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
| const uint num_threads = 256; | |
| layout (binding = 3, std430) buffer PartialBuf {float partial_sums[];}; | |
| layout(local_size_x = num_threads, local_size_y = 1, local_size_z = 1) in; | |
| // XXX TODO this could be sized based on number of subgroups, but that't not considered a constant | |
| shared FLOAT_TYPE sumsh[num_threads]; | |
| void main() { | |
| uint idx = get_idx(); | |
| uint orig_idx = idx; | |
| // num_threads * num_iter must equal 512, to match the wg_denoms and get_idx calculation | |
| const uint num_iter = 2; | |
| FLOAT_TYPE sum_sq = 0; | |
| [[unroll]] for (uint i = 0; i < num_iter; ++i) { | |
| if (idx >= p.ne) { | |
| continue; | |
| } | |
| uint i00, i01, i02, i03; | |
| get_indices(idx, i00, i01, i02, i03); | |
| FLOAT_TYPE sum = FLOAT_TYPE(data_a[get_aoffset() + src0_idx(i00, i01, i02, i03)]) + FLOAT_TYPE(data_b[get_boffset() + src1_idx(i00, i01, i02, i03)]); | |
| sum_sq += sum*sum; | |
| data_d[get_doffset() + dst_idx(i00, i01, i02, i03)] = D_TYPE(sum); | |
| idx += num_threads; | |
| } | |
| if (p.param3 != 0) { | |
| // reduce the sum within each subgroup, then across subgroups | |
| const uint NumSubgroups = num_threads / gl_SubgroupSize; | |
| sum_sq = subgroupAdd(sum_sq); | |
| if (gl_SubgroupInvocationID == 0) { | |
| sumsh[gl_SubgroupID] = sum_sq; | |
| } | |
| barrier(); | |
| [[unroll]] for (uint s = NumSubgroups / 2; s > 0; s >>= 1) { | |
| if (gl_SubgroupID < s && gl_SubgroupInvocationID == 0) { | |
| sum_sq += sumsh[gl_SubgroupID + s]; | |
| sumsh[gl_SubgroupID] = sum_sq; | |
| } | |
| barrier(); | |
| } | |
| if (gl_SubgroupID == 0 && gl_SubgroupInvocationID == 0) { | |
| partial_sums[orig_idx / (num_iter * num_threads)] = sum_sq; | |
| } | |
| } | |
| } | |