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
| // | |
| // MIT license | |
| // Copyright (C) 2024 Intel Corporation | |
| // SPDX-License-Identifier: MIT | |
| // | |
| // | |
| // Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions. | |
| // See https://llvm.org/LICENSE.txt for license information. | |
| // SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception | |
| // | |
| static inline size_t elem_size(ggml_type t) { | |
| return ggml_type_size(t) / ggml_blck_size(t); | |
| } | |
| template <typename T> | |
| static void concat_T_dim0(const T *x, const T *y, T *dst, | |
| const int ne0, const int ne00, | |
| const sycl::nd_item<3> &item_ct1) { | |
| int nidx = item_ct1.get_local_id(2) + | |
| item_ct1.get_group(2) * item_ct1.get_local_range(2); | |
| if (nidx >= ne0) { | |
| return; | |
| } | |
| // operation | |
| int offset_dst = nidx + item_ct1.get_group(1) * ne0 + | |
| item_ct1.get_group(0) * ne0 * item_ct1.get_group_range(1); | |
| if (nidx < ne00) { // src0 | |
| int offset_src = nidx + item_ct1.get_group(1) * ne00 + | |
| item_ct1.get_group(0) * ne00 * item_ct1.get_group_range(1); | |
| dst[offset_dst] = x[offset_src]; | |
| } else { | |
| int offset_src = | |
| nidx - ne00 + item_ct1.get_group(1) * (ne0 - ne00) + | |
| item_ct1.get_group(0) * (ne0 - ne00) * item_ct1.get_group_range(1); | |
| dst[offset_dst] = y[offset_src]; | |
| } | |
| } | |
| template <typename T> | |
| static void concat_T_dim1(const T *x, const T *y, T *dst, | |
| const int ne0, const int ne01, | |
| const sycl::nd_item<3> &item_ct1) { | |
| int nidx = item_ct1.get_local_id(2) + | |
| item_ct1.get_group(2) * item_ct1.get_local_range(2); | |
| if (nidx >= ne0) { | |
| return; | |
| } | |
| // operation | |
| int offset_dst = nidx + item_ct1.get_group(1) * ne0 + | |
| item_ct1.get_group(0) * ne0 * item_ct1.get_group_range(1); | |
| if (item_ct1.get_group(1) < (size_t) ne01) { // src0 | |
| int offset_src = | |
| nidx + item_ct1.get_group(1) * ne0 + item_ct1.get_group(0) * ne0 * ne01; | |
| dst[offset_dst] = x[offset_src]; | |
| } else { | |
| int offset_src = | |
| nidx + (item_ct1.get_group(1) - ne01) * ne0 + | |
| item_ct1.get_group(0) * ne0 * (item_ct1.get_group_range(1) - ne01); | |
| dst[offset_dst] = y[offset_src]; | |
| } | |
| } | |
| template <typename T> | |
| static void concat_T_dim2(const T *x, const T *y, T *dst, | |
| const int ne0, const int ne02, | |
| const sycl::nd_item<3> &item_ct1) { | |
| int nidx = item_ct1.get_local_id(2) + | |
| item_ct1.get_group(2) * item_ct1.get_local_range(2); | |
| if (nidx >= ne0) { | |
| return; | |
| } | |
| // operation | |
| int offset_dst = nidx + item_ct1.get_group(1) * ne0 + | |
| item_ct1.get_group(0) * ne0 * item_ct1.get_group_range(1); | |
| if (item_ct1.get_group(0) < (size_t) ne02) { // src0 | |
