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
This build documentation is specific only to RISC-V SpacemiT SOCs.
Build llama.cpp locally (for riscv64)
- Prepare Toolchain For RISCV
wget https://github.com/spacemit-com/toolchain/releases/download/v1.2.4/spacemit-toolchain-linux-glibc-x86_64-v1.2.4.tar.xz
- Build
Below is the build script: it requires utilizing RISC-V vector instructions for acceleration. Ensure the
GGML_CPU_RISCV64_SPACEMITcompilation option is enabled. The currently supported optimization version isRISCV64_SPACEMIT_IME1andRISCV64_SPACEMIT_IME2, corresponding to theRISCV64_SPACEMIT_IME_SPECcompilation option. Compiler configurations are defined in theriscv64-spacemit-linux-gnu-gcc.cmakefile. Please ensure you have installed the RISC-V compiler and set the environment variable viaexport RISCV_ROOT_PATH={your_compiler_path}.
cmake -B build \
-DCMAKE_BUILD_TYPE=Release \
-DGGML_CPU_RISCV64_SPACEMIT=ON \
-DGGML_CPU_REPACK=OFF \
-DLLAMA_OPENSSL=OFF \
-DGGML_RVV=ON \
-DGGML_RV_ZVFH=ON \
-DGGML_RV_ZFH=ON \
-DGGML_RV_ZICBOP=ON \
-DGGML_RV_ZIHINTPAUSE=ON \
-DGGML_RV_ZBA=ON \
-DCMAKE_TOOLCHAIN_FILE=${PWD}/cmake/riscv64-spacemit-linux-gnu-gcc.cmake \
-DCMAKE_INSTALL_PREFIX=build/installed
cmake --build build --parallel $(nproc) --config Release
pushd build
make install
popd
Simulation
You can use QEMU to perform emulation on non-RISC-V architectures.
- Download QEMU
wget https://archive.spacemit.com/spacemit-ai/qemu/jdsk-qemu-v0.0.14.tar.gz
- Run Simulation After build your llama.cpp, you can run the executable file via QEMU for simulation, for example:
export QEMU_ROOT_PATH={your QEMU file path}
export RISCV_ROOT_PATH_IME1={your RISC-V compiler path}
${QEMU_ROOT_PATH}/bin/qemu-riscv64 -L ${RISCV_ROOT_PATH_IME1}/sysroot -cpu max,vlen=256,elen=64,vext_spec=v1.0 ${PWD}/build/bin/llama-cli -m ${PWD}/models/Qwen2.5-0.5B-Instruct-Q4_0.gguf -t 1
Quantization Support For Matrix
| Quantization Type | X60 | A100 |
|---|---|---|
| Q2_K | :heavy_check_mark: | |
| Q3_K | :heavy_check_mark: | |
| Q4_0 | :heavy_check_mark: | :heavy_check_mark: |
| Q4_1 | :heavy_check_mark: | :heavy_check_mark: |
| Q4_K | :heavy_check_mark: | :heavy_check_mark: |
| Q5_0 | :heavy_check_mark: | |
| Q5_1 | :heavy_check_mark: | |
| Q5_K | :heavy_check_mark: | |
| Q6_K | :heavy_check_mark: | |
| Q8_0 | :heavy_check_mark: |
Performance
- Spacemit(R) X60
model name : Spacemit(R) X60
isa : rv64imafdcv_zicbom_zicboz_zicntr_zicond_zicsr_zifencei_zihintpause_zihpm_zfh_zfhmin_zca_zcd_zba_zbb_zbc_zbs_zkt_zve32f_zve32x_zve64d_zve64f_zve64x_zvfh_zvfhmin_zvkt_sscofpmf_sstc_svinval_svnapot_svpbmt
