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 powerpc_features { | |
| std::string platform = ""; | |
| int power_version = -1; | |
| bool has_vsx = false; | |
| powerpc_features() { | |
| unsigned long auxval = getauxval(AT_PLATFORM); | |
| if (auxval) { | |
| platform = std::string(reinterpret_cast<const char*>(auxval)); | |
| // TBD: Do systems exist that return this in uppercase? | |
| if (platform.substr(0, 5) == "power") { | |
| // Extractt a numeric suffix, if one exists | |
| int vpos = -1; | |
| for (int i = platform.length() - 1; i >= 0; i--) { | |
| if (std::isdigit(platform[i])) { | |
| vpos = i; | |
| } else { | |
| break; | |
| } | |
| } | |
| if (vpos > -1) { | |
| power_version = std::stoi(platform.substr(vpos)); | |
| } | |
| } | |
| } | |
| if (power_version >= 9) { | |
| has_vsx = true; | |
| } | |
| } | |
| }; | |
| static int ggml_backend_cpu_powerpc_score() { | |
| int score = 1; | |
| powerpc_features pf; | |
| // Platform scores | |
| if (pf.power_version < 7) { return 0; } | |
| score += 1<<1; | |
| if (pf.power_version < 8) { return 0; } | |
| score += 1<<2; | |
| if (pf.power_version < 9) { return 0; } | |
| score += 1<<3; | |
| if (pf.power_version < 10) { return 0; } | |
| score += 1<<4; | |
| if (pf.power_version < 11) { return 0; } | |
| score += 1<<5; | |
| // Feature scores | |
| if (!pf.has_vsx) { return 0; } | |
| score += 1<<6; | |
| return score; | |
| } | |
| GGML_BACKEND_DL_SCORE_IMPL(ggml_backend_cpu_powerpc_score) | |