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
| #version 450 | |
| #include "types.glsl" | |
| #include "generic_unary_head.glsl" | |
| // workgroup does 32x32 tile, but uses 32x8 threads | |
| #define TILE_DIM 32 | |
| layout(local_size_x = 32, local_size_y = 8, local_size_z = 1) in; | |
| shared uint sh[TILE_DIM][TILE_DIM + 1]; | |
| void iter(uvec3 wg_id) { | |
| const uint tile_col = wg_id.x; | |
| const uint tile_row = wg_id.y; | |
| const uint tid_col = gl_LocalInvocationID.x; | |
| const uint tid_row = gl_LocalInvocationID.y; | |
| const uint i2 = wg_id.z % p.ne12; | |
| const uint i3 = wg_id.z / p.ne12; | |
| const uint i02 = i2; | |
| const uint i03 = i3; | |
| // The workgroup does TILE_DIM x TILE_DIM, but swaps the LSBs of the | |
| // src coords to make memory accesses contiguous, dst has tid.x in i0, | |
| // src has tid.x in i01 | |
| [[unroll]] for (uint y = 0; y < 4; ++y) { | |
| const uint i00 = tile_col * TILE_DIM + tid_row + 8 * y; | |
| const uint i01 = tile_row * TILE_DIM + tid_col; | |
| if (i00 < p.ne00 && i01 < p.ne01 && i02 < p.ne02 && i03 < p.ne03) { | |
| const uint src_idx = i00 * p.nb00 + i01 * p.nb01 + i02 * p.nb02 + i03 * p.nb03; | |
| sh[tid_row + 8 * y][tid_col] = uint(data_a[get_aoffset() + src_idx]); | |
| } | |
| } | |
| barrier(); | |
| [[unroll]] for (uint y = 0; y < 4; ++y) { | |
| const uint i0 = tile_col * TILE_DIM + tid_col; | |
| const uint i1 = tile_row * TILE_DIM + tid_row + 8 * y; | |
| if (i0 < p.ne10 && i1 < p.ne11 && i2 < p.ne12 && i3 < p.ne13) { | |
| const uint dst_idx = i0 * p.nb10 + i1 * p.nb11 + i2 * p.nb12 + i3 * p.nb13; | |
| // load transposed | |
| data_d[get_doffset() + dst_idx] = D_TYPE(sh[tid_col][tid_row + 8 * y]); | |
| } | |
| } | |
| } | |
| #define CEIL_DIV(a, b) (((a) + (b) - 1) / (b)) | |
| void main() { | |
| uint z = gl_WorkGroupID.z; | |
| uint y = gl_WorkGroupID.y; | |
| bool need_barrier = false; | |
| for (uint z = gl_WorkGroupID.z; z < p.ne12 * p.ne13; z += gl_NumWorkGroups.z) { | |
| for (uint y = gl_WorkGroupID.y; y < CEIL_DIV(p.ne11, TILE_DIM); y += gl_NumWorkGroups.y) { | |
| for (uint x = gl_WorkGroupID.x; x < CEIL_DIV(p.ne10, TILE_DIM); x += gl_NumWorkGroups.x) { | |
| if (need_barrier) { | |
| barrier(); | |
| } | |
| need_barrier = true; | |
| iter(uvec3(x, y, z)); | |
| } | |
| } | |
| } | |
| } | |