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(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in; | |
| layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; | |
| layout (binding = 1) buffer D {D_TYPE data_d[];}; | |
| layout (binding = 2) readonly buffer T {D_TYPE data_t[];}; | |
| layout (constant_id = 0) const uint BLOCK_SIZE = 128; | |
| layout (constant_id = 1) const uint SUBGROUP_SIZE = 32; | |
| shared FLOAT_TYPE temp[BLOCK_SIZE / SUBGROUP_SIZE]; | |
| void main() { | |
| const uint row = gl_WorkGroupID.y; | |
| const uint tid = gl_LocalInvocationID.x; | |
| const uint i03 = fastdiv(row, p.ne0_12mp, p.ne0_12L); | |
| const uint i03_offset = i03 * p.ne01*p.ne02; | |
| const uint i02 = fastdiv(row - i03_offset, p.ne0_1mp, p.ne0_1L); | |
| const uint i01 = row - i03_offset - i02*p.ne01; | |
| const uint src_idx = get_aoffset() + i01 * p.nb01 + i02 * p.nb02 + i03 * p.nb03; | |
| const uint dst_idx = get_doffset() + i01 * p.nb11 + i02 * p.nb12 + i03 * p.nb13; | |
| const uint col = gl_GlobalInvocationID.x; | |
| float v = 0; | |
| // prefetch value we're adding to | |
| if (col < p.n_cols) { | |
| v = data_d[dst_idx + col]; | |
| } | |
| // compute the sum of all previous blocks | |
| uint c = tid; | |
| float sum = 0; | |
| while (c < gl_WorkGroupID.x) { | |
| sum += data_t[c + gl_NumWorkGroups.x * row]; | |
| c += BLOCK_SIZE; | |
| } | |
| sum = subgroupAdd(sum); | |
| if (gl_SubgroupInvocationID == 0) { | |
| temp[gl_SubgroupID] = sum; | |
| } | |
| barrier(); | |
| sum = 0; | |
| [[unroll]] for (uint s = 0; s < BLOCK_SIZE / SUBGROUP_SIZE; ++s) { | |
| sum += temp[s]; | |
| } | |
| // Add the sum to what the first pass computed | |
| if (col < p.n_cols) { | |
| data_d[dst_idx + col] = v + sum; | |
| } | |
| } | |