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
File size: 2,207 Bytes
0862f9d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 | #!/usr/bin/env bash
RESULTS="bench-models-results.txt"
: > "$RESULTS"
ARGS_BB="-c 270336 -npp 512,4096,8192 -npl 1,2,4,8,16,32 -ntg 32"
ARGS_B="-d 0,4096,8192,16384,32768 -p 2048 -n 32"
QUICK=0
DIO=0
while (( "$#" )); do
case "$1" in
--quick) QUICK=1; shift ;;
--dio) DIO=1; shift ;;
*) shift ;;
esac
done
if (( QUICK )); then
ARGS_BB="-c 20480 -npp 512,4096 -npl 1,2,4 -ntg 32"
ARGS_B="-d 0 -p 2048 -n 32"
fi
if (( DIO )); then
ARGS_BB="${ARGS_BB} --no-mmap --direct-io"
ARGS_B="${ARGS_B} -mmp 0 -dio 1"
fi
run_model() {
local HFR=$1
local HFF=$2
printf "## ${HFR}\n" | tee -a "$RESULTS"
printf "\n" | tee -a "$RESULTS"
printf "Model: https://huggingface.co/${HFR}\n" | tee -a "$RESULTS"
printf "\n" | tee -a "$RESULTS"
printf -- "- \`llama-batched-bench\`\n" | tee -a "$RESULTS"
printf "\n" | tee -a "$RESULTS"
./bin/llama-batched-bench \
-hfr "${HFR}" -hff "${HFF}" \
-m "${HFF}" -fa 1 -ub 2048 \
${ARGS_BB} | tee -a "$RESULTS"
printf "\n" | tee -a "$RESULTS"
printf -- "- \`llama-bench\`\n" | tee -a "$RESULTS"
printf "\n" | tee -a "$RESULTS"
./bin/llama-bench \
-m "${HFF}" -fa 1 -ub 2048 \
${ARGS_B} | tee -a "$RESULTS"
printf "\n" | tee -a "$RESULTS"
printf "\n"
}
run_model "ggml-org/gpt-oss-20b-GGUF" "gpt-oss-20b-mxfp4.gguf"
run_model "ggml-org/gpt-oss-120b-GGUF" "gpt-oss-120b-mxfp4-00001-of-00003.gguf"
run_model "ggml-org/Qwen3-Coder-30B-A3B-Instruct-Q8_0-GGUF" "qwen3-coder-30b-a3b-instruct-q8_0.gguf"
run_model "ggml-org/Qwen2.5-Coder-7B-Q8_0-GGUF" "qwen2.5-coder-7b-q8_0.gguf"
run_model "ggml-org/gemma-3-4b-it-qat-GGUF" "gemma-3-4b-it-qat-Q4_0.gguf"
run_model "ggml-org/GLM-4.7-Flash-GGUF" "GLM-4.7-Flash-Q8_0.gguf"
if [[ -f models-extra.txt ]]; then
while read -r HFR HFF; do
[[ -z "$HFR" ]] && continue
run_model "$HFR" "$HFF"
done < models-extra.txt
fi
printf "\n=====================================\n"
printf "\n"
cat "$RESULTS"
printf "\n"
printf "Done! Results are written to $RESULTS\n"
printf "\n"
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