> `model`.
*/
export const MODEL_WEIGHT_EXTENSION_RE = /\.(gguf|ggml)$/i;
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