Fallen1-800M

Model · Text generation

gpt2-large

OpenAI community

GPT-2 Large is the 774M parameter version of GPT-2, a transformer-based language model created and released by OpenAI. The model is a pretrained model on English language using a causal language modeling (CLM) objective. - Test the full generation capabilities here: https://transformer.huggingface.co/doc/gpt2-large Use the code below to get started with the model. You can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we Here is how to use this model to get the features of a given text in PyTorch: In their model card about GPT-2, OpenAI wrote: In their model card about GPT-2, OpenAI wrote: In their model card about GPT-2, OpenAI…

Model · Text generation

Qwen3.5-0.8B-Japanese-SFT-v2.5

Atemoya

Qwen/Qwen3.5-0.8B を日本語向けに追加学習した、非公式の派生モデルです。Qwen チームとは関係ありません。v2 の続きで、次の大きな版（v3）に向けた途中の版です。 - 思考モードで理系の問題を解けるようになった：日本語で短く考えてから答えます（MMLU-Pro 理科 4.0% → 36.1%、MGSM 日本語 0.0% → 27.4%。どちらも思考あり・上限 4,096 トークン、複数回の平均） An unofficial Japanese fine-tune of Qwen3.5-0.8B (v2.5). Adds a short Japanese thinking mode (MMLU-Pro science 36.1% (mean of 3 runs), MGSM-ja 27.4% with thinking at a 4,096-token cap; v2: 4.0% / 0.0%), thinking-mode chat, and Japanese code review, while keeping v2's chat quality. Still a small model: it hallucinates facts and is weaker than the original model at long, careful calculation. - GGUF 版（llama.cpp・LM Studio 用）：Takenoko12345678/Qwen3.5-0.8B-Japanese-SFT-v2.5-GGUF…

gpt2-large fine-tuned on CommonGen, used as the base language model for the CommonGen experiments in Mitigating Bias in Locally Constrained Decoding via Tractable Proposals (arXiv:2606.01926). This is a plain causal language model: it supplies the base distribution that GCD and P-GCD steer. The tractable proposal it is paired with is the HMM at which shares its 50257-token vocabulary. configs/common-gen.yaml in github.com/MhDang/gelatwo already points at this checkpoint, so the CommonGen runs need no override.

Model · Text generation

dQwen3.5-0.8B-Base

IFML

A masked diffusion language model adapted from Qwen3.5-0.8B. The backbone is hybrid: only its attention layers are made bidirectional, and the Gated DeltaNet layers stay causal. This is a base model, with no instruction tuning. Paper: dQwen3.5: Hybrid-Attention Diffusion Language Models. Code: https://github.com/AntonXue/dQwen Needs a CUDA GPU and transformers>=5.13 (tested with torch 2.7.1+cu128, flash-linear-attention 0.5.1). generate decodes the whole canvas at once, committing positions above a confidence threshold (tau=0.9); pass blocklength=32 for left-to-right block decoding, or tau=None, stepsperblock=k for a fixed budget. The 50B-token checkpoint from the paper is…

Model · Text generation

szl-receiptagent-qwen35-0.8b-v3

SZL Holdings

This is a separate research bf16 LoRA adapter (r=16, alpha=32) on Qwen/Qwen3.5-0.8B, trained with Unsloth on a laptop Blackwell GPU, with a derived merged artifact also present in the repository. "Placeholder" describes release status, not absence of weights. It does not overwrite ReceiptAgent v2 Sizes were read from the Hub listing; no weight file was downloaded or hashed by this pass, and the revision SHA was not captured. The Hub-hosted banner referenced by the szl-forge card (holo-banner.svg) is not on this tree, so it is not embedded here. The training receipt records 180 training rows and train loss 0.0537. Training loss is not evaluation. The receipt describes the local laptop…

A 0.75B-parameter local model that lets a coding agent hand off routine, read-only repository work. Hydra Nano is the model behind Hydra, a CLI that a frontier coding agent (Claude Code, Codex and others) calls instead of running noisy commands itself. Hydra asks the model to choose from a fixed menu of read-only probes, runs those probes in code, and returns the command's own output lines. The model never writes a shell command. One model serves all five Hydra heads: All figures are end-to-end Hydra results: the task counts as solved only when the probe plan is right and the returned evidence contains every expected fact with nothing forbidden. Errors and timeouts count as failures. Every…