PowerMoE-3b

Model · Text generation

MarinSkyRL-Open-MOPD-SmolLM3-3B-step-32

Open Athena

This is a MarinSkyRL-native Open-MOPD student after 32 optimizer steps. It starts from the authors' mixed-domain SFT checkpoint. Student responses were scored by the authors' math, code, and instruction-following RL teachers, routed by domain. The objective uses the student's selected top-16 token IDs and a clipped policy surrogate. This is an early checkpoint, not the authors' step-200 final model. The checkpoint is an unquantized, six-file Hugging Face export of the durable MarinSkyRL globalstep32 FSDP2 checkpoint. The policy export was used for the independent step-32 evaluation. The export's model.safetensors SHA-256 is bb7326640142069bc2e1fba5f54f15e0cccb1ff861f34f318b372eaab7abaf4b.…

Model · Text generation

prism-roleplay-1.5-small-fast

Vertex AGI

The Prism Roleplay 1.5 Small recipe on PrismML's 1-bit Bonsai-8B: a standalone model, faster and smaller than the 4-bit original, with a small quality gap A standalone model, not an adapter: load it directly with mlx-lm. It is Prism Roleplay 1.5 Small's training recipe (same data, hyperparameters and system prompt) applied to prism-ml/Bonsai-8B-mlx-1bit, a 1-bit (g128) build of Qwen3-8B. How the LoRA was merged into a 1-bit model. A plain merge re-quantizes every layer to 1 bit, and the small LoRA update disappears in the rounding (we measured it: merged held-out loss 3.415 = untuned base 3.405). So the 16 layers the LoRA touched (all their attention and MLP projections) are merged at fp16…

This is the model card of a transformers model that has been pushed on the Hub. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Use the code below to get started with the model. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

Model · Text generation

gaggle-nanochat-pretrained-climbmix-20260924

Castorini

A 3.3B-parameter decoder-only base language model, pre-trained from scratch with nanochat on one pass over ClimbMix (287B tokens). This is the causal model described in the pre-training section of (PDF). It has had no post-training (no instruction tuning or RLHF), so it continues text rather than following instructions. The same weights load three ways: with transformers (trustremotecode=True), with vLLM (through a small plugin), and with nanochat's own code from the original checkpoint files, which are unchanged. one pass of 287B tokens. The last of its 6,543 shards is held out for validation. Documents are packed into 2,048-token sequences with nanochat's BOS-aligned best-fit packing.…

Bonsai 2 27B by Prism ML, repacked for vLLM. Unofficial; not affiliated with Prism ML. It needs the prismternary vLLM plugin from fraserprice/bonsai-vllm, which has the run command, kernels and throughput numbers: Built and tested for the RTX PRO 6000 Blackwell only; other NVIDIA GPUs are untested. Problems: open an issue. - The ternary weights of the MLX pack, bit for bit: the same 2-bit codes and FP16 group scales (g128), in the same Hadamard-rotated basis. MLX's redundant per-group biases (-scale) are dropped, and the rotation signs move into config.json. - The embedding table is dequantized to BF16. - Norms and the linear-attention state path are Prism ML's own tensors from the MLX…

Model · Text generation

Llama-3.2-3B-Instruct

Meta Llama

The Llama 3.2 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction-tuned generative models in 1B and 3B sizes (text in/text out). The Llama 3.2 instruction-tuned text only models are optimized for multilingual dialogue use cases, including agentic retrieval and summarization tasks. They outperform many of the available open source and closed chat models on common industry benchmarks. Model Architecture: Llama 3.2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for…