| int offset_src = nidx + item_ct1.get_group(1) * ne0 + | |
| item_ct1.get_group(0) * ne0 * item_ct1.get_group_range(1); | |
| dst[offset_dst] = x[offset_src]; | |
| } else { | |
| int offset_src = | |
| nidx + item_ct1.get_group(1) * ne0 + | |
| (item_ct1.get_group(0) - ne02) * ne0 * item_ct1.get_group_range(1); | |
| dst[offset_dst] = y[offset_src]; | |
| } | |
| } | |
| template <typename T> | |
| static void concat_T_sycl(const T *x, const T *y, T *dst, | |
| int ne00, int ne01, int ne02, int ne0, int ne1, | |
| int ne2, int dim, queue_ptr stream) { | |
| int num_blocks = (ne0 + SYCL_CONCAT_BLOCK_SIZE - 1) / SYCL_CONCAT_BLOCK_SIZE; | |
| sycl::range<3> gridDim(ne2, ne1, num_blocks); | |
| switch (dim) { | |
| case 0: | |
| stream->parallel_for(sycl::nd_range<3>(gridDim * sycl::range<3>(1, 1, SYCL_CONCAT_BLOCK_SIZE), | |
| sycl::range<3>(1, 1, SYCL_CONCAT_BLOCK_SIZE)), | |
| [=](sycl::nd_item<3> item_ct1) { concat_T_dim0<T>(x, y, dst, ne0, ne00, item_ct1); }); | |
| break; | |
| case 1: | |
| stream->parallel_for(sycl::nd_range<3>(gridDim * sycl::range<3>(1, 1, SYCL_CONCAT_BLOCK_SIZE), | |
| sycl::range<3>(1, 1, SYCL_CONCAT_BLOCK_SIZE)), | |
| [=](sycl::nd_item<3> item_ct1) { concat_T_dim1<T>(x, y, dst, ne0, ne01, item_ct1); }); | |
| break; | |
| // dim >=2 will be dispatched to the default path | |
| default: | |
| stream->parallel_for(sycl::nd_range<3>(gridDim * sycl::range<3>(1, 1, SYCL_CONCAT_BLOCK_SIZE), | |
| sycl::range<3>(1, 1, SYCL_CONCAT_BLOCK_SIZE)), | |
| [=](sycl::nd_item<3> item_ct1) { concat_T_dim2<T>(x, y, dst, ne0, ne02, item_ct1); }); | |
| break; | |
| } | |
| } | |
| // non-contiguous kernel (slow) | |
| template<typename T> | |
| static void concat_T_sycl_non_cont( | |
| queue_ptr stream, const char *src0, const char *src1, char *dst, | |
| int64_t ne00, int64_t ne01, int64_t ne02, int64_t ne03, uint64_t nb00, | |
| uint64_t nb01, uint64_t nb02, uint64_t nb03, int64_t /*ne10*/, | |
| int64_t /*ne11*/, int64_t /*ne12*/, int64_t /*ne13*/, uint64_t nb10, | |
| uint64_t nb11, uint64_t nb12, uint64_t nb13, int64_t ne0, int64_t ne1, | |
| int64_t ne2, int64_t ne3, uint64_t nb0, uint64_t nb1, uint64_t nb2, | |
| uint64_t nb3, int32_t dim) { | |
| sycl::range<3> gridDim(ne3, ne2, ne1); | |
| stream->parallel_for(sycl::nd_range<3>(gridDim, sycl::range<3>(1, 1, 1)), [=](sycl::nd_item<3> item_ct1) { | |
| int64_t i3 = item_ct1.get_group(0); | |
| int64_t i2 = item_ct1.get_group(1); | |
| int64_t i1 = item_ct1.get_group(2); | |
| int64_t o[4] = { 0, 0, 0, 0 }; | |
| o[dim] = dim == 0 ? ne00 : (dim == 1 ? ne01 : (dim == 2 ? ne02 : ne03)); | |
| const T * x; | |
| for (int i0 = item_ct1.get_local_id(2); i0 < ne0; i0 += item_ct1.get_local_range(2)) { | |
| if (i0 < ne00 && i1 < ne01 && i2 < ne02 && i3 < ne03) { | |
