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
| enable f16; | |
| @group(0) @binding(0) | |
| var<storage, read_write> src: array<SRC_TYPE>; | |
| @group(0) @binding(1) | |
| var<storage, read_write> idx: array<u32>; | |
| @group(0) @binding(2) | |
| var<storage, read_write> dst: array<DST_TYPE>; | |
| @group(0) @binding(3) | |
| var<storage, read_write> error: atomic<u32>; | |
| struct Params { | |
| offset_src: u32, // in elements | |
| offset_idx: u32, // in elements | |
| offset_dst: u32, // in elements | |
| // Strides (in elements) | |
| stride_src1: u32, | |
| stride_src2: u32, | |
| stride_src3: u32, | |
| stride_idx0: u32, | |
| stride_idx1: u32, | |
| stride_idx2: u32, | |
| stride_dst1: u32, | |
| stride_dst2: u32, | |
| stride_dst3: u32, | |
| // Shape of src | |
| ne0: u32, | |
| n_rows: u32, | |
| ne2: u32, | |
| ne3: u32, | |
| // Shape of idx | |
| idx1: u32, | |
| idx2: u32, | |
| }; | |
| @group(0) @binding(PARAMS_BINDING) | |
| var<uniform> params: Params; | |
| @compute @workgroup_size(WG_SIZE) | |
| fn main(@builtin(global_invocation_id) gid: vec3<u32>) { | |
| if (gid.x >= (params.ne3 * params.ne2 * params.n_rows * params.ne0) / VEC_SIZE) { | |
| return; | |
| } | |
| let elems_per_row = params.ne0 / VEC_SIZE; | |
| var i = gid.x / elems_per_row; | |
| let i_src3 = i / (params.ne2 * params.n_rows); | |
| i = i % (params.ne2 * params.n_rows); | |
| let i_src2 = i / params.n_rows; | |
| let i_src1 = i % params.n_rows; | |
| let i_idx2 = i_src3 % params.idx2; | |
| let i_idx1 = i_src2 % params.idx1; | |
| let i_idx0 = i_src1; | |
| let idx_high = (params.offset_idx + i_idx0 * params.stride_idx0 + i_idx1 * params.stride_idx1 + i_idx2 * params.stride_idx2) * 2; | |
| let idx_val = idx[idx_high]; | |
| let idx_low_val = idx[idx_high + 1]; | |
| if (idx_low_val != 0) { | |
| // Upper bits of index are not zero, output will be incorrect | |
| atomicStore(&error, 1); | |
| return; | |
| } | |
| let idx_i = params.offset_idx + i_idx0 * params.stride_idx0 + i_idx1 * params.stride_idx1 + i_idx2 * params.stride_idx2; | |
| let idx_val = idx[idx_i]; | |
| let i_dst_row = params.offset_dst + idx_val * params.stride_dst1 + i_src2 * params.stride_dst2 + i_src3 * params.stride_dst3; | |
| let i_src_row = params.offset_src + i_src1 * params.stride_src1 + i_src2 * params.stride_src2 + i_src3 * params.stride_src3; | |
| let col_idx = gid.x % elems_per_row; | |
| dst[i_dst_row / VEC_SIZE + col_idx] = DST_TYPE(src[i_src_row / VEC_SIZE + col_idx]); | |
| } | |