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
| find_package(OpenCL REQUIRED) | |
| find_package(Python3 REQUIRED) | |
| set(TARGET_NAME ggml-opencl) | |
| ggml_add_backend_library(${TARGET_NAME} | |
| ggml-opencl.cpp | |
| ../../include/ggml-opencl.h) | |
| target_link_libraries(${TARGET_NAME} PRIVATE ${OpenCL_LIBRARIES}) | |
| target_include_directories(${TARGET_NAME} PRIVATE ${OpenCL_INCLUDE_DIRS}) | |
| if (GGML_OPENCL_PROFILING) | |
| message(STATUS "OpenCL profiling enabled (increases CPU overhead)") | |
| add_compile_definitions(GGML_OPENCL_PROFILING) | |
| endif () | |
| add_compile_definitions(GGML_OPENCL_SOA_Q) | |
| add_compile_definitions(GGML_OPENCL_TARGET_VERSION=${GGML_OPENCL_TARGET_VERSION}) | |
| if (GGML_OPENCL_USE_ADRENO_KERNELS) | |
| message(STATUS "OpenCL will use matmul kernels optimized for Adreno") | |
| add_compile_definitions(GGML_OPENCL_USE_ADRENO_KERNELS) | |
| endif () | |
| if (GGML_OPENCL_EMBED_KERNELS) | |
| add_compile_definitions(GGML_OPENCL_EMBED_KERNELS) | |
| set(EMBED_KERNEL_SCRIPT "${CMAKE_CURRENT_SOURCE_DIR}/kernels/embed_kernel.py") | |
| file(MAKE_DIRECTORY "${CMAKE_CURRENT_BINARY_DIR}/autogenerated") | |
| target_include_directories(${TARGET_NAME} PRIVATE "${CMAKE_CURRENT_BINARY_DIR}/autogenerated") | |
| endif () | |
| if (GGML_OPENCL_USE_ADRENO_BIN_KERNELS) | |
| message(STATUS "OpenCL will use precompiled binary kernels for Adreno (improved performance on some platforms)") | |
| add_compile_definitions(GGML_OPENCL_USE_ADRENO_BIN_KERNELS) | |
| endif () | |
| function(ggml_opencl_add_kernel KNAME) | |
| set(KERN_HDR ${CMAKE_CURRENT_BINARY_DIR}/autogenerated/${KNAME}.cl.h) | |
| set(KERN_SRC ${CMAKE_CURRENT_SOURCE_DIR}/kernels/${KNAME}.cl) | |
| if (GGML_OPENCL_EMBED_KERNELS) | |
| message(STATUS "opencl: embedding kernel ${KNAME}") | |
| # Python must be accessible from command line | |
| add_custom_command( | |
| OUTPUT ${KERN_HDR} | |
| COMMAND ${Python3_EXECUTABLE} ${EMBED_KERNEL_SCRIPT} ${KERN_SRC} ${KERN_HDR} | |
| DEPENDS ${KERN_SRC} ${EMBED_KERNEL_SCRIPT} | |
| COMMENT "Generate ${KERN_HDR}" | |
| ) | |
| target_sources(${TARGET_NAME} PRIVATE ${KERN_HDR}) | |
| else () | |
| message(STATUS "opencl: adding kernel ${KNAME}") | |
| configure_file(${KERN_SRC} ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/${KNAME}.cl COPYONLY) | |
| endif () | |
| endfunction() | |
| set(GGML_OPENCL_KERNELS | |
| add | |
| add_id | |
| argsort | |
| tri | |
| fill | |
| clamp | |
| cpy | |
| cvt | |
| diag_mask_inf | |
| diag | |
| div | |
| gelu | |
| get_rows | |
| glu | |
| group_norm | |
| solve_tri | |
| im2col_f32 | |
| im2col_f16 | |
| mean | |
| mul_mv_f16_f16 | |
| mul_mv_f16_f32_1row | |
| mul_mv_f16_f32_l4 | |
| mul_mv_f16_f32 | |
| mul_mv_f32_f32 | |
| mul_mv_q1_0_f32 | |
| mul_mv_q1_0_f32_flat | |
| mul_mv_q4_0_f32 | |
| mul_mv_q4_0_f32_v | |
| mul_mv_q4_0_f32_8x_flat | |
| mul_mv_q4_0_f32_1d_8x_flat | |
| mul_mv_q4_0_f32_1d_16x_flat | |
| mul_mv_q4_1_f32 | |
