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
| @group(0) @binding(0) | |
| var<storage, read_write> src0: array<TYPE>; | |
| @group(0) @binding(SRC1_BINDING) | |
| var<storage, read_write> src1: array<TYPE>; | |
| @group(0) @binding(DST_BINDING) | |
| var<storage, read_write> dst: array<TYPE>; | |
| struct Params { | |
| ne: u32, | |
| offset_src0: u32, | |
| offset_src1: u32, | |
| offset_view: u32, | |
| stride_src10: u32, | |
| stride_src11: u32, | |
| stride_src12: u32, | |
| stride_src13: u32, | |
| stride_dst10: u32, | |
| stride_dst11: u32, | |
| stride_dst12: u32, | |
| stride_dst13: u32, | |
| src1_ne0: u32, | |
| src1_ne1: u32, | |
| src1_ne2: u32, | |
| src1_ne3: u32, | |
| }; | |
| @group(0) @binding(PARAMS_BINDING) | |
| var<uniform> params: Params; | |
| fn decode_src1_coords(idx: u32) -> vec4<u32> { | |
| var i = idx; | |
| let plane = params.src1_ne2 * params.src1_ne1 * params.src1_ne0; | |
| let i3 = i / plane; | |
| i = i % plane; | |
| let row = params.src1_ne1 * params.src1_ne0; | |
| let i2 = i / row; | |
| i = i % row; | |
| let i1 = i / params.src1_ne0; | |
| let i0 = i % params.src1_ne0; | |
| return vec4<u32>(i0, i1, i2, i3); | |
| } | |
| fn decode_view_coords(rel: u32) -> vec4<u32> { | |
| let i3 = rel / params.stride_dst13; | |
| let rem3 = rel % params.stride_dst13; | |
| let i2 = rem3 / params.stride_dst12; | |
| let rem2 = rem3 % params.stride_dst12; | |
| let i1 = rem2 / params.stride_dst11; | |
| let i0 = rem2 % params.stride_dst11; | |
| return vec4<u32>(i0, i1, i2, i3); | |
| } | |
| fn view_rel_from_coords(coords: vec4<u32>) -> u32 { | |
| return coords.x * params.stride_dst10 + coords.y * params.stride_dst11 + | |
| coords.z * params.stride_dst12 + coords.w * params.stride_dst13; | |
| } | |
| fn src1_idx_from_coords(coords: vec4<u32>) -> u32 { | |
| return coords.x * params.stride_src10 + coords.y * params.stride_src11 + | |
| coords.z * params.stride_src12 + coords.w * params.stride_src13; | |
| } | |
| fn in_set_view(rel: u32, coords: vec4<u32>) -> bool { | |
| return view_rel_from_coords(coords) == rel; | |
| } | |
| @compute @workgroup_size(WG_SIZE) | |
| fn main(@builtin(global_invocation_id) gid: vec3<u32>) { | |
| if (gid.x >= params.ne) { | |
| return; | |
| } | |
| let coords = decode_src1_coords(gid.x); | |
| let src1_idx = params.offset_src1 + src1_idx_from_coords(coords); | |
| let dst_idx = params.offset_view + view_rel_from_coords(coords); | |
| dst[dst_idx] = src1[src1_idx]; | |
| let rel = select(params.ne, gid.x - params.offset_view, gid.x >= params.offset_view); | |
| let coords = decode_view_coords(rel); | |
| if (rel < params.stride_dst13 * params.src1_ne3 && in_set_view(rel, coords)) { | |
| dst[gid.x] = src1[params.offset_src1 + src1_idx_from_coords(coords)]; | |
| } else { | |
| dst[gid.x] = src0[params.offset_src0 + gid.x]; | |
| } | |
| } | |