| x = (const T *) (src0 + (i3) *nb03 + (i2) *nb02 + (i1) *nb01 + (i0) *nb00); | |
| } else { | |
| x = (const T *) (src1 + (i3 - o[3]) * nb13 + (i2 - o[2]) * nb12 + (i1 - o[1]) * nb11 + | |
| (i0 - o[0]) * nb10); | |
| } | |
| T *y = (T *)(dst + i3 * nb3 + i2 * nb2 + i1 * nb1 + i0 * nb0); | |
| *y = *x; | |
| } | |
| }); | |
| } | |
| template <typename T> | |
| void concat_impl_sycl(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]; | |
| queue_ptr stream = ctx.stream(); | |
| const int32_t dim = ((int32_t *) dst->op_params)[0]; | |
| if (ggml_is_contiguous(src0) && ggml_is_contiguous(src1)) { | |
| const T * src0_d = (const T *) src0->data; | |
| const T * src1_d = (const T *) src1->data; | |
| T * dst_d = (T *) dst->data; | |
| size_t type_size = elem_size(dst->type); | |
| if (dim != 3) { | |
| for (int i3 = 0; i3 < dst->ne[3]; i3++) { | |
| concat_T_sycl<T>(src0_d + i3 * (src0->nb[3] / type_size), src1_d + i3 * (src1->nb[3] / type_size), | |
| dst_d + i3 * (dst->nb[3] / type_size), src0->ne[0], src0->ne[1], src0->ne[2], dst->ne[0], | |
| dst->ne[1], dst->ne[2], dim, stream); | |
| } | |
| } else { | |
| const size_t size0 = ggml_nbytes(src0); | |
| const size_t size1 = ggml_nbytes(src1); | |
| SYCL_CHECK(CHECK_TRY_ERROR(stream->memcpy(dst_d, src0_d, size0).wait())); | |
| SYCL_CHECK(CHECK_TRY_ERROR(stream->memcpy(dst_d + size0 / type_size, src1_d, size1).wait())); | |
| } | |
| } else { | |
| concat_T_sycl_non_cont<T>(stream, (const char *) src0->data, (const char *) src1->data, (char *) dst->data, | |
| src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3], src0->nb[0], src0->nb[1], | |
| src0->nb[2], src0->nb[3], src1->ne[0], src1->ne[1], src1->ne[2], src1->ne[3], | |
| src1->nb[0], src1->nb[1], src1->nb[2], src1->nb[3], dst->ne[0], dst->ne[1], dst->ne[2], | |
| dst->ne[3], dst->nb[0], dst->nb[1], dst->nb[2], dst->nb[3], dim); | |
| } | |
| } | |
| void ggml_sycl_op_concat(ggml_backend_sycl_context & ctx, ggml_tensor *dst) { | |
| switch (dst->type) { | |
| case GGML_TYPE_F32: | |
| concat_impl_sycl<float>(ctx, dst); | |
| break; | |
| case GGML_TYPE_F16: | |
| concat_impl_sycl<sycl::half>(ctx, dst); | |
| break; | |
| case GGML_TYPE_BF16: | |
| concat_impl_sycl<sycl::ext::oneapi::bfloat16>(ctx, dst); | |
| break; | |
| case GGML_TYPE_I32: | |
| concat_impl_sycl<int32_t>(ctx, dst); | |
| break; | |
| case GGML_TYPE_I16: | |
| concat_impl_sycl<int16_t>(ctx, dst); | |
| break; | |
| case GGML_TYPE_I64: | |
| concat_impl_sycl<int64_t>(ctx, dst); | |
| break; | |
| case GGML_TYPE_I8: | |
| concat_impl_sycl<int8_t>(ctx, dst); | |
| break; | |
| default: | |
| fprintf(stderr, "%s: unsupported types: dst: %s\n", __func__, ggml_type_name(dst->type)); | |
| GGML_ASSERT(false); | |
| break; | |
| } | |
| } | |