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
| layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; // columns: [K_OC, T_in] | |
| layout (binding = 1) writeonly buffer D {D_TYPE data_d[];}; // output: [T_out, OC] | |
| layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in; | |
| layout (push_constant) uniform parameter { | |
| uint32_t T_out; | |
| uint32_t OC; | |
| uint32_t K_OC; | |
| uint32_t T_in; | |
| uint32_t K; | |
| int32_t stride; | |
| int32_t p0; | |
| } p; | |
| // Load A_TYPE to float | |
| float load_col(uint32_t idx) { | |
| return bf16_to_fp32(uint32_t(data_a[idx])); | |
| return float(data_a[idx]); | |
| } | |
| // Store float as D_TYPE | |
| void store_dst(uint32_t idx, float v) { | |
| data_d[idx] = D_TYPE(fp32_to_bf16(v)); | |
| data_d[idx] = D_TYPE(v); | |
| } | |
| void main() { | |
| const uint32_t t_out = gl_GlobalInvocationID.x; | |
| const uint32_t oc = gl_GlobalInvocationID.y; | |
| if (t_out >= p.T_out || oc >= p.OC) return; | |
| const int32_t t_abs = int32_t(t_out) + p.p0; // absolute position in uncropped signal | |
| // Gather: only the ceil(K/stride) columns that scatter into t_abs, no modulo | |
| int32_t t_in_min = (t_abs - int32_t(p.K) + p.stride) / p.stride; | |
| if (t_in_min < 0) t_in_min = 0; | |
| int32_t t_in_max = t_abs / p.stride; | |
| if (t_in_max >= int32_t(p.T_in)) t_in_max = int32_t(p.T_in) - 1; | |
| float val = 0.0; | |
| for (int32_t t_in = t_in_min; t_in <= t_in_max; t_in++) { | |
| int32_t k = t_abs - t_in * p.stride; | |
| // col layout: [K_OC, T_in], column index = oc * K + k | |
| uint32_t col_idx = (oc * p.K + uint32_t(k)) + uint32_t(t_in) * p.K_OC; | |
| val += load_col(col_idx); | |
| } | |
| // dst layout: [T_out, OC], element (t_out, oc) = t_out + oc * T_out | |
| store_dst(t_out + oc * p.T_out, val); | |
| } | |