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
| struct aarch64_features { | |
| // has_neon not needed, aarch64 has NEON guaranteed | |
| bool has_dotprod = false; | |
| bool has_fp16_va = false; | |
| bool has_sve = false; | |
| bool has_sve2 = false; | |
| bool has_i8mm = false; | |
| bool has_sme = false; | |
| aarch64_features() { | |
| uint32_t hwcap = getauxval(AT_HWCAP); | |
| uint32_t hwcap2 = getauxval(AT_HWCAP2); | |
| has_dotprod = !!(hwcap & HWCAP_ASIMDDP); | |
| has_fp16_va = !!(hwcap & HWCAP_FPHP); | |
| has_sve = !!(hwcap & HWCAP_SVE); | |
| has_sve2 = !!(hwcap2 & HWCAP2_SVE2); | |
| has_i8mm = !!(hwcap2 & HWCAP2_I8MM); | |
| has_sme = !!(hwcap2 & HWCAP2_SME); | |
| int oldp = 0; | |
| size_t size = sizeof(oldp); | |
| if (sysctlbyname("hw.optional.arm.FEAT_DotProd", &oldp, &size, NULL, 0) == 0) { | |
| has_dotprod = static_cast<bool>(oldp); | |
| } | |
| if (sysctlbyname("hw.optional.arm.FEAT_I8MM", &oldp, &size, NULL, 0) == 0) { | |
| has_i8mm = static_cast<bool>(oldp); | |
| } | |
| if (sysctlbyname("hw.optional.arm.FEAT_SME", &oldp, &size, NULL, 0) == 0) { | |
| has_sme = static_cast<bool>(oldp); | |
| } | |
| // Apple apparently does not implement SVE yet | |
| } | |
| }; | |
| static int ggml_backend_cpu_aarch64_score() { | |
| int score = 1; | |
| aarch64_features af; | |
| if (!af.has_dotprod) { return 0; } | |
| score += 1<<1; | |
| if (!af.has_fp16_va) { return 0; } | |
| score += 1<<2; | |
| if (!af.has_sve) { return 0; } | |
| score += 1<<3; | |
| if (!af.has_i8mm) { return 0; } | |
| score += 1<<4; | |
| if (!af.has_sve2) { return 0; } | |
| score += 1<<5; | |
| if (!af.has_sme) { return 0; } | |
| score += 1<<6; | |
| return score; | |
| } | |
| GGML_BACKEND_DL_SCORE_IMPL(ggml_backend_cpu_aarch64_score) | |