Tracked topic
…from nanovllm import LLM, SamplingParams llm = LLM( "/path/to/model" , enforce_eager= True , tensor_parallel_size= 1 ) sampling_params = SamplingParams(temperature= 0.6 , max_tokens= 256 ) outputs = llm.generate([ "Hello, Nano-vLLM…
…AI-generated summary Diffusion language models (DLMs) have recently emerged as a promising alternative to autoregressive models , primarily due to their ability to enable parallel decoding . Despite this advantage, most existing DLMs…
…ClawGym-Agents — SFT on black-box rollouts + lightweight RL pipeline parallelizing rollouts across per-task sandboxes 3️⃣ Eval : ClawGym-Bench — 200 instances, auto-filtered & human-LLM reviewed, calibrated for trust Resources dropping…
…cache:/opt/nim/.cache \ -v $(pwd):$(pwd) \ -e HF_TOKEN=$HF_TOKEN \ -e NIM_TENSOR_PARALLEL_SIZE=1 \ -e NIM_MODEL_NAME=$MODEL \ -e NGC_API_KEY \ -e NIM_RELAX_MEM_CONSTRAINTS…
…Pre-launch testing exposed failures that text-only benchmarks rarely measure, including fabricated trading rules, fee paralysis, numeric anchoring, cadence trading, and misread tokenomics. Targeted harness changes reduced fabricated sell rules from…
…We further implement UniPrefill as a continuous batching operator and extend vLLM 's scheduling strategy to natively support prefill-decode co-processing and tensor parallel for UniPrefill, enabling its seamless integration into…
…Memory-Efficient Parallel Token Generation via Dual-View Diffusion (2026) MindVLA-U1: VLA Beats VA with Unified Streaming Architecture for Autonomous Driving (2026) PSD: Pushing the Pareto Frontier of Diffusion LLMs via…
…OpenMP parallelisation of the training loop yields 1.9x speedup on 4 cores. The system is implemented in pure C with AVX2 SIMD and processes approximately 2,000 tokens per second on…
…This paper introduces ESamp , a test-time sampling algorithm that helps LLMs generate multiple semantically diverse responses in parallel, rather than merely producing surface-level variations of the same idea. The key…
…Architecturally, NTM combines shallow invertible blocks within each step with a deep parallel predictor across the trajectory, forming an end-to-end network trainable from scratch or initializable from pretrained flow-matching…