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
| //------------------------------------------------------------------------------ | |
| // div | |
| //------------------------------------------------------------------------------ | |
| kernel void kernel_div( | |
| global char * src0, | |
| ulong offset0, | |
| global char * src1, | |
| ulong offset1, | |
| global char * dst, | |
| ulong offsetd, | |
| ulong nb00, | |
| ulong nb01, | |
| ulong nb02, | |
| ulong nb03, | |
| int ne10, | |
| int ne11, | |
| int ne12, | |
| int ne13, | |
| ulong nb10, | |
| ulong nb11, | |
| ulong nb12, | |
| ulong nb13, | |
| int ne0, | |
| ulong nb0, | |
| ulong nb1, | |
| ulong nb2, | |
| ulong nb3 | |
| ) { | |
| src0 = src0 + offset0; | |
| src1 = src1 + offset1; | |
| dst = dst + offsetd; | |
| int i03 = get_group_id(2); | |
| int i02 = get_group_id(1); | |
| int i01 = get_group_id(0); | |
| int i13 = i03 % ne13; | |
| int i12 = i02 % ne12; | |
| int i11 = i01 % ne11; | |
| global char * src0_ptr = src0 + i03*nb03 + i02*nb02 + i01*nb01; | |
| global char * src1_ptr = src1 + i13*nb13 + i12*nb12 + i11*nb11; | |
| global char * dst_ptr = dst + i03*nb3 + i02*nb2 + i01*nb1; | |
| for (int i0 = get_local_id(0); i0 < ne0; i0 += get_local_size(0)) { | |
| const int i10 = i0 % ne10; | |
| *((global float *)(dst_ptr + i0*nb0)) = *((global float *)(src0_ptr + i0*nb00)) / *((global float *)(src1_ptr + i10*nb10)); | |
| } | |
| } | |
| // assumption: src1 is a row | |
| // broadcast src1 into src0 | |
| kernel void kernel_div_row( | |
| global float4 * src0, | |
| ulong offset0, | |
| global float4 * src1, | |
| ulong offset1, | |
| global float4 * dst, | |
| ulong offsetd, | |
| int ne | |
| ) { | |
| src0 = (global float4*)((global char*)src0 + offset0); | |
| src1 = (global float4*)((global char*)src1 + offset1); | |
| dst = (global float4*)((global char*)dst + offsetd); | |
| // This performs better than using %. | |
| uint gid = get_global_id(0); | |
| uint idx1 = gid - (gid/ne)*ne; // get_global_id(0) % ne | |
| dst[gid] = src0[gid] / src1[idx1]; | |
| } | |
| kernel void kernel_div_f16( | |
| global char * src0, | |
| ulong offset0, | |
| global char * src1, | |
| ulong offset1, | |
| global char * dst, | |
| ulong offsetd, | |
| ulong nb00, | |
| ulong nb01, | |
| ulong nb02, | |
| ulong nb03, | |
| int ne10, | |
| int ne11, | |
| int ne12, | |
| int ne13, | |
| ulong nb10, | |
| ulong nb11, | |
| ulong nb12, | |
| ulong nb13, | |
| int ne0, | |
| ulong nb0, | |
| ulong nb1, | |
| ulong nb2, | |
| ulong nb3 | |
| ) { | |
| src0 = src0 + offset0; | |
| src1 = src1 + offset1; | |
| dst = dst + offsetd; | |
| int i03 = get_group_id(2); | |
| int i02 = get_group_id(1); | |
| int i01 = get_group_id(0); | |
| int i13 = i03 % ne13; | |
| int i12 = i02 % ne12; | |
| int i11 = i01 % ne11; | |
| global char * src0_ptr = src0 + i03*nb03 + i02*nb02 + i01*nb01; | |
| global char * src1_ptr = src1 + i13*nb13 + i12*nb12 + i11*nb11; | |
| global char * dst_ptr = dst + i03*nb3 + i02*nb2 + i01*nb1; | |
| for (int i0 = get_local_id(0); i0 < ne0; i0 += get_local_size(0)) { | |
| const int i10 = i0 % ne10; | |
| *((global half *)(dst_ptr + i0*nb0)) = *((global half *)(src0_ptr + i0*nb00)) / *((global half *)(src1_ptr + i10*nb10)); | |
| } | |
| } | |
| kernel void kernel_div_row_f16( | |
| global half4 * src0, | |
| ulong offset0, | |
| global half4 * src1, | |
| ulong offset1, | |
| global half4 * dst, | |
| ulong offsetd, | |
| int ne | |
| ) { | |
| src0 = (global half4*)((global char*)src0 + offset0); | |
| src1 = (global half4*)((global char*)src1 + offset1); | |
| dst = (global half4*)((global char*)dst + offsetd); | |
| // This performs better than using %. | |
| uint gid = get_global_id(0); | |
| uint idx1 = gid - (gid/ne)*ne; // get_global_id(0) % ne | |
| dst[gid] = src0[gid] / src1[idx1]; | |
| } | |