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: 4,219 Bytes
1debbd5 | 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 | #!/bin/bash
# MIT license
# Copyright (C) 2024 Intel Corporation
# SPDX-License-Identifier: MIT
Help() {
cat << EOF
Usage: $(basename "$0") [OPTIONS]
This script processes files with specified options.
Options:
-h, --help Display this help message and exit.
-c, --context <value> Set context length. Bigger need more memory.
-p, --promote <value> Prompt to start generation with.
-m, --model <value> Full model file path.
-mg,--main-gpu <value> Set main GPU ID (0 - n) for single GPU mode.
-sm,--split-mode <value> How to split the model across multiple GPUs, one of:
- none: use one GPU only
- layer (default): split layers and KV across GPUs
- row: split rows across GPUs
-ngl,--n-gpu-layers <value> Max. number of layers to store in VRAM (default: -1)
-lv,--log-verbosity <value> Set the verbosity threshold. Messages with a higher verbosity will be
ignored. Values:
- 0: generic output
- 1: error
- 2: warning
- 3: info
- 4: debug
EOF
}
BIN_FILE=./build/bin/llama-completion
SEED=0
GPUS_SETTING=""
INPUT_PROMPT="Building a website can be done in 10 simple steps:\nStep 1:"
MODEL_FILE=../models/llama-2-7b.Q4_0.gguf
NGL=99
CONTEXT=4096
GGML_SYCL_DEVICE=-1
SPLIT_MODE=layer
LOG_VERBOSE=3
while [[ $# -gt 0 ]]; do
case "$1" in
-c|--context)
CONTEXT=$2
# Shift twice to consume both the option flag and its value
shift
shift
;;
-p|--promote)
# Option that is a simple flag (boolean)
INPUT_PROMPT="$2"
# Shift once to consume the option flag
shift
shift
;;
-m|--model)
MODEL_FILE="$2"
# Shift twice to consume both the option flag and its value
shift
shift
;;
-mg|--main-gpu)
GGML_SYCL_DEVICE=$2
SPLIT_MODE=none
# Shift twice to consume both the option flag and its value
shift
shift
;;
-sm|--split-mode)
SPLIT_MODE=$2
# Shift twice to consume both the option flag and its value
shift
shift
;;
-ngl|--n-gpu-layers)
NGL=$2
# Shift twice to consume both the option flag and its value
shift
shift
;;
-lv|--log-verbosity)
LOG_VERBOSE=$2
# Shift twice to consume both the option flag and its value
shift
shift
;;
-h|--help)
Help
exit 0
;;
*)
# Handle unknown options or stop processing options
echo "Invalid option: $1"
# Optional: exit script or shift to treat remaining as positional args
exit 1
;;
esac
done
source /opt/intel/oneapi/setvars.sh
#export GGML_SYCL_DEBUG=1
#ZES_ENABLE_SYSMAN=1, Support to get free memory of GPU by sycl::aspect::ext_intel_free_memory. Recommended to use when --split-mode = layer.
#support malloc device memory more than 4GB.
export UR_L0_ENABLE_RELAXED_ALLOCATION_LIMITS=1
echo "UR_L0_ENABLE_RELAXED_ALLOCATION_LIMITS=${UR_L0_ENABLE_RELAXED_ALLOCATION_LIMITS}"
if [ $GGML_SYCL_DEVICE -ne -1 ]; then
echo "Use $GGML_SYCL_DEVICE as main GPU"
#use signle GPU only
GPUS_SETTING="-mg $GGML_SYCL_DEVICE -sm ${SPLIT_MODE}"
echo "ONEAPI_DEVICE_SELECTOR=${ONEAPI_DEVICE_SELECTOR}"
else
echo "Use all Intel GPUs, including iGPU & dGPU"
GPUS_SETTING="-sm ${SPLIT_MODE}"
fi
echo "run cmd: ZES_ENABLE_SYSMAN=1 ${BIN_FILE} -m ${MODEL_FILE} -no-cnv -p "${INPUT_PROMPT}" -n 200 -e -ngl ${NGL} -s ${SEED} -c ${CONTEXT} ${GPUS_SETTING} -lv ${LOG_VERBOSE} --mmap "
ZES_ENABLE_SYSMAN=1 ${BIN_FILE} -m ${MODEL_FILE} -no-cnv -p "${INPUT_PROMPT}" -n 200 -e -ngl ${NGL} -s ${SEED} -c ${CONTEXT} ${GPUS_SETTING} -lv ${LOG_VERBOSE} --mmap
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