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 "dequant_head.glsl" | |
| layout(local_size_x = 64, local_size_y = 1, local_size_z = 1) in; | |
| layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; | |
| layout (binding = 1) writeonly buffer D {D_TYPE data_b[];}; | |
| void main() { | |
| [[unroll]] for (uint wgy = 0; wgy < 256; wgy++) { | |
| const uint ib = gl_WorkGroupID.x * 256 + wgy; | |
| if (ib >= p.nel / QUANT_K) { | |
| return; | |
| } | |
| const uint tid = gl_LocalInvocationID.x; | |
| const uint il = tid / 16; | |
| const uint ir = tid % 16; | |
| const uint is = 2 * il; | |
| const FLOAT_TYPE dall = FLOAT_TYPE(data_a[ib].dm.x); | |
| const FLOAT_TYPE dmin = FLOAT_TYPE(data_a[ib].dm.y); | |
| const uint y_idx = ib * QUANT_K + 64 * il + 2 * ir; | |
| const uint qs_idx = 32*il + 2 * ir; | |
| const uint qh_idx = 2 * ir; | |
| uint scidx0 = (is < 4) ? is : (is + 4); | |
| uint scidx1 = (is < 4) ? is : (is - 4); | |
| uint scidxmask1 = (is < 4) ? 0x30 : 0xC0; | |
| uint scidxshift1 = (is < 4) ? 0 : 2; | |
| uint mbidx0 = is + 4; | |
| uint mbidx1 = (is < 4) ? is + 4 : is; | |
| uint mbidxmask0 = (is < 4) ? 0xF : 0xF0; | |
| uint mbidxshift0 = (is < 4) ? 0 : 4; | |
| uint mbidxmask1 = (is < 4) ? 0x30 : 0xC0; | |
| uint mbidxshift1 = (is < 4) ? 0 : 2; | |
| uint8_t sc = uint8_t((data_a[ib].scales[scidx0] & 0xF) | ((data_a[ib].scales[scidx1] & scidxmask1) >> scidxshift1)); | |
| uint8_t mbyte = uint8_t((data_a[ib].scales[mbidx0] & mbidxmask0) >> mbidxshift0 | ((data_a[ib].scales[mbidx1] & mbidxmask1) >> mbidxshift1)); | |
| const FLOAT_TYPE d1 = dall * sc; | |
| const FLOAT_TYPE m1 = dmin * mbyte; | |
| scidx0 = (is < 4) ? is + 1 : (is + 5); | |
| scidx1 = (is < 4) ? is + 1 : (is - 3); | |
| scidxmask1 = (is < 4) ? 0x30 : 0xC0; | |
| scidxshift1 = (is < 4) ? 0 : 2; | |
| mbidx0 = is + 5; | |
| mbidx1 = (is < 4) ? is + 5 : is + 1; | |
| mbidxmask0 = (is < 4) ? 0xF : 0xF0; | |
| mbidxshift0 = (is < 4) ? 0 : 4; | |
| mbidxmask1 = (is < 4) ? 0x30 : 0xC0; | |
| mbidxshift1 = (is < 4) ? 0 : 2; | |
| sc = uint8_t((data_a[ib].scales[scidx0] & 0xF) | ((data_a[ib].scales[scidx1] & scidxmask1) >> scidxshift1)); | |
| mbyte = uint8_t((data_a[ib].scales[mbidx0] & mbidxmask0) >> mbidxshift0 | ((data_a[ib].scales[mbidx1] & mbidxmask1) >> mbidxshift1)); | |
| const FLOAT_TYPE d2 = dall * sc; | |
| const FLOAT_TYPE m2 = dmin * mbyte; | |
| const uint8_t hm1 = uint8_t(1 << (2 * il )); | |
| const uint8_t hm2 = uint8_t(1 << (2 * il + 1)); | |
| data_b[y_idx ] = D_TYPE(d1 * FLOAT_TYPE((data_a[ib].qs[qs_idx ] & 0xF) + (((data_a[ib].qh[qh_idx ] & hm1) != 0) ? 16 : 0)) - m1); | |
| data_b[y_idx + 1] = D_TYPE(d1 * FLOAT_TYPE((data_a[ib].qs[qs_idx + 1] & 0xF) + (((data_a[ib].qh[qh_idx + 1] & hm1) != 0) ? 16 : 0)) - m1); | |
| data_b[y_idx + 32] = D_TYPE(d2 * FLOAT_TYPE((data_a[ib].qs[qs_idx ] >> 4) + (((data_a[ib].qh[qh_idx ] & hm2) != 0) ? 16 : 0)) - m2); | |
| data_b[y_idx + 33] = D_TYPE(d2 * FLOAT_TYPE((data_a[ib].qs[qs_idx + 1] >> 4) + (((data_a[ib].qh[qh_idx + 1] & hm2) != 0) ? 16 : 0)) - m2); | |
| } | |
| } | |