REVIEW 16 cited by
BitNet b1.58 2B4T Technical Report
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
BitNet b1.58 2B4T Technical Report
read the original abstract
We introduce BitNet b1.58 2B4T, the first open-source, native 1-bit Large Language Model (LLM) at the 2-billion parameter scale. Trained on a corpus of 4 trillion tokens, the model has been rigorously evaluated across benchmarks covering language understanding, mathematical reasoning, coding proficiency, and conversational ability. Our results demonstrate that BitNet b1.58 2B4T achieves performance on par with leading open-weight, full-precision LLMs of similar size, while offering significant advantages in computational efficiency, including substantially reduced memory footprint, energy consumption, and decoding latency. To facilitate further research and adoption, the model weights are released via Hugging Face along with open-source inference implementations for both GPU and CPU architectures.
Forward citations
Cited by 16 Pith papers
-
When Can Depth Replace Precision? A Resource Theory of Quantized Neural Computation
For a fixed low-bit residual library, the distance to the closed relaxed reachable set is an exact structural floor that pure depth approaches at O(1/D), while write-back arithmetic can reverse the gain and accuracy m...
-
VitaLLM: A Versatile and Tiny Accelerator for Mixed-Precision LLM Inference on Edge Devices
VitaLLM demonstrates a 16nm silicon prototype accelerator achieving 72.46 tokens/s decode for 3B ternary LLMs in 0.214 mm² area with reduced KV cache traffic via predictive sparse attention.
-
Vec-LUT: Vector Table Lookup for Parallel Ultra-Low-Bit LLM Inference on Edge Devices
Vec-LUT delivers up to 4.2x speedup over prior LUT methods for parallel ultra-low-bit LLM inference on edge devices by unifying lookups across tokens and adding cache-aware tensor layouts.
-
BitNet Text Embeddings
BITEMBED converts LLM backbones to ternary BitNet-style encoders, adapts them with contrastive pre-training and teacher distillation, and produces text embeddings at multiple precisions that perform comparably to full...
-
BitNet Text Embeddings
BITEMBED trains 1.58-bit ternary-weight LLM embedders with contrastive pre-training, supervised distillation, and multi-precision output training, matching FP16 teachers within ~0.6 MMTEB points at ~2x CPU speed.
-
LC-QAT: Data-Efficient 2-Bit QAT for LLMs via Linear-Constrained Vector Quantization
LC-QAT achieves data-efficient 2-bit weight-only QAT for LLMs by representing quantized weights as a learned affine transform over discrete vectors, supporting end-to-end optimization from a high-quality PTQ start.
-
Stochastic Sparse Attention for Memory-Bound Inference
SANTA sparsifies post-softmax value aggregation via stratified sampling of S << n_k indices to produce an unbiased estimator, delivering 1.5x decode attention speedup on RTX 6000 Ada at 32k contexts while matching bas...
-
Stochastic Sparse Attention for Memory-Bound Inference
SANTA replaces full value-cache multiply-accumulates with stochastic gather-and-add sampling from the attention distribution to reduce memory bandwidth while preserving an unbiased estimator.
-
VitaLLM: A Versatile, Ultra-Compact Ternary LLM Accelerator with Dependency-Aware Scheduling
VitaLLM delivers 70.7 tokens/s decoding in a 0.223 mm² TSMC 16 nm chip at 66 mW with a figure-of-merit of 17.4 TOPS/mm²/W by combining TINT cores, BoothFlex attention, leading-one prediction, and dependency-aware scheduling.
-
LBLLM: Lightweight Binarization of Large Language Models via Three-Stage Distillation
LBLLM achieves better accuracy than prior binarization methods for LLMs by decoupling weight and activation quantization through initialization, layer-wise distillation, and learnable activation scaling.
-
Rethinking 1-bit Optimization Leveraging Pre-trained Large Language Models
A progressive training scheme with binary-aware initialization and dual-scaling allows pre-trained LLMs to be converted to high-performance 1-bit models without training from scratch.
-
LC-QAT: Data-Efficient 2-Bit QAT for LLMs via Linear-Constrained Vector Quantization
LC-QAT is a 2-bit weight-only vector quantization aware training framework for LLMs that uses linear-constrained affine mappings to achieve data-efficient optimization and outperform prior QAT methods.
-
Spike-Aware C++ INT8 Inference for Sparse Spiking Language Models on Commodity CPUs
A spike-aware C++ INT8 runtime for sparse spiking LMs delivers 22.63 tokens/s single-thread on Ryzen 7, beating several Q8_0 dense models in llama.cpp while cutting weights from 3.49 GB to 1.06 GB, at the cost of high...
-
On the Expressive Power of Weight Quantization in Large Language Models
Weight-quantized LLMs retain universal approximation up to 1.58 bits with expressive collapse below it and polynomial degradation in capacity as bit count falls.
-
GoldenFloat: A Phi-Derived Static-Split Floating-Point Family from GF4 to GF1024 with a Lucas-Exact Integer Identity
GoldenFloat introduces a phi-derived rule for setting exponent and fraction widths across floating-point formats from 4 to 1024 bits, backed by open RTL generator, Lucas-exact accumulator, and FPGA implementation.
-
ECG Foundation Models and Medical LLMs for Agentic Cardiovascular Intelligence at the Edge: A Review and Outlook
ECG foundation models for signal interpretation and medical LLMs for reasoning can be integrated into agentic systems for real-time cardiovascular intelligence on edge devices.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.