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
| // | |
| // MIT license | |
| // Copyright (C) 2024 Intel Corporation | |
| // SPDX-License-Identifier: MIT | |
| // | |
| extern "C" { | |
| // backend API | |
| GGML_BACKEND_API ggml_backend_t ggml_backend_sycl_init(int device); | |
| GGML_BACKEND_API bool ggml_backend_is_sycl(ggml_backend_t backend); | |
| // devide buffer | |
| GGML_BACKEND_API ggml_backend_buffer_type_t ggml_backend_sycl_buffer_type(int device); | |
| // split tensor buffer that splits matrices by rows across multiple devices | |
| GGML_BACKEND_API ggml_backend_buffer_type_t ggml_backend_sycl_split_buffer_type(const float * tensor_split); | |
| // Tensor parallelism (--split-mode tensor): comm_init/free/allreduce_tensor | |
| // trio queried by the meta-backend via ggml_backend_reg_get_proc_address. | |
| // See typedefs in ggml/include/ggml-backend.h. Mirrors the CUDA backend's | |
| // pattern (ggml_backend_cuda_comm_*). | |
| GGML_BACKEND_API void * ggml_backend_sycl_comm_init(ggml_backend_t * backends, size_t n_backends); | |
| GGML_BACKEND_API void ggml_backend_sycl_comm_free(void * comm_ctx); | |
| GGML_BACKEND_API bool ggml_backend_sycl_comm_allreduce_tensor(void * comm_ctx, struct ggml_tensor ** tensors); | |
| // pinned host buffer for use with the CPU backend for faster copies between CPU and GPU | |
| GGML_BACKEND_API ggml_backend_buffer_type_t ggml_backend_sycl_host_buffer_type(void); | |
| GGML_BACKEND_API void ggml_backend_sycl_print_sycl_devices(void); | |
| GGML_BACKEND_API void ggml_backend_sycl_get_gpu_list(int *id_list, int max_len); | |
| GGML_BACKEND_API void ggml_backend_sycl_get_device_description(int device, | |
| char *description, | |
| size_t description_size); | |
| GGML_BACKEND_API int ggml_backend_sycl_get_device_count(); | |
| GGML_BACKEND_API void ggml_backend_sycl_get_device_memory(int device, size_t *free, size_t *total); | |
| // SYCL doesn't support registering host memory, keep here for reference | |
| // GGML_BACKEND_API bool ggml_backend_sycl_register_host_buffer(void * buffer, size_t size); | |
| // GGML_BACKEND_API void ggml_backend_sycl_unregister_host_buffer(void * buffer); | |
| GGML_BACKEND_API ggml_backend_reg_t ggml_backend_sycl_reg(void); | |
| } | |