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
| # llama.cpp for IBM zDNN Accelerator | |
| > [!WARNING] | |
| > **Note:** zDNN is **not** the same as ZenDNN. | |
| > - **zDNN** (this page): IBM's Deep Neural Network acceleration library for IBM Z & LinuxONE Mainframes | |
| > - **ZenDNN**: AMD's deep learning library for AMD EPYC CPUs ([see ZenDNN documentation](ZenDNN.md)) | |
| ## Background | |
| IBM zDNN (Z Deep Neural Network) is a hardware acceleration library designed specifically to leverage the IBM NNPA (Neural Network Processor Assist) accelerator located within IBM Telum I and II processors. It provides significant performance improvements for neural network inference operations. | |
| ### Llama.cpp + IBM zDNN | |
| The llama.cpp zDNN backend is designed to enable llama.cpp on IBM z17 and later systems via the IBM zDNN hardware acceleration library. | |
| ## Software & Hardware Support | |
| | Hardware Level | Status | Verified | | |
| | -------------------- | ------------- | -------------------------- | | |
| | IBM z17 / LinuxONE 5 | Supported | RHEL 9.6, IBM z17, 40 IFLs | | |
| | IBM z16 / LinuxONE 4 | Not Supported | | | |
| ## Data Types Supported | |
| | Data Type | Status | | |
| | --------- | --------- | | |
| | F32 | Supported | | |
| | F16 | Supported | | |
| | BF16 | Supported | | |
| ## CMake Options | |
| The IBM zDNN backend has the following CMake options that control the behaviour of the backend. | |
| | CMake Option | Default Value | Description | | |
| | ------------ | ------------- | ----------------------------------- | | |
| | `GGML_ZDNN` | `OFF` | Compile llama.cpp with zDNN support | | |
| | `ZDNN_ROOT` | `""` | Override zDNN library lookup | | |
| ## 1. Install zDNN Library | |
| Note: Using the zDNN library provided via `apt` or `yum` may not work correctly as reported in [#15772](https://github.com/ggml-org/llama.cpp/issues/15772). It is preferred that you compile from source. | |
| ```sh | |
| git clone --recurse-submodules https://github.com/IBM/zDNN | |
| cd zDNN | |
| autoreconf . | |
| ./configure --prefix=/opt/zdnn-libs | |
| make build | |
| sudo make install | |
| ``` | |
| ## 2. Build llama.cpp | |
| ```sh | |
| git clone https://github.com/ggml-org/llama.cpp | |
| cd llama.cpp | |
| cmake -S . -G Ninja -B build \ | |
| -DCMAKE_BUILD_TYPE=Release \ | |
| -DGGML_ZDNN=ON \ | |
| -DZDNN_ROOT=/opt/zdnn-libs | |
| cmake --build build --config Release -j$(nproc) | |
| ``` | |