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.
- 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
- 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
- Atomic Chat
File size: 8,013 Bytes
8efb28e | 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 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 | #!/usr/bin/env bash
# make sure we are in the right directory
SCRIPT_DIR=$( cd -- "$( dirname -- "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )
cd $SCRIPT_DIR
#export LLAMA_CACHE="$SCRIPT_DIR/tmp"
set -eux
mkdir -p $SCRIPT_DIR/output
PROJ_ROOT="$SCRIPT_DIR/../.."
cd $PROJ_ROOT
export MTMD_TEST_RESPONSE_MARKER="<MTMD_TEST_RESPONSE_MARKER>"
# Check if the first argument is "big", then run test with big models
# This is useful if we're running the script on a larger machine, so we can test the big models
RUN_BIG_TESTS=false
if [ "${1:-}" = "big" ]; then
RUN_BIG_TESTS=true
echo "Include BIG models..."
fi
RUN_HUGE_TESTS=false
if [ "${1:-}" = "huge" ]; then
RUN_HUGE_TESTS=true
RUN_BIG_TESTS=true
echo "Include BIG and HUGE models..."
fi
USE_VIDEO=false
if [ "${1:-}" = "video" ]; then
USE_VIDEO=true
echo "Using video as input..."
# behavior of USE_VIDEO:
# do NOT check if the output contains "new york", only verify if the exit code is 0
# when printing the result, print the OK/FAIL line then print the generated text
fi
# Check if the second argument is "flash", then enable flash attention
# This is useful to test if flash attention off works correctly
FLASH_ATTN="on"
if [ "${2:-}" = "flash_off" ] || [ "${1:-}" = "flash_off" ]; then
FLASH_ATTN="off"
echo "Flash attention disabled..."
fi
###############
arr_prefix=()
arr_hf=()
arr_extra_args=()
arr_file=()
add_test_vision() {
local hf=$1
shift
local extra_args=""
if [ $# -gt 0 ]; then
extra_args=$(printf " %q" "$@")
fi
if [ "$USE_VIDEO" = true ]; then
arr_file+=("test-3.mp4")
else
arr_file+=("test-1.jpeg")
fi
arr_prefix+=("[vision]")
arr_hf+=("$hf")
arr_extra_args+=("$extra_args")
}
add_test_audio() {
if [ "$USE_VIDEO" = true ]; then
return 0
fi
local hf=$1
shift
local extra_args=""
if [ $# -gt 0 ]; then
extra_args=$(printf " %q" "$@")
fi
arr_prefix+=("[audio] ")
arr_hf+=("$hf")
arr_extra_args+=("$extra_args")
arr_file+=("test-2.mp3")
}
add_test_vision "ggml-org/SmolVLM-500M-Instruct-GGUF:Q8_0"
add_test_vision "ggml-org/SmolVLM2-2.2B-Instruct-GGUF:Q4_K_M"
add_test_vision "ggml-org/SmolVLM2-500M-Video-Instruct-GGUF:Q8_0"
add_test_vision "ggml-org/gemma-3-4b-it-GGUF:Q4_K_M"
add_test_vision "THUDM/glm-edge-v-5b-gguf:Q4_K_M" -p "name of the newspaper?<__media__>"
add_test_vision "second-state/Llava-v1.5-7B-GGUF:Q2_K" --chat-template vicuna
add_test_vision "cjpais/llava-1.6-mistral-7b-gguf:Q3_K_M" --chat-template vicuna
add_test_vision "ibm-research/granite-vision-3.2-2b-GGUF:Q4_K_M"
add_test_vision "second-state/MiniCPM-Llama3-V-2_5-GGUF:Q2_K" # model from openbmb is corrupted
add_test_vision "openbmb/MiniCPM-V-2_6-gguf:Q2_K"
add_test_vision "openbmb/MiniCPM-o-2_6-gguf:Q4_0"
add_test_vision "bartowski/Qwen2-VL-2B-Instruct-GGUF:Q4_K_M"
add_test_vision "ggml-org/Qwen2.5-VL-3B-Instruct-GGUF:Q4_K_M"
add_test_vision "ggml-org/InternVL2_5-1B-GGUF:Q8_0"
add_test_vision "ggml-org/InternVL3-1B-Instruct-GGUF:Q8_0"
add_test_vision "ggml-org/Qwen2.5-Omni-3B-GGUF:Q4_K_M"
add_test_vision "ggml-org/LFM2-VL-450M-GGUF:Q8_0"
add_test_vision "ggml-org/granite-docling-258M-GGUF:Q8_0"
add_test_vision "ggml-org/LightOnOCR-1B-1025-GGUF:Q8_0"
add_test_vision "ggml-org/DeepSeek-OCR-GGUF:Q8_0" -p "Free OCR." --chat-template deepseek-ocr
add_test_vision "ggml-org/dots.ocr-GGUF:Q8_0" -p "OCR"
add_test_vision "ggml-org/HunyuanOCR-GGUF:Q8_0" -p "OCR"
add_test_vision "ggml-org/gemma-4-E2B-it-GGUF:Q8_0" --jinja
add_test_audio "ggml-org/ultravox-v0_5-llama-3_2-1b-GGUF:Q8_0"
add_test_audio "ggml-org/Qwen2.5-Omni-3B-GGUF:Q4_K_M"
add_test_audio "ggml-org/Voxtral-Mini-3B-2507-GGUF:Q4_K_M"