| mul_mv_q4_1_f32_flat | |
| mul_mv_q4_k_f32 | |
| mul_mv_q4_k_f32_flat | |
| mul_mv_q5_0_f32 | |
| mul_mv_q5_0_f32_flat | |
| mul_mv_q5_1_f32 | |
| mul_mv_q5_1_f32_flat | |
| mul_mv_q5_k_f32 | |
| mul_mv_q5_k_f32_flat | |
| mul_mv_q6_k_f32 | |
| mul_mv_q6_k_f32_flat | |
| mul_mv_q8_0_f32 | |
| mul_mv_q8_0_f32_flat | |
| mul_mv_iq4_nl_f32 | |
| mul_mv_iq4_nl_f32_flat | |
| mul_mv_mxfp4_f32 | |
| mul_mv_mxfp4_f32_flat | |
| mul_mv_id_q4_0_f32_8x_flat | |
| mul_mv_id_q8_0_f32 | |
| mul_mv_id_q8_0_f32_flat | |
| mul_mv_id_mxfp4_f32 | |
| mul_mv_id_mxfp4_f32_flat | |
| gemm_moe_q4_0_f32_ns | |
| gemv_moe_q4_0_f32_ns | |
| gemm_moe_q4_1_f32_ns | |
| gemv_moe_q4_1_f32_ns | |
| gemm_moe_q5_0_f32_ns | |
| gemv_moe_q5_0_f32_ns | |
| gemm_moe_q5_1_f32_ns | |
| gemv_moe_q5_1_f32_ns | |
| gemm_moe_q4_k_f32_ns | |
| gemv_moe_q4_k_f32_ns | |
| gemm_moe_q5_k_f32_ns | |
| gemv_moe_q5_k_f32_ns | |
| gemm_moe_q6_k_f32_ns | |
| gemv_moe_q6_k_f32_ns | |
| gemm_moe_mxfp4_f32 | |
| gemv_moe_mxfp4_f32 | |
| gemm_moe_mxfp4_f32_ns | |
| gemv_moe_mxfp4_f32_ns | |
| moe_reorder_b | |
| moe_sort_by_expert | |
| mul_mm_f32_f32_l4_lm | |
| mul_mm_f16_f32_l4_lm | |
| mul_mm_q1_0_f32_l4_lm | |
| mul_mm_q4_0_f32_l4_lm | |
| mul_mm_q4_1_f32_l4_lm | |
| mul_mm_q5_0_f32_l4_lm | |
| mul_mm_q5_1_f32_l4_lm | |
| mul_mm_q8_0_f32_l4_lm | |
| mul_mm_iq4_nl_f32_l4_lm | |
| mul_mm_q4_k_f32_l4_lm | |
| mul_mm_q5_k_f32_l4_lm | |
| mul_mm_q6_k_f32_l4_lm | |
| gemv_noshuffle_q1_0_f32 | |
| gemm_noshuffle_q1_0_f32 | |
| gemv_noshuffle_q4_0_f32 | |
| gemv_noshuffle_q4_0_f32_spec | |
| gemm_noshuffle_q4_0_f32 | |
| gemv_noshuffle_q4_1_f32 | |
| gemm_noshuffle_q4_1_f32 | |
| gemv_noshuffle_q5_0_f32 | |
| gemm_noshuffle_q5_0_f32 | |
| gemv_noshuffle_q5_1_f32 | |
| gemm_noshuffle_q5_1_f32 | |
| gemv_noshuffle_iq4_nl_f32 | |
| gemm_noshuffle_iq4_nl_f32 | |
| gemv_noshuffle_q8_0_f32 | |
| gemm_noshuffle_q8_0_f32 | |
| gemv_noshuffle_q4_k_f32 | |
| gemm_noshuffle_q4_k_f32 | |
| gemv_noshuffle_q6_k_f32 | |
| gemm_noshuffle_q6_k_f32 | |
| gemv_noshuffle_q5_k_f32 | |
| gemm_noshuffle_q5_k_f32 | |
| mul | |
| neg | |
| norm | |
| relu | |
| l2_norm | |
| rms_norm | |
| rope | |
| scale | |
| set_rows | |
| sigmoid | |
| silu | |
| softmax_4_f32 | |
| softmax_4_f16 | |
| softmax_f32 | |
| softmax_f16 | |
| sqr | |
| sqrt | |
| ssm_conv | |
| gated_delta_net | |
| sub | |
| sum_rows | |
| cumsum | |
| transpose | |
| concat | |
| tsembd | |
| upscale | |
| tanh | |
| exp | |
| expm1 | |
| softplus | |
| pad | |
| repeat | |
| mul_mat_f16_f32 | |
| mul_mm_f16_f32_kq_kqv | |
| conv2d | |
| conv2d_f16_f32 | |
| flash_attn_pre_f16 | |
| flash_attn_f32_f16 | |
| flash_attn_f32_q8_0 | |
| flash_attn_f32_q4_0 | |
| flash_attn_f16 | |
| flash_attn_f32 | |
| ) | |
| if (GGML_OPENCL_USE_ADRENO_KERNELS) | |
| list(APPEND GGML_OPENCL_KERNELS gemm_xmem_f16_f32_os8) | |
| endif () | |
| foreach (K ${GGML_OPENCL_KERNELS}) | |
| ggml_opencl_add_kernel(${K}) | |
| endforeach() | |