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
| set -e | |
| # Parse command line arguments | |
| MODEL_PATH="" | |
| MODEL_NAME="" | |
| PROMPTS_FILE="" | |
| # First argument is always model path | |
| if [ $# -gt 0 ] && [[ "$1" != --* ]]; then | |
| MODEL_PATH="$1" | |
| shift | |
| fi | |
| # Parse remaining arguments | |
| while [[ $# -gt 0 ]]; do | |
| case $1 in | |
| --prompts-file|-pf) | |
| PROMPTS_FILE="$2" | |
| shift 2 | |
| ;; | |
| *) | |
| # If MODEL_NAME not set and this isn't a flag, use as model name | |
| if [ -z "$MODEL_NAME" ] && [[ "$1" != --* ]]; then | |
| MODEL_NAME="$1" | |
| fi | |
| shift | |
| ;; | |
| esac | |
| done | |
| # Set defaults | |
| MODEL_PATH="${MODEL_PATH:-"$EMBEDDING_MODEL_PATH"}" | |
| MODEL_NAME="${MODEL_NAME:-$(basename "$MODEL_PATH")}" | |
| CONVERTED_MODEL_PATH="${CONVERTED_EMBEDDING_PATH:-"$CONVERTED_EMBEDDING_MODEL"}" | |
| CONVERTED_MODEL_NAME="${CONVERTED_MODEL_NAME:-$(basename "$CONVERTED_MODEL_PATH" .gguf)}" | |
| if [ -t 0 ]; then | |
| CPP_EMBEDDINGS="data/llamacpp-${CONVERTED_MODEL_NAME}-embeddings.bin" | |
| else | |
| # Process piped JSON data and convert to binary (matching logits.cpp format) | |
| TEMP_FILE=$(mktemp /tmp/tmp.XXXXXX.binn) | |
| python3 -c " | |
| import json | |
| import sys | |
| import struct | |
| data = json.load(sys.stdin) | |
| # Flatten all embeddings completely | |
| flattened = [] | |
| for item in data: | |
| embedding = item['embedding'] | |
| for token_embedding in embedding: | |
| flattened.extend(token_embedding) | |
| print(f'Total embedding values: {len(flattened)}', file=sys.stderr) | |
| # Write as binary floats - matches logitc.cpp fwrite format | |
| with open('$TEMP_FILE', 'wb') as f: | |
| for value in flattened: | |
| f.write(struct.pack('f', value)) | |
| " | |
| CPP_EMBEDDINGS="$TEMP_FILE" | |
| trap "rm -f $TEMP_FILE" EXIT | |
| fi | |
| # Build the semantic_check.py command | |
| SEMANTIC_CMD="python scripts/utils/semantic_check.py --model-path $MODEL_PATH \ | |
| --python-embeddings data/pytorch-${MODEL_NAME}-embeddings.bin \ | |
| --cpp-embeddings $CPP_EMBEDDINGS" | |
| # Add prompts file if specified, otherwise use default prompt | |
| if [ -n "$PROMPTS_FILE" ]; then | |
| SEMANTIC_CMD="$SEMANTIC_CMD --prompts-file \"$PROMPTS_FILE\"" | |
| else | |
| SEMANTIC_CMD="$SEMANTIC_CMD --prompt \"Hello world today\"" | |
| fi | |
| # Execute the command | |
| eval $SEMANTIC_CMD | |