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 | |
| // ggml_tensor is serialized into apir_rpc_tensor | |
| struct apir_rpc_tensor { | |
| uint64_t id; | |
| uint32_t type; | |
| uint64_t buffer; | |
| uint32_t ne[GGML_MAX_DIMS]; | |
| uint32_t nb[GGML_MAX_DIMS]; | |
| uint32_t op; | |
| int32_t op_params[GGML_MAX_OP_PARAMS / sizeof(int32_t)]; | |
| int32_t flags; | |
| uint64_t src[GGML_MAX_SRC]; | |
| uint64_t view_src; | |
| uint64_t view_offs; | |
| uint64_t data; | |
| char name[GGML_MAX_NAME]; | |
| char padding[4]; | |
| }; | |
| /* frontend */ | |
| apir_rpc_tensor apir_serialize_tensor(const ggml_tensor * tensor); | |
| void apir_serialize_graph(const ggml_cgraph * cgraph, std::vector<uint8_t> & output); | |
| /* backend */ | |
| void apir_track_backend_buffer(ggml_backend_buffer_t buffer); | |
| bool apir_untrack_backend_buffer(ggml_backend_buffer_t buffer); | |
| std::unordered_set<ggml_backend_buffer_t> apir_get_track_backend_buffers(); | |
| void apir_add_tensor(ggml_tensor * tensor, | |
| std::vector<apir_rpc_tensor> & tensors, | |
| std::unordered_set<ggml_tensor *> & visited); | |
| ggml_tensor * apir_deserialize_tensor(ggml_context * ctx, const apir_rpc_tensor * tensor); | |
| ggml_tensor * apir_create_node(uint64_t id, | |
| ggml_context * ctx, | |
| const std::unordered_map<uint64_t, const apir_rpc_tensor *> & tensor_ptrs, | |
| std::unordered_map<uint64_t, ggml_tensor *> & tensor_map); | |
| ggml_cgraph * apir_deserialize_graph(uint32_t n_nodes, | |
| uint32_t n_tensors, | |
| const apir_rpc_tensor * tensors, | |
| const uint64_t * nodes); | |