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
| zdnn_data_types ggml_zdnn_type_mapping(ggml_type type) { | |
| switch (type) { | |
| case GGML_TYPE_F32: | |
| return FP32; | |
| case GGML_TYPE_F16: | |
| return FP16; | |
| case GGML_TYPE_BF16: | |
| return BFLOAT; | |
| case GGML_TYPE_Q8_0: | |
| return INT8; | |
| case GGML_TYPE_I8: | |
| return INT8; | |
| case GGML_TYPE_I32: | |
| return INT32; | |
| default: | |
| GGML_ABORT("%s: fatal: unable to determine zTensor data type", | |
| __func__); | |
| break; | |
| } | |
| } | |
| void ggml_zdnn_create_tensor(zdnn_tensor_desc & pre_tfm_desc, | |
| zdnn_tensor_desc & tfm_desc, | |
| zdnn_ztensor & ztensor, | |
| const ggml_tensor * src, | |
| const int64_t * ne, | |
| const zdnn_data_layouts layout) { | |
| zdnn_init_pre_transformed_desc( | |
| layout, | |
| ggml_zdnn_type_mapping(src->type), | |
| &pre_tfm_desc, | |
| ne[3], ne[2], ne[1], ne[0] | |
| ); | |
| ZDNN_CHECK(zdnn_generate_transformed_desc(&pre_tfm_desc, &tfm_desc)); | |
| ZDNN_CHECK(zdnn_init_ztensor_with_malloc(&pre_tfm_desc, &tfm_desc, &ztensor)); | |
| } | |
| void ggml_zdnn_load_tensor(zdnn_ztensor & ztensor, void * buffer) { | |
| ZDNN_CHECK(zdnn_transform_ztensor(&ztensor, buffer)); | |
| } | |
| void ggml_zdnn_init_tensor(ggml_backend_zdnn_buffer * buffer, const ggml_tensor * tensor) { | |
| switch (tensor->op) { | |
| case GGML_OP_MUL_MAT: | |
| { | |
| zdnn_init_pre_transformed_desc( | |
| ZDNN_2D, | |
| ggml_zdnn_type_mapping(tensor->type), | |
| &buffer->pre_tfm_desc, | |
| tensor->ne[1], tensor->ne[0] | |
| ); | |
| } break; | |
| default: | |
| { | |
| // For 4D tensors, GGML uses NCHW layout. However, because zDNN | |
| // automatically transforms everything to NHWC, we will use it | |
| // directly to avoid the performance penalty changing the | |
| // layout and reshaping the tensor. | |
| zdnn_init_pre_transformed_desc( | |
| ZDNN_NHWC, | |
| ggml_zdnn_type_mapping(tensor->type), | |
| &buffer->pre_tfm_desc, | |
| tensor->ne[3], tensor->ne[2], tensor->ne[1], tensor->ne[0] | |
| ); | |
| // TODO: Consider adding a ggml check. | |
| // TODO: If tensor = 4D, use ZDNN_NCHW by default. | |
| // TODO: If tensor = 2D, use ZDNN_NHWC by default. | |
| } break; | |
| } | |
| ZDNN_CHECK(zdnn_generate_transformed_desc(&buffer->pre_tfm_desc, &buffer->tfm_desc)); | |
| ZDNN_CHECK(zdnn_init_ztensor_with_malloc(&buffer->pre_tfm_desc, &buffer->tfm_desc, &buffer->ztensor)); | |
| } | |