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
| import os | |
| import re | |
| import argparse | |
| def expand_includes(shader, input_dir): | |
| """ | |
| Replace #include "file" lines in the text with the contents of that file. | |
| Searches for files relative to input_dir. | |
| """ | |
| include_pattern = re.compile(r'^\s*#include\s+"([^"]+)"\s*$', re.MULTILINE) | |
| def replacer(match): | |
| fname = match.group(1) | |
| file_path = os.path.join(input_dir, fname) | |
| if not os.path.exists(file_path): | |
| raise FileNotFoundError(f"Included file not found: {file_path}") | |
| with open(file_path, "r", encoding="utf-8") as f: | |
| included_code = f.read() | |
| # Recursively expand includes inside the included file | |
| return expand_includes(included_code, input_dir) | |
| return include_pattern.sub(replacer, shader) | |
| def chunk_shader(shader_code, max_chunk_len=60000): | |
| """Split shader_code into safe raw-string sized chunks.""" | |
| return [shader_code[i : i + max_chunk_len] for i in range(0, len(shader_code), max_chunk_len)] | |
| def raw_delim(shader_code): | |
| """Pick a raw-string delimiter that does not appear in the shader.""" | |
| delim = "wgsl" | |
| while f"){delim}\"" in shader_code: | |
| delim += "_x" | |
| return delim | |
| def write_shader(shader_name, shader_code, output_dir, outfile, input_dir): | |
| shader_code = expand_includes(shader_code, input_dir) | |
| if output_dir: | |
| wgsl_filename = os.path.join(output_dir, f"{shader_name}.wgsl") | |
| with open(wgsl_filename, "w", encoding="utf-8") as f_out: | |
| f_out.write(shader_code) | |
| delim = raw_delim(shader_code) | |
| chunks = chunk_shader(shader_code) | |
| if len(chunks) == 1: | |
| outfile.write(f'const char* wgsl_{shader_name} = R"{delim}({shader_code}){delim}";\n\n') | |
| else: | |
| for idx, chunk in enumerate(chunks): | |
| outfile.write(f'static const char wgsl_{shader_name}_part{idx}[] = R"{delim}({chunk}){delim}";\n\n') | |
| outfile.write(f'static const std::string& wgsl_{shader_name}_str() {{\n') | |
| outfile.write(' static const std::string s = []{\n') | |
| outfile.write(' std::string tmp;\n') | |
| outfile.write(f' tmp.reserve({len(shader_code)});\n') | |
| for idx in range(len(chunks)): | |
| outfile.write(f' tmp.append(wgsl_{shader_name}_part{idx});\n') | |
| outfile.write(' return tmp;\n') | |
| outfile.write(' }();\n') | |
| outfile.write(' return s;\n') | |
| outfile.write('}\n') | |
| outfile.write(f'const char* wgsl_{shader_name} = wgsl_{shader_name}_str().c_str();\n\n') | |
| def main(): | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument("--input_dir", required=True) | |
| parser.add_argument("--output_file", required=True) | |
| args = parser.parse_args() | |
| with open(args.output_file, "w", encoding="utf-8") as out: | |
| out.write("// Auto-generated shader embedding\n") | |
| out.write("#include <string>\n\n") | |
| for fname in sorted(os.listdir(args.input_dir)): | |
| if fname.endswith(".wgsl"): | |
| shader_path = os.path.join(args.input_dir, fname) | |
| shader_name = fname.replace(".wgsl", "") | |
| with open(shader_path, "r", encoding="utf-8") as f: | |
| shader_code = f.read() | |
| write_shader(shader_name, shader_code, None, out, args.input_dir) | |
| if __name__ == "__main__": | |
| main() | |