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
| // clang-format off | |
| // clang-format on | |
| // Will be defined include/drm/virtgpu_drm.h when | |
| // https://gitlab.freedesktop.org/virgl/virglrenderer/-/merge_requests/1590/diffs | |
| // is merged | |
| // Mesa/Virlgrenderer Venus internal. Only necessary during the | |
| // Venus->APIR transition in Virglrenderer | |
| typedef uint32_t virgl_renderer_capset; | |
| /* from src/virtio/vulkan/vn_renderer_virtgpu.c */ | |
| enum virt_gpu_result_t { | |
| APIR_SUCCESS = 0, | |
| APIR_ERROR_INITIALIZATION_FAILED = -1, | |
| }; | |
| struct virtgpu { | |
| bool use_apir_capset; | |
| int fd; | |
| struct { | |
| virgl_renderer_capset id; | |
| uint32_t version; | |
| virgl_renderer_capset_apir data; | |
| } capset; | |
| util_sparse_array shmem_array; | |
| /* APIR communication pages */ | |
| virtgpu_shmem reply_shmem; | |
| virtgpu_shmem data_shmem; | |
| /* Mutex to protect shared data_shmem buffer from concurrent access */ | |
| mtx_t data_shmem_mutex; | |
| /* Cached device information to prevent memory leaks and race conditions */ | |
| struct { | |
| char * description; | |
| char * name; | |
| int32_t device_count; | |
| uint32_t type; | |
| size_t memory_free; | |
| size_t memory_total; | |
| } cached_device_info; | |
| /* Cached buffer type information to prevent memory leaks and race conditions */ | |
| struct { | |
| apir_buffer_type_host_handle_t host_handle; | |
| char * name; | |
| size_t alignment; | |
| size_t max_size; | |
| } cached_buffer_type; | |
| }; | |
| static inline int virtgpu_ioctl(virtgpu * gpu, unsigned long request, void * args) { | |
| return drmIoctl(gpu->fd, request, args); | |
| } | |
| virtgpu * create_virtgpu(); | |
| apir_encoder * remote_call_prepare(virtgpu * gpu, ApirCommandType apir_cmd_type, int32_t cmd_flags); | |
| uint32_t remote_call(virtgpu * gpu, | |
| apir_encoder * enc, | |
| apir_decoder ** dec, | |
| float max_wait_ms, | |
| long long * call_duration_ns); | |
| void remote_call_finish(virtgpu * gpu, apir_encoder * enc, apir_decoder * dec); | |