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
| // Simplified API for asynchronous data loading. | |
| static __device__ __forceinline__ unsigned int ggml_cuda_cvta_generic_to_shared(void * generic_ptr) { | |
| return __cvta_generic_to_shared(generic_ptr); | |
| GGML_UNUSED(generic_ptr); | |
| NO_DEVICE_CODE; | |
| return 0; | |
| } | |
| // Copies data from global to shared memory, cg == cache global. | |
| // Both the src and dst pointers must be aligned to 16 bit. | |
| // Shared memory uses 32 bit addressing, the pointer is passed as unsigned int. | |
| // Generic pointers can be converted to 32 bit shared memory pointers using __cvta_generic_to_shared. | |
| // Only the 16 bit copy is exposed because 4 and 8 bit copies did not yield performance improvements. | |
| template <int preload> | |
| static __device__ __forceinline__ void cp_async_cg_16(const unsigned int dst, const void * src) { | |
| static_assert(preload == 0 || preload == 64 || preload == 128 || preload == 256, "bad preload"); | |
| if (preload == 256) { | |
| asm volatile("cp.async.cg.shared.global.L2::256B [%0], [%1], 16;" | |
| : : "r"(dst), "l"(src)); | |
| } else if (preload == 128) { | |
| asm volatile("cp.async.cg.shared.global.L2::128B [%0], [%1], 16;" | |
| : : "r"(dst), "l"(src)); | |
| } else if (preload == 64) { | |
| asm volatile("cp.async.cg.shared.global.L2::64B [%0], [%1], 16;" | |
| : : "r"(dst), "l"(src)); | |
| } else | |
| { | |
| asm volatile("cp.async.cg.shared.global [%0], [%1], 16;" | |
| : : "r"(dst), "l"(src)); | |
| } | |
| GGML_UNUSED(dst); | |
| GGML_UNUSED(src); | |
| NO_DEVICE_CODE; | |
| } | |
| // Makes each thread wait until its asynchronous data copies are done. | |
| // This does NOT provide any additional synchronization. | |
| // In particular, when copying data with multiple warps a call to __syncthreads will be needed. | |
| static __device__ __forceinline__ void cp_async_wait_all() { | |
| asm volatile("cp.async.wait_all;"); | |
| NO_DEVICE_CODE; | |
| } | |