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 | |
| # Get the directory where this script is located | |
| SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" | |
| echo "=== llama-server-simulator Test Script ===" | |
| echo "" | |
| PORT=8033 | |
| SUCCESS_RATE=0.8 | |
| TEST_PORT=8034 | |
| echo "Starting simulator on port $PORT with success rate $SUCCESS_RATE..." | |
| source "$SCRIPT_DIR/venv/bin/activate" | |
| python3 "$SCRIPT_DIR/llama-server-simulator.py" --port $PORT --success-rate $SUCCESS_RATE > /tmp/simulator-test.log 2>&1 & | |
| SIMULATOR_PID=$! | |
| echo "Waiting for simulator to start..." | |
| sleep 5 | |
| # Helper function to make a request and extract the answer | |
| make_request() { | |
| local question="$1" | |
| curl -s -X POST http://localhost:$PORT/v1/chat/completions \ | |
| -H "Content-Type: application/json" \ | |
| -d "{ | |
| \"model\": \"llama\", | |
| \"messages\": [ | |
| {\"role\": \"user\", \"content\": \"$question\"} | |
| ], | |
| \"temperature\": 0, | |
| \"max_tokens\": 2048 | |
| }" | python3 -c "import sys, json; data = json.load(sys.stdin); print(data.get('choices', [{}])[0].get('message', {}).get('content', data.get('error', 'No response')))" | |
| } | |
| # Test question (repeated in multiple tests) | |
| TEST_QUESTION="Quadratic polynomials P(x) and Q(x) have leading coefficients 2 and -2, respectively. The graphs of both polynomials pass through the two points (16,54) and (20,53). Find P(0) + Q(0)." | |
| echo "" | |
| echo "=== Test 1: Correct Answer ===" | |
| echo "Sending request with known question..." | |
| answer=$(make_request "$TEST_QUESTION") | |
| echo "Answer: $answer" | |
| echo "Expected: 116" | |
| echo "Correct: $([ "$answer" == "116" ] && echo "Yes" || echo "No")" | |
| echo "" | |
| echo "=== Test 2: Wrong Answer ===" | |
| echo "Sending request with known question (success rate 0.0)..." | |
| answer=$(make_request "$TEST_QUESTION") | |
| echo "Answer: $answer" | |
| echo "Expected: 116" | |
| echo "Correct: $([ "$answer" == "116" ] && echo "Yes" || echo "No")" | |
| echo "" | |
| echo "=== Test 3: No Matching Question ===" | |
| echo "Sending request with non-matching text..." | |
| response=$(make_request "What is the capital of France?") | |
| echo "Response: $response" | |
| echo "Expected: No matching question found" | |
| echo "Correct: $([ "$response" == "No matching question found" ] && echo "Yes" || echo "No")" | |
| echo "" | |
| echo "=== Test 4: Success Rate Verification ===" | |
| echo "Sending 10 requests to test success rate..." | |
| correct_count=0 | |
| for i in {1..10}; do | |
| answer=$(make_request "$TEST_QUESTION") | |
| if [ "$answer" == "116" ]; then | |
| correct_count=$((correct_count + 1)) | |
| fi | |
| echo " Request $i: Answer = $answer" | |
| done | |
| echo "Correct answers: $correct_count/10" | |
| echo "Expected: ~8/10 (80% success rate)" | |
| echo "Success rate: $(echo "scale=1; $correct_count * 10" | bc)%" | |
| echo "" | |
| echo "=== Test Complete ===" | |
| echo "Stopping simulator..." | |
| kill $SIMULATOR_PID 2>/dev/null | |
| wait $SIMULATOR_PID 2>/dev/null || true | |
| echo "Simulator stopped." | |