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
| static uint32_t virtgpu_ioctl_resource_create_blob(virtgpu * gpu, | |
| uint32_t blob_mem, | |
| uint32_t blob_flags, | |
| size_t blob_size, | |
| uint64_t blob_id, | |
| uint32_t * res_id) { | |
| blob_size = align64(blob_size, 4096); | |
| drm_virtgpu_resource_create_blob args = { | |
| .blob_mem = blob_mem, | |
| .blob_flags = blob_flags, | |
| .bo_handle = 0, | |
| .res_handle = 0, | |
| .size = blob_size, | |
| .pad = 0, | |
| .cmd_size = 0, | |
| .cmd = 0, | |
| .blob_id = blob_id, | |
| }; | |
| if (virtgpu_ioctl(gpu, DRM_IOCTL_VIRTGPU_RESOURCE_CREATE_BLOB, &args)) { | |
| return 0; | |
| } | |
| *res_id = args.res_handle; | |
| return args.bo_handle; | |
| } | |
| static void virtgpu_ioctl_gem_close(virtgpu * gpu, uint32_t gem_handle) { | |
| drm_gem_close args = { | |
| .handle = gem_handle, | |
| .pad = 0, | |
| }; | |
| const int ret = virtgpu_ioctl(gpu, DRM_IOCTL_GEM_CLOSE, &args); | |
| assert(!ret); | |
| UNUSED(ret); | |
| } | |
| static void * virtgpu_ioctl_map(virtgpu * gpu, uint32_t gem_handle, size_t size) { | |
| drm_virtgpu_map args = { | |
| .offset = 0, | |
| .handle = gem_handle, | |
| .pad = 0, | |
| }; | |
| if (virtgpu_ioctl(gpu, DRM_IOCTL_VIRTGPU_MAP, &args)) { | |
| return NULL; | |
| } | |
| void * ptr = mmap(NULL, size, PROT_READ | PROT_WRITE, MAP_SHARED, gpu->fd, args.offset); | |
| if (ptr == MAP_FAILED) { | |
| return NULL; | |
| } | |
| return ptr; | |
| } | |
| void virtgpu_shmem_destroy(virtgpu * gpu, virtgpu_shmem * shmem) { | |
| munmap(shmem->mmap_ptr, shmem->mmap_size); | |
| virtgpu_ioctl_gem_close(gpu, shmem->gem_handle); | |
| } | |
| int virtgpu_shmem_create(virtgpu * gpu, size_t size, virtgpu_shmem * shmem) { | |
| size = align64(size, 16384); | |
| uint32_t res_id; | |
| uint32_t gem_handle = virtgpu_ioctl_resource_create_blob(gpu, VIRTGPU_BLOB_MEM_HOST3D, | |
| VIRTGPU_BLOB_FLAG_USE_MAPPABLE, size, 0, &res_id); | |
| if (!gem_handle) { | |
| return 1; | |
| } | |
| void * ptr = virtgpu_ioctl_map(gpu, gem_handle, size); | |
| if (!ptr) { | |
| virtgpu_ioctl_gem_close(gpu, gem_handle); | |
| GGML_LOG_ERROR(GGML_VIRTGPU "%s: virtgpu_ioctl_map failed\n", __func__); | |
| return 1; | |
| } | |
| shmem->res_id = res_id; | |
| shmem->mmap_size = size; | |
| shmem->mmap_ptr = ptr; | |
| shmem->gem_handle = gem_handle; | |
| return 0; | |
| } | |