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
| static void add_id_kernel( | |
| const float* src0, | |
| const float* src1, | |
| const int32_t* src2, | |
| float* dst, | |
| int64_t ne0, | |
| int64_t ne1, | |
| size_t nb01, | |
| size_t nb02, | |
| size_t nb11, | |
| size_t nb21, | |
| sycl::nd_item<3> item_ct1) { | |
| const int64_t i1 = item_ct1.get_group(2); | |
| const int64_t i2 = item_ct1.get_group(1); | |
| const int i11 = | |
| *(const int32_t*)((const char*)src2 + i1 * sizeof(int32_t) + i2 * nb21); | |
| const size_t nb1 = ne0 * sizeof(float); | |
| const size_t nb2 = ne1 * nb1; | |
| float* dst_row = (float*)((char*)dst + i1 * nb1 + i2 * nb2); | |
| const float* src0_row = | |
| (const float*)((const char*)src0 + i1 * nb01 + i2 * nb02); | |
| const float* src1_row = (const float*)((const char*)src1 + i11 * nb11); | |
| for (int64_t i0 = item_ct1.get_local_id(2); i0 < ne0; | |
| i0 += item_ct1.get_local_range(2)) { | |
| dst_row[i0] = src0_row[i0] + src1_row[i0]; | |
| } | |
| } | |
| void ggml_sycl_add_id(ggml_backend_sycl_context& ctx, ggml_tensor* dst) { | |
| const ggml_tensor* src0 = dst->src[0]; | |
| const ggml_tensor* src1 = dst->src[1]; | |
| const ggml_tensor* src2 = dst->src[2]; | |
| GGML_TENSOR_TERNARY_OP_LOCALS | |
| GGML_ASSERT(dst->type == GGML_TYPE_F32); | |
| GGML_ASSERT(src0->type == GGML_TYPE_F32); | |
| GGML_ASSERT(src1->type == GGML_TYPE_F32); | |
| GGML_ASSERT(src2->type == GGML_TYPE_I32); | |
| GGML_ASSERT(nb00 == sizeof(float)); | |
| GGML_ASSERT(nb10 == sizeof(float)); | |
| GGML_ASSERT(nb20 == sizeof(int32_t)); | |
| const float* src0_d = (const float*)src0->data; | |
| const float* src1_d = (const float*)src1->data; | |
| const int32_t* src2_d = (const int32_t*)src2->data; | |
| float* dst_d = (float*)dst->data; | |
| const unsigned int max_work_group_size = ggml_sycl_info().max_work_group_sizes[ctx.device]; | |
| GGML_ASSERT(max_work_group_size % (WARP_SIZE * WARP_SIZE) == 0); | |
| int threads = std::min((unsigned int)ne00, max_work_group_size); // cols | |
| ctx.stream()->parallel_for( | |
| sycl::nd_range<3>( | |
| sycl::range<3>(1, ne02, ne01) * sycl::range<3>(1, 1, threads), | |
| sycl::range<3>(1, 1, threads)), | |
| [=](sycl::nd_item<3> item_ct1) { | |
| add_id_kernel( | |
| src0_d, | |
| src1_d, | |
| src2_d, | |
| dst_d, | |
| ne0, | |
| ne1, | |
| nb01, | |
| nb02, | |
| nb11, | |
| nb21, | |
| item_ct1); | |
| }); | |
| } | |