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 inline uint64_t util_logbase2_64(uint64_t n) { | |
| return ((sizeof(uint64_t) * 8 - 1) - __builtin_clzll(n | 1)); | |
| uint64_t pos = 0ull; | |
| if (n >= 1ull << 32) { | |
| n >>= 32; | |
| pos += 32; | |
| } | |
| if (n >= 1ull << 16) { | |
| n >>= 16; | |
| pos += 16; | |
| } | |
| if (n >= 1ull << 8) { | |
| n >>= 8; | |
| pos += 8; | |
| } | |
| if (n >= 1ull << 4) { | |
| n >>= 4; | |
| pos += 4; | |
| } | |
| if (n >= 1ull << 2) { | |
| n >>= 2; | |
| pos += 2; | |
| } | |
| if (n >= 1ull << 1) { | |
| pos += 1; | |
| } | |
| return pos; | |
| } | |
| void util_sparse_array_init(util_sparse_array * arr, size_t elem_size, size_t node_size) { | |
| memset(arr, 0, sizeof(*arr)); | |
| arr->elem_size = elem_size; | |
| arr->node_size_log2 = util_logbase2_64(node_size); | |
| assert(node_size >= 2 && node_size == (1ull << arr->node_size_log2)); | |
| } | |
| static inline void * os_malloc_aligned(size_t size, size_t alignment) { | |
| void * ptr; | |
| alignment = (alignment + sizeof(void *) - 1) & ~(sizeof(void *) - 1); | |
| if (posix_memalign(&ptr, alignment, size) != 0) { | |
| return NULL; | |
| } | |
| return ptr; | |
| } | |
| static inline void * _util_sparse_array_node_data(uintptr_t handle) { | |
| return (void *) (handle & NODE_PTR_MASK); | |
| } | |
| static inline unsigned _util_sparse_array_node_level(uintptr_t handle) { | |
| return handle & NODE_LEVEL_MASK; | |
| } | |
| static inline void _util_sparse_array_node_finish(util_sparse_array * arr, uintptr_t node) { | |
| if (_util_sparse_array_node_level(node) > 0) { | |
| uintptr_t * children = (uintptr_t *) _util_sparse_array_node_data(node); | |
| size_t node_size = 1ull << arr->node_size_log2; | |
| for (size_t i = 0; i < node_size; i++) { | |
| if (children[i]) { | |
| _util_sparse_array_node_finish(arr, children[i]); | |
| } | |
| } | |
| } | |
| os_free_aligned(_util_sparse_array_node_data(node)); | |
| } | |
| static inline uintptr_t _util_sparse_array_node(void * data, unsigned level) { | |
| assert(data != NULL); | |
| assert(((uintptr_t) data & NODE_LEVEL_MASK) == 0); | |
| assert((level & NODE_PTR_MASK) == 0); | |
| return (uintptr_t) data | level; | |
| } | |
| inline uintptr_t _util_sparse_array_node_alloc(util_sparse_array * arr, unsigned level) { | |
| size_t size; | |
| if (level == 0) { | |
| size = arr->elem_size << arr->node_size_log2; | |
| } else { | |
| size = sizeof(uintptr_t) << arr->node_size_log2; | |
| } | |
| void * data = os_malloc_aligned(size, NODE_ALLOC_ALIGN); | |
| memset(data, 0, size); | |
| return _util_sparse_array_node(data, level); | |
| } | |
| static inline uintptr_t _util_sparse_array_set_or_free_node(uintptr_t * node_ptr, uintptr_t cmp_node, uintptr_t node) { | |
| uintptr_t prev_node = p_atomic_cmpxchg(node_ptr, cmp_node, node); | |
| if (prev_node != cmp_node) { | |
| /* We lost the race. Free this one and return the one that was already | |
| * allocated. | |
| */ | |
| os_free_aligned(_util_sparse_array_node_data(node)); | |
| return prev_node; | |
| } else { | |
| return node; | |
| } | |
| } | |
| void * util_sparse_array_get(util_sparse_array * arr, uint64_t idx) { | |
| const unsigned node_size_log2 = arr->node_size_log2; | |
| uintptr_t root = p_atomic_read(&arr->root); | |
| if (unlikely(!root)) { | |
| unsigned root_level = 0; | |
| uint64_t idx_iter = idx >> node_size_log2; | |
| while (idx_iter) { | |
| idx_iter >>= node_size_log2; | |
| root_level++; | |
| } | |
| uintptr_t new_root = _util_sparse_array_node_alloc(arr, root_level); | |
| root = _util_sparse_array_set_or_free_node(&arr->root, NULL_NODE, new_root); | |
| } | |
| while (1) { | |
| unsigned root_level = _util_sparse_array_node_level(root); | |
| uint64_t root_idx = idx >> (root_level * node_size_log2); | |
| if (likely(root_idx < (1ull << node_size_log2))) { | |
| break; | |
| } | |
| /* In this case, we have a root but its level is low enough that the | |
| * requested index is out-of-bounds. | |
| */ | |
| uintptr_t new_root = _util_sparse_array_node_alloc(arr, root_level + 1); | |
| uintptr_t * new_root_children = (uintptr_t *) _util_sparse_array_node_data(new_root); | |
| new_root_children[0] = root; | |
| /* We only add one at a time instead of the whole tree because it's | |
| * easier to ensure correctness of both the tree building and the | |
| * clean-up path. Because we're only adding one node we never have to | |
| * worry about trying to free multiple things without freeing the old | |
| * things. | |
| */ | |
| root = _util_sparse_array_set_or_free_node(&arr->root, root, new_root); | |
| } | |
| void * node_data = _util_sparse_array_node_data(root); | |
| unsigned node_level = _util_sparse_array_node_level(root); | |
| while (node_level > 0) { | |
| uint64_t child_idx = (idx >> (node_level * node_size_log2)) & ((1ull << node_size_log2) - 1); | |
| uintptr_t * children = (uintptr_t *) node_data; | |
| uintptr_t child = p_atomic_read(&children[child_idx]); | |
| if (unlikely(!child)) { | |
| child = _util_sparse_array_node_alloc(arr, node_level - 1); | |
| child = _util_sparse_array_set_or_free_node(&children[child_idx], NULL_NODE, child); | |
| } | |
| node_data = _util_sparse_array_node_data(child); | |
| node_level = _util_sparse_array_node_level(child); | |
| } | |
| uint64_t elem_idx = idx & ((1ull << node_size_log2) - 1); | |
| return (void *) ((char *) node_data + (elem_idx * arr->elem_size)); | |
| } | |