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
| layout(constant_id = 0) const int BLOCK_SIZE = 1024; | |
| layout(constant_id = 1) const int WG_UNROLL_FACTOR = 2; | |
| layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in; | |
| layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; | |
| layout (binding = 1) workgroupcoherent buffer B {ivec2 tmp_idx[];}; | |
| layout (binding = 2) workgroupcoherent buffer D {int data_d[];}; | |
| layout (push_constant) uniform parameter { | |
| uint ncols; | |
| uint ncols_padded; | |
| uint ncols_padded_log2; | |
| uint nrows; | |
| uint order; | |
| uint outer_start; | |
| uint outer_end; | |
| uint inner_start; | |
| uint inner_end; | |
| } p; | |
| void argsort(bool needs_bounds_check, const uint row) { | |
| // bitonic sort | |
| int col = int(gl_GlobalInvocationID.x); | |
| col = (col % BLOCK_SIZE) + (col / BLOCK_SIZE) * BLOCK_SIZE * WG_UNROLL_FACTOR; | |
| const uint row_offset = row * p.ncols; | |
| uint idx_offset = row * p.ncols_padded; | |
| bool need_barrier = false; | |
| // initialize indices | |
| if (p.outer_start == 0 && p.inner_start == 0) { | |
| [[unroll]] for (int u = 0; u < WG_UNROLL_FACTOR; ++u) { | |
| uint c = u*BLOCK_SIZE + col; | |
| if (c < p.ncols_padded) { | |
| ivec2 v = ivec2(c, floatBitsToInt(data_a[row_offset + c])); | |
| tmp_idx[idx_offset + c] = v; | |
| } | |
| } | |
| need_barrier = true; | |
| } | |
| [[unroll]] for (uint outer_idx = p.outer_start, k = (2 << outer_idx); outer_idx < p.outer_end; k *= 2, outer_idx++) { | |
| uint inner_end = min(p.inner_end, outer_idx + 1); | |
| for (uint j = k >> (p.inner_start + 1), inner_idx = p.inner_start; inner_idx < inner_end; j /= 2, inner_idx++) { | |
| if (need_barrier) { | |
| controlBarrier(gl_ScopeWorkgroup, gl_ScopeWorkgroup, gl_StorageSemanticsBuffer, gl_SemanticsAcquireRelease); | |
| } | |
| need_barrier = true; | |
| [[unroll]] for (int u = 0; u < WG_UNROLL_FACTOR; ++u) { | |
| int c = u*BLOCK_SIZE + col; | |
| const int ixj = int(c ^ j); | |
| if (ixj < c) { | |
| continue; | |
| } | |
| int idx_0 = (c & k) == 0 ? c : ixj; | |
| int idx_1 = (c & k) == 0 ? ixj : c; | |
| ivec2 sh_idx_0 = tmp_idx[idx_offset + idx_0]; | |
| ivec2 sh_idx_1 = tmp_idx[idx_offset + idx_1]; | |
| bool idx_0_oob = needs_bounds_check ? sh_idx_0.x >= p.ncols : false; | |
| bool idx_1_oob = needs_bounds_check ? sh_idx_1.x >= p.ncols : false; | |
| if ((idx_0_oob || | |
| (!idx_1_oob && intBitsToFloat(sh_idx_0.y) > intBitsToFloat(sh_idx_1.y)))) { | |
| tmp_idx[idx_offset + idx_0] = sh_idx_1; | |
| tmp_idx[idx_offset + idx_1] = sh_idx_0; | |
| } | |
| } | |
| } | |
| } | |
| if (p.outer_end == p.ncols_padded_log2 && | |
| p.inner_end >= p.ncols_padded_log2 + 1) { | |
| controlBarrier(gl_ScopeWorkgroup, gl_ScopeWorkgroup, gl_StorageSemanticsBuffer, gl_SemanticsAcquireRelease); | |
| [[unroll]] for (int u = 0; u < WG_UNROLL_FACTOR; ++u) { | |
| uint c = u*BLOCK_SIZE + col; | |
| if (c < p.ncols) { | |
| if (p.order == ASC) { | |
| data_d[row_offset + c] = tmp_idx[idx_offset + c].x; | |
| } else { | |
| data_d[row_offset + p.ncols - c - 1] = tmp_idx[idx_offset + c].x; | |
| } | |
| } | |
| } | |
| } | |
| } | |
| void main() { | |
| if (p.ncols == p.ncols_padded) { | |
| uint row = gl_WorkGroupID.y; | |
| while (row < p.nrows) { | |
| argsort(false, row); | |
| row += gl_WorkGroupSize.y * gl_NumWorkGroups.y; | |
| } | |
| } else { | |
| uint row = gl_WorkGroupID.y; | |
| while (row < p.nrows) { | |
| argsort(true, row); | |
| row += gl_WorkGroupSize.y * gl_NumWorkGroups.y; | |
| } | |
| } | |
| } | |