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
| namespace spacemit_kernels { | |
| constexpr auto div_round_up(auto up, auto down) { | |
| return (up + down - 1) / down; | |
| } | |
| // Q8 Blk [f32] [s16] [int8 * blk_len] | |
| // Q8 Blk N [f32 * N] [s16 * N] [int8 * blk_len * N] | |
| constexpr size_t q8_blk_size(size_t blk_len, bool with_blk_sum = false) { | |
| const size_t blk_size = sizeof(float) + blk_len * sizeof(int8_t) + (with_blk_sum ? sizeof(int16_t) : 0); | |
| return blk_size; | |
| } | |
| // Q8 HP row block: K is split into K32 subblocks. | |
| // Each subblock stores [f32 scale] [int8 * 32], with an optional fp16 sum trailer per subblock. | |
| constexpr size_t q8_hp_blk_size(size_t blk_len, bool with_blk_sum = false, bool with_blk_scale = false) { | |
| const size_t subblk_count = div_round_up(blk_len, size_t(32)); | |
| const size_t blk_size = blk_len * sizeof(int8_t) + subblk_count * sizeof(_Float16) + | |
| (with_blk_sum ? subblk_count * sizeof(_Float16) : 0) + | |
| (with_blk_scale ? sizeof(_Float16) : 0); | |
| return blk_size; | |
| } | |
| // Q8K Blk [f32] [s16 * (blk_len / 16)] [int8 * blk_len] | |
| // Q8K Blk N [f32 * N] [s16 * (blk_len / 16) * N] [int8 * blk_len * N] | |
| constexpr size_t q8k_blk_size(size_t blk_len) { | |
| const size_t blk_size = sizeof(float) + blk_len * sizeof(int8_t) + sizeof(int16_t) * blk_len / 16; | |
| return blk_size; | |
| } | |
| using quantize_a_row_def = std::function<void(size_t, const float *, size_t, uint8_t *)>; | |
| namespace rvv { | |
| void memcpy1d(void * dst, const void * src, int64_t size); | |
| void memcpy2d(void * dst, int64_t dst_stride, const void * src, int64_t src_stride, int64_t tile_rows, int64_t size); | |
| void forward_flash_attn_ext_f16_one_chunk_vlen1024_vf16(const ggml_compute_params * params, | |
| ggml_tensor * dst, | |
| int ir0, | |
| int ir1, | |
| void * tcm_buffer, | |
| size_t tcm_buffer_size); | |
| void forward_flash_attn_ext_f16_tiled_vlen1024_vf16(const ggml_compute_params * params, | |
| ggml_tensor * dst, | |
| int ir0, | |
| int ir1, | |
| void * tcm_buffer, | |
| size_t tcm_buffer_size); | |
| void forward_rms_norm_f32(ggml_compute_params * params, ggml_tensor * op); | |
| void forward_norm_f32(ggml_compute_params * params, ggml_tensor * op); | |
| void forward_cont_with_permute(ggml_compute_params * params, ggml_tensor * op); | |
| void forward_cpy_with_permute(ggml_compute_params * params, ggml_tensor * op); | |
| template <typename T> void forward_get_rows(ggml_compute_params * params, ggml_tensor * op); | |
| template <typename T> void forward_concat(ggml_compute_params * params, ggml_tensor * op); | |
| template <ggml_op op_type, typename T> void forward_binary(ggml_compute_params * params, ggml_tensor * op); | |
| template <typename T> void forward_sum_rows(const ggml_compute_params * params, ggml_tensor * op); | |
| template <typename T> void forward_repeat_nrows(ggml_compute_params * params, ggml_tensor * op); | |
| template <typename T> void forward_repeat_dim1(ggml_compute_params * params, ggml_tensor * op); | |
| void quantize_a_row_i8(size_t blk_len, const float * a_ptr, size_t count_k, uint8_t * quant_a_ptr); | |
| void quantize_a_4row_i8(size_t blk_len, const float * a_ptr, size_t count_k, uint8_t * quant_a_ptr); | |
| void quantize_a_row_i8_hp(size_t blk_len, const float * a_ptr, size_t count_k, uint8_t * quant_a_ptr); | |
| void quantize_a_4row_i8_hp(size_t blk_len, const float * a_ptr, size_t count_k, uint8_t * quant_a_ptr); | |
| void quantize_a_row_i8k(size_t blk_len, const float * a_ptr, size_t count_k, uint8_t * quant_a_ptr); | |
| void quantize_a_4row_i8k(size_t blk_len, const float * a_ptr, size_t count_k, uint8_t * quant_a_ptr); | |
| } // namespace rvv | |
| } // namespace spacemit_kernels | |