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
| template <typename T> | |
| static void count_equal(const T *__restrict__ x, const T *__restrict__ y, | |
| int64_t *__restrict__ dst, const int64_t dk, | |
| const int64_t k) { | |
| auto item_ct1 = sycl::ext::oneapi::this_work_item::get_nd_item<3>(); | |
| const int64_t i0 = (int64_t)item_ct1.get_group(2) * dk; | |
| const int64_t i1 = sycl::min(i0 + dk, k); | |
| int nequal = 0; | |
| for (int64_t i = i0 + item_ct1.get_local_id(2); i < i1; i += WARP_SIZE) { | |
| const T xi = x[i]; | |
| const T yi = y[i]; | |
| nequal += xi == yi; | |
| } | |
| nequal = warp_reduce_sum<WARP_SIZE>(nequal); | |
| if (item_ct1.get_local_id(2) != 0) { | |
| return; | |
| } | |
| dpct::atomic_fetch_add<sycl::access::address_space::generic_space>( | |
| (int *)dst, nequal); | |
| } | |
| void ggml_sycl_count_equal(ggml_backend_sycl_context &ctx, ggml_tensor *dst) { | |
| scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/2); | |
| const ggml_tensor * src0 = dst->src[0]; | |
| const ggml_tensor * src1 = dst->src[1]; | |
| GGML_ASSERT(src0->type == src1->type); | |
| GGML_ASSERT( dst->type == GGML_TYPE_I64); | |
| GGML_ASSERT(ggml_are_same_shape(src0, src1)); | |
| GGML_ASSERT(ggml_is_contiguous(src0)); | |
| GGML_ASSERT(ggml_is_contiguous(src1)); | |
| GGML_ASSERT(ggml_is_contiguous(dst)); | |
| int64_t * dst_d = (int64_t *) dst->data; | |
| dpct::queue_ptr stream = ctx.stream(); | |
| const int id = get_current_device_id(); | |
| const int nsm = ggml_sycl_info().devices[id].nsm; | |
| const int64_t ne = ggml_nelements(src0); | |
| GGML_ASSERT(ne < (1 << 30) && "atomicAdd implementation only supports int"); | |
| const int64_t dne = | |
| GGML_PAD((ne + 4 * nsm - 1) / (4 * nsm), SYCL_COUNT_EQUAL_CHUNK_SIZE); | |
| SYCL_CHECK(CHECK_TRY_ERROR(stream->memset(dst_d, 0, ggml_nbytes(dst)))); | |
| const dpct::dim3 block_dims(WARP_SIZE, 1, 1); | |
| const dpct::dim3 block_nums( | |
| std::min((int64_t)4 * nsm, (ne + SYCL_COUNT_EQUAL_CHUNK_SIZE - 1) / | |
| SYCL_COUNT_EQUAL_CHUNK_SIZE), | |
| 1, 1); | |
| switch (src0->type) { | |
| case GGML_TYPE_I32: { | |
| const int *src0_d = (const int *)src0->data; | |
| const int *src1_d = (const int *)src1->data; | |
| stream->parallel_for( | |
| sycl::nd_range<3>(block_nums * block_dims, block_dims), | |
| [=](sycl::nd_item<3> item_ct1) { | |
| count_equal(src0_d, src1_d, dst_d, dne, ne); | |
| GGML_UNUSED(item_ct1); | |
| }); | |
| } break; | |
| default: | |
| GGML_ASSERT(false); | |
| break; | |
| } | |
| } | |