ling-lang

[package]
name = "ling-ai"
version = "2030.0.10"
edition = "2024"
authors = ["Ling Lin <taellinglin@gmail.com>", "Sanny Lin <SannyLing53@gmail.com>"]
description = "Tensor computation, neural networks, and LLM inference"
license = "Apache-2.0 OR MIT"
readme = "README.md"
repository = "https://github.com/taellinglin/ling"

[features]
default = []
cpu = ["dep:candle-core", "burn"]
cuda = ["candle-core/cuda", "candle-transformers/cuda", "dfdx/cuda"]
# CUDA for the chat-completion engine only — decoupled from `cuda` above
# because that one also builds dfdx's CUDA kernels, which need a full MSVC
# dev-prompt environment (cl.exe on PATH + INCLUDE/LIB) that a plain build
# shell may not have. This needs only candle's own CUDA backend.
llm-cuda = ["llm", "candle-core/cuda", "candle-transformers/cuda"]
metal = ["candle-core/metal"]
tensorrt = []
llm = ["dep:candle-transformers", "dep:tokenizers", "dep:candle-core", "dep:candle-nn", "dep:serde_json", "dep:zip"]
# Image understanding (Moondream2, quantized) for chat.ling-lang.org's
# attach-an-image flow — UI screenshots and 3D/game screenshots. Shares the
# same candle-transformers/tokenizers stack as `llm`; adds only image
# decoding (the `image` crate) on top.
vision = ["llm", "dep:image"]
nlp = ["dep:rust-bert"]
cnn = ["dep:dfdx"]
rnn = []
training = []

[dependencies]
ling-core = { workspace = true }

# Tensor computation
candle-core = { version = "0.11", optional = true, default-features = false }
burn = { version = "0.14", optional = true, default-features = false }
tract = { version = "0.21", optional = true }  # ONNX runtime
dfdx = { version = "0.13", optional = true }

# Neural network layers
candle-nn = { version = "0.11", optional = true }
burn-nn = { version = "0.21.0-pre.5", optional = true }

# LLM inference — in-process chat completion engine (Milestone 2). Loads a
# GGUF checkpoint via candle-transformers' quantized model implementations
# (CUDA-capable) and a HF `tokenizers` vocab; no external server involved.
candle-transformers = { version = "0.11", optional = true, default-features = false }
tokenizers = { version = "0.23", optional = true, default-features = false, features = ["fancy-regex"] }
serde_json = { version = "1.0", optional = true }
# reading RVC / HuBERT .pth checkpoints (the config list next to the weights); the same version candle-core uses
zip = { version = "8.6", optional = true, default-features = false }
image = { version = "0.25", optional = true, default-features = false, features = ["png", "jpeg"] }
# llama-cpp-rs = { version = "0.1", optional = true, features = ["cuda"] }
# mistralrs currently disabled to prevent pulling reqwest/web-sys on non-wasm builds
# (unblocks workspace compilation for the `ling` binary)
# mistralrs = { version = "0.7.0-alpha.4", optional = true, default-features = false, features = [] }

# phi-rs = { version = "0.2", optional = true }

# NLP
rust-bert = { version = "0.21", optional = true }
# sentence-transformers = { version = "0.3", optional = true }

# Computer Vision
# candle-cnn = { version = "0.6", optional = true }

# RNN layers
# candle-rnn = { version = "0.6", optional = true }

# Training
# candle-training = { version = "0.6", optional = true }
# burn-autodiff = { version = "0.14", optional = true }
# dfdx-training = { version = "0.13", optional = true }

# Data handling
ndarray = "0.15"
rand = "0.8"
num-traits = "0.2"

[lib]
name = "ling_ai"
path = "src/lib.rs"

# [[example]]
# name = "mnist"
# required-features = ["cpu"]

# [[example]]
# name = "llm_chat"
# required-features = ["llm"]