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
| /** | |
| * Model load progress helpers for the /models/sse surfaces | |
| * (selector row and chat message). | |
| */ | |
| import { MODEL_LOAD_STAGE_LABELS, MODEL_LOAD_TAIL_SHARE } from '$lib/constants'; | |
| /** | |
| * Human label for a model load stage. | |
| */ | |
| export function modelLoadStageLabel(stage: ApiModelLoadStage): string { | |
| return MODEL_LOAD_STAGE_LABELS[stage]; | |
| } | |
| /** | |
| * Overall load fraction (0.0 -> 1.0) across the declared stage plan. | |
| * text_model fills [0, 1 - tail], each later phase owns one tail slice. | |
| */ | |
| export function modelLoadFraction(progress: ModelLoadProgress | null): number { | |
| if (!progress) return 0; | |
| const { stages, current, value } = progress; | |
| const tailCount = Math.max(stages.length - 1, 0); | |
| const textCeiling = 1 - tailCount * MODEL_LOAD_TAIL_SHARE; | |
| const idx = stages.indexOf(current); | |
| if (idx <= 0) { | |
| return value * textCeiling; | |
| } | |
| return textCeiling + (idx - 1 + value) * MODEL_LOAD_TAIL_SHARE; | |
| } | |
| /** | |
| * Single line describing load progress: active stage label and overall percent. | |
| * Returns null when there is no progress to show. | |
| */ | |
| export function modelLoadProgressText(progress: ModelLoadProgress | null): string | null { | |
| if (!progress) return null; | |
| const label = modelLoadStageLabel(progress.current); | |
| return `${label} ${Math.round(modelLoadFraction(progress) * 100)}%`; | |
| } | |