mmu : sv39
uarch : spacemit,x60
mvendorid : 0x710
marchid : 0x8000000058000001
| model | size | params | backend | threads | n_ubatch | fa | mmap | test | t/s |
|---|---|---|---|---|---|---|---|---|---|
| qwen35 2B Q4_1 | 1.19 GiB | 1.88 B | CPU | 4 | 128 | 1 | 0 | pp128 | 10.32 ± 0.02 |
| qwen35 2B Q4_1 | 1.19 GiB | 1.88 B | CPU | 4 | 128 | 1 | 0 | tg128 | 3.07 ± 0.01 |
| qwen3 0.6B Q4_0 | 358.78 MiB | 596.05 M | CPU | 4 | 128 | 1 | 0 | pp128 | 49.15 ± 0.25 |
| qwen3 0.6B Q4_0 | 358.78 MiB | 596.05 M | CPU | 4 | 128 | 1 | 0 | tg128 | 11.73 ± 0.02 |
- Spacemit(R) A100
model name : Spacemit(R) A100
isa : rv64imafdcvh_zicbom_zicbop_zicboz_zicntr_zicond_zicsr_zifencei_zihintntl_zihintpause_zihpm_zimop_zaamo_zalrsc_zawrs_zfa_zfh_zfhmin_zca_zcb_zcd_zcmop_zba_zbb_zbc_zbs_zkt_zvbb_zvbc_zve32f_zve32x_zve64d_zve64f_zve64x_zvfh_zvfhmin_zvkb_zvkg_zvkned_zvknha_zvknhb_zvksed_zvksh_zvkt_smaia_smstateen_ssaia_sscofpmf_sstc_svinval_svnapot_svpbmt_sdtrig
mmu : sv39
mvendorid : 0x710
marchid : 0x8000000041000002
mimpid : 0x10000000d5686200
hart isa : rv64imafdcv_zicbom_zicbop_zicboz_zicntr_zicond_zicsr_zifencei_zihintntl_zihintpause_zihpm_zimop_zaamo_zalrsc_zawrs_zfa_zfh_zfhmin_zca_zcb_zcd_zcmop_zba_zbb_zbc_zbs_zkt_zvbb_zvbc_zve32f_zve32x_zve64d_zve64f_zve64x_zvfh_zvfhmin_zvkb_zvkg_zvkned_zvknha_zvknhb_zvksed_zvksh_zvkt_smaia_smstateen_ssaia_sscofpmf_sstc_svinval_svnapot_svpbmt_sdtrig
| model | size | params | backend | threads | n_ubatch | fa | mmap | test | t/s |
|---|---|---|---|---|---|---|---|---|---|
| qwen3 0.6B Q4_0 | 358.78 MiB | 596.05 M | CPU | 8 | 128 | 1 | 0 | pp128 | 565.83 ± 0.31 |
| qwen3 0.6B Q4_0 | 358.78 MiB | 596.05 M | CPU | 8 | 128 | 1 | 0 | tg128 | 55.77 ± 0.02 |
| qwen3 4B Q4_0 | 2.21 GiB | 4.02 B | CPU | 8 | 128 | 1 | 0 | pp128 | 79.74 ± 0.04 |
| qwen3 4B Q4_0 | 2.21 GiB | 4.02 B | CPU | 8 | 128 | 1 | 0 | tg128 | 11.29 ± 0.00 |
| qwen3moe 30B.A3B Q4_0 | 16.18 GiB | 30.53 B | CPU | 8 | 128 | 1 | 0 | pp128 | 57.88 ± 0.31 |
| qwen3moe 30B.A3B Q4_0 | 16.18 GiB | 30.53 B | CPU | 8 | 128 | 1 | 0 | tg128 | 12.79 ± 0.00 |
| qwen35 2B Q4_1 | 1.19 GiB | 1.88 B | CPU | 8 | 128 | 1 | 0 | pp128 | 115.23 ± 0.04 |
| qwen35 2B Q4_1 | 1.19 GiB | 1.88 B | CPU | 8 | 128 | 1 | 0 | tg128 | 16.49 ± 0.01 |
| gemma4 E4B Q4_K - Medium | 4.76 GiB | 7.52 B | CPU | 8 | 128 | 1 | 0 | pp128 | 21.13 ± 0.01 |
| gemma4 E4B Q4_K - Medium | 4.76 GiB | 7.52 B | CPU | 8 | 128 | 1 | 0 | tg128 | 5.66 ± 0.00 |