add_test_audio "ggml-org/LFM2-Audio-1.5B-GGUF:Q8_0"
add_test_audio "ggml-org/gemma-4-E2B-it-GGUF:Q8_0" --jinja
add_test_audio "ggml-org/Qwen3-ASR-0.6B-GGUF:Q8_0"
# to test the big models, run: ./tests.sh big
if [ "$RUN_BIG_TESTS" = true ]; then
add_test_vision "ggml-org/pixtral-12b-GGUF:Q4_K_M"
add_test_vision "ggml-org/Mistral-Small-3.1-24B-Instruct-2503-GGUF" --chat-template mistral-v7
add_test_vision "ggml-org/Qwen2-VL-2B-Instruct-GGUF:Q4_K_M"
add_test_vision "ggml-org/Qwen2-VL-7B-Instruct-GGUF:Q4_K_M"
add_test_vision "ggml-org/Qwen2.5-VL-3B-Instruct-GGUF:Q4_K_M"
add_test_vision "ggml-org/Qwen2.5-VL-7B-Instruct-GGUF:Q4_K_M"
add_test_vision "ggml-org/Qwen3-VL-2B-Instruct-GGUF:Q8_0"
add_test_vision "ggml-org/InternVL3-8B-Instruct-GGUF:Q4_K_M"
add_test_vision "ggml-org/InternVL3-14B-Instruct-GGUF:Q4_K_M"
add_test_vision "ggml-org/Qwen2.5-Omni-7B-GGUF:Q4_K_M"
# add_test_vision "ggml-org/Qwen2.5-VL-32B-Instruct-GGUF:Q4_K_M" # does not work on my mac M3 Ultra
# add_test_vision "ggml-org/Kimi-VL-A3B-Thinking-2506-GGUF:Q4_K_M" # not always working
add_test_vision "ggml-org/GLM-4.6V-Flash-GGUF:Q4_K_M" -p "extract all texts from this image"
add_test_audio "ggml-org/ultravox-v0_5-llama-3_1-8b-GGUF:Q4_K_M"
add_test_audio "ggml-org/Qwen2.5-Omni-7B-GGUF:Q4_K_M"
fi
# to test the huge models, run: ./tests.sh huge
# this will run both the big and huge models
# huge models are > 32B parameters
if [ "$RUN_HUGE_TESTS" = true ]; then
add_test_vision "ggml-org/Qwen2.5-VL-72B-Instruct-GGUF:Q4_K_M"
add_test_vision "ggml-org/Llama-4-Scout-17B-16E-Instruct-GGUF:IQ1_S"
fi
# these models always give the wrong answer, not sure why
# add_test_vision "ggml-org/SmolVLM-Instruct-GGUF:Q4_K_M"
# add_test_vision "ggml-org/SmolVLM-256M-Instruct-GGUF:Q8_0"
# add_test_vision "ggml-org/SmolVLM2-256M-Video-Instruct-GGUF:Q8_0"
# this model has broken chat template, not usable
# add_test_vision "cmp-nct/Yi-VL-6B-GGUF:Q5_K"
# add_test_vision "guinmoon/MobileVLM-3B-GGUF:Q4_K_M" "deepseek"
###############
cmake --build build -j --target llama-mtmd-cli
arr_res=()
for i in "${!arr_hf[@]}"; do
bin="llama-mtmd-cli"
prefix="${arr_prefix[$i]}"
hf="${arr_hf[$i]}"
extra_args="${arr_extra_args[$i]}"
inp_file="${arr_file[$i]}"
echo "Running test with binary: $bin and HF model: $hf"
echo ""
echo ""
cmd="$(printf %q "$PROJ_ROOT/build/bin/$bin") \
-hf $(printf %q "$hf") \
--image $(printf %q "$SCRIPT_DIR/$inp_file") \
--temp 0 -n 128 \
--flash-attn $(printf %q "$FLASH_ATTN") \
${extra_args}"
# if extra_args does not contain -p, we add a default prompt
if ! [[ "$extra_args" =~ "-p" ]]; then
cmd+=" -p \"what is the publisher name of the newspaper?\""
fi
exit_code=0
output=$(eval "$cmd" 2>&1 | tee /dev/tty) || exit_code=$?
echo "$output" > $SCRIPT_DIR/output/$bin-$(echo "$hf" | tr '/' '-').log
if [ "$USE_VIDEO" = true ]; then
# for video, only check exit code; do not grep for "new york"
if [ $exit_code -eq 0 ]; then
result="$prefix \033[32mOK\033[0m: $hf"
else
result="$prefix \033[31mFAIL\033[0m: $hf"
fi
# append generated text (after the response marker)
generated_text=$(echo "$output" | sed "1,/${MTMD_TEST_RESPONSE_MARKER}/d" | tail -10)
if [ -n "$generated_text" ]; then
result+="\n$generated_text"
fi
echo -e "$result"
else
# either contains "new york" or both "men" and "walk"
if echo "$output" | grep -iq "new york" \
|| (echo "$output" | grep -iq "men" && echo "$output" | grep -iq "walk")
then
result="$prefix \033[32mOK\033[0m: $hf"
else
result="$prefix \033[31mFAIL\033[0m: $hf"
fi
echo -e "$result"
fi
arr_res+=("$result")
echo ""
echo ""
echo ""
echo "#################################################"
echo "#################################################"
echo ""
echo ""
done
set +x
for i in "${!arr_res[@]}"; do
echo -e "${arr_res[$i]}"
done
echo ""
echo "Output logs are saved in $SCRIPT_DIR/output"
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