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
| // SPDX-FileCopyrightText: Copyright 2025 Arm Limited and/or its affiliates <open-source-office@arm.com> | |
| // SPDX-License-Identifier: MIT | |
| // | |
| enum cpu_feature { | |
| CPU_FEATURE_NONE = 0, | |
| CPU_FEATURE_DOTPROD = 1, | |
| CPU_FEATURE_I8MM = 2, | |
| CPU_FEATURE_SVE = 4, | |
| CPU_FEATURE_SME = 8 | |
| }; | |
| inline cpu_feature& operator|=(cpu_feature& lhs, cpu_feature rhs) { | |
| lhs = static_cast<cpu_feature>(lhs | rhs); | |
| return lhs; | |
| } | |
| inline cpu_feature operator|(cpu_feature lhs, cpu_feature rhs) { | |
| return static_cast<cpu_feature>(static_cast<int>(lhs) | static_cast<int>(rhs)); | |
| } | |
| struct kernel_info { | |
| size_t (*get_m_step)(void); | |
| size_t (*get_n_step)(void); | |
| size_t (*get_mr)(void); | |
| size_t (*get_nr)(void); | |
| size_t (*get_kr)(void); | |
| size_t (*get_sr)(void); | |
| size_t (*get_dst_offset)(size_t m_idx, size_t n_idx, size_t stride); | |
| size_t (*get_dst_size)(size_t m, size_t n); | |
| size_t (*get_lhs_offset_ex)(size_t m_idx, size_t k, size_t bl); | |
| size_t (*get_rhs_packed_offset_ex)(size_t n_idx, size_t k, size_t bl); | |
| void (*run_kernel_ex)( | |
| size_t m, size_t n, size_t k, size_t bl, | |
| const void* lhs_packed, const void* rhs_packed, | |
| void* dst, size_t dst_stride_row, size_t dst_stride_col, | |
| float clamp_min, float clamp_max); | |
| }; | |
| struct lhs_packing_info { | |
| size_t (*get_offset)(size_t m_idx, size_t lhs_stride); | |
| size_t (*get_packed_offset_ex)(size_t m_idx, size_t k, size_t bl, size_t mr, size_t kr, size_t sr); | |
| size_t (*packed_size_ex)(size_t m, size_t k, size_t bl, size_t mr, size_t kr, size_t sr); | |
| void (*pack_func_ex)(size_t m, size_t k, size_t bl, size_t mr, size_t kr, size_t sr, | |
| size_t m_idx_start, const void * lhs, size_t lhs_stride, void * lhs_packed); | |
| }; | |
| struct rhs_packing_info { | |
| size_t (*packed_stride)(size_t k, size_t nr, size_t kr, size_t bl); | |
| void (*to_float)(const void *packed_data, int32_t row_idx, int64_t nc, float *out, | |
| size_t nr_pack, size_t packed_row_stride, size_t kr, size_t bl, | |
| size_t num_bytes_multiplier); | |
| size_t (*packed_size_ex)(size_t n, size_t k, size_t nr, size_t kr, size_t bl); | |
| size_t (*packed_stride_ex)(size_t k, size_t nr, size_t kr, size_t bl); | |
| void (*pack_func_ex)(size_t num_groups, size_t n, size_t k, size_t nr, size_t kr, size_t sr, size_t bl, | |
| size_t rhs_stride, const void * rhs, const void * bias, const void * scale, void * rhs_packed, size_t extra_bytes, const void * params); | |
| }; | |
| struct ggml_kleidiai_kernels { | |
| kernel_info gemm; | |
| lhs_packing_info gemm_lhs_info; | |
| kernel_info gemv; | |
| lhs_packing_info gemv_lhs_info; | |
| rhs_packing_info rhs_info; | |
| cpu_feature required_cpu; | |
| ggml_type lhs_type; | |
| ggml_type rhs_type; | |
| ggml_type op_type; | |
| }; | |
| ggml_kleidiai_kernels * ggml_kleidiai_select_kernels(cpu_feature cpu_features, const ggml_tensor * tensor); | |
| ggml_kleidiai_kernels * ggml_kleidiai_select_kernels_q4_0(cpu_feature features); | |
| ggml_kleidiai_kernels * ggml_kleidiai_select_kernels_q8_0(cpu_feature features); | |