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
| kernel void kernel_exp_f32( | |
| global const float * src0, | |
| ulong offset0, | |
| global float * dst, | |
| ulong offsetd, | |
| int n | |
| ) { | |
| if (get_global_id(0) >= n) { | |
| return; | |
| } | |
| src0 = (global float*)((global char*)src0 + offset0); | |
| dst = (global float*)((global char*)dst + offsetd); | |
| dst[get_global_id(0)] = exp(src0[get_global_id(0)]); | |
| } | |
| kernel void kernel_exp_f32_4( | |
| global const float4 * src0, | |
| ulong offset0, | |
| global float4 * dst, | |
| ulong offsetd, | |
| int n | |
| ) { | |
| if (get_global_id(0) >= n) { | |
| return; | |
| } | |
| src0 = (global float4*)((global char*)src0 + offset0); | |
| dst = (global float4*)((global char*)dst + offsetd); | |
| dst[get_global_id(0)] = exp(src0[get_global_id(0)]); | |
| } | |
| kernel void kernel_exp_f16( | |
| global const half * src0, | |
| ulong offset0, | |
| global half * dst, | |
| ulong offsetd, | |
| int n | |
| ) { | |
| if (get_global_id(0) >= n) { | |
| return; | |
| } | |
| src0 = (global half*)((global char*)src0 + offset0); | |
| dst = (global half*)((global char*)dst + offsetd); | |
| dst[get_global_id(0)] = exp(src0[get_global_id(0)]); | |
| } | |
| kernel void kernel_exp_f16_4( | |
| global const half4 * src0, | |
| ulong offset0, | |
| global half4 * dst, | |
| ulong offsetd, | |
| int n | |
| ) { | |
| if (get_global_id(0) >= n) { | |
| return; | |
| } | |
| src0 = (global half4*)((global char*)src0 + offset0); | |
| dst = (global half4*)((global char*)dst + offsetd); | |
| dst[get_global_id(0)] = exp(src0[get_global_id(0)]); | |
| } | |
| kernel void kernel_exp_f32_nc( | |
| global const char * src0, | |
| ulong offset0, | |
| global char * dst, | |
| ulong offsetd, | |
| int ne00, | |
| ulong nb00, | |
| ulong nb01, | |
| ulong nb02, | |
| ulong nb03, | |
| ulong nb0, | |
| ulong nb1, | |
| ulong nb2, | |
| ulong nb3 | |
| ) { | |
| src0 = src0 + offset0; | |
| dst = dst + offsetd; | |
| const int i3 = get_group_id(2); | |
| const int i2 = get_group_id(1); | |
| const int i1 = get_group_id(0); | |
| for (int i0 = get_local_id(0); i0 < ne00; i0 += get_local_size(0)) { | |
| global const float * x = (global const float *)(src0 + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); | |
| global float * y = (global float *)(dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); | |
| *y = exp(*x); | |
| } | |
| } | |
| kernel void kernel_exp_f16_nc( | |
| global const char * src0, | |
| ulong offset0, | |
| global char * dst, | |
| ulong offsetd, | |
| int ne00, | |
| ulong nb00, | |
| ulong nb01, | |
| ulong nb02, | |
| ulong nb03, | |
| ulong nb0, | |
| ulong nb1, | |
| ulong nb2, | |
| ulong nb3 | |
| ) { | |
| src0 = src0 + offset0; | |
| dst = dst + offsetd; | |
| const int i3 = get_group_id(2); | |
| const int i2 = get_group_id(1); | |
| const int i1 = get_group_id(0); | |
| for (int i0 = get_local_id(0); i0 < ne00; i0 += get_local_size(0)) { | |
| global const half * x = (global const half *)(src0 + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); | |
| global half * y = (global half *)(dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); | |
| *y = exp(*x); | |
| } | |
| } | |