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(local_size_x = 256, local_size_y = 1, local_size_z = 1) in; | |
| layout (binding = 0) readonly buffer A {block_iq1_m data_a[];}; | |
| layout (binding = 1) writeonly buffer D {D_TYPE data_b[];}; | |
| void main() { | |
| // Each thread handles 1 subblock (32 values with 2 scales) | |
| const uint ib = gl_WorkGroupID.x * 32 + gl_LocalInvocationID.x / 8; | |
| init_iq_shmem(gl_WorkGroupSize); | |
| if (ib >= p.nel / 256) { | |
| return; | |
| } | |
| const uint ib32 = gl_LocalInvocationID.x % 8; | |
| const uint ib64 = ib32 / 2; | |
| const uint b_idx = 256 * ib + 32 * ib32; | |
| const uint16_t[4] scales = data_a[ib].scales; | |
| const u16vec4 s = u16vec4(scales[0], scales[1], scales[2], scales[3]) >> 12; | |
| const float d = float(unpackHalf2x16(s.x | (s.y << 4) | (s.z << 8) | (s.w << 12)).x); | |
| const uint sc = data_a[ib].scales[ib64]; | |
| [[unroll]] for (int l = 0; l < 4; ++l) { | |
| const uint ib16 = 2 * ib32 + l / 2; | |
| const float dl = d * (2 * bitfieldExtract(sc, 3 * int(ib16 & 3), 3) + 1); | |
| const uint qh = data_a[ib].qh[ib16] >> (4 * (l & 1)); | |
| const uint qs = data_a[ib].qs[4 * ib32 + l]; | |
| const float delta = ((qh & 8) != 0) ? -IQ1M_DELTA : IQ1M_DELTA; | |
| const int16_t grid = int16_t(iq1s_grid[qs | ((qh & 7) << 8)]); | |
| [[unroll]] for (int j = 0; j < 8; ++j) { | |
| data_b[b_idx + 8 * l + j] = D_TYPE(dl * (bitfieldExtract(grid, 2*j, 2) + delta)); | |
| } | |
| } | |
| } | |