Open-Weight Models

Model

my-embodied-ai

Marcin Nowak

This is an exploratory note for Embodied AI. It records the intended comparison, likely confounders, and reproducibility requirements before any benchmark result is reported. - the scope of the research question and likely confounders - a proposed comparison with matched baselines - concrete evaluation context such as task-appropriate public benchmarks named in the main note - reproducibility checks, failure modes, and open questions - topic-relevant references Start with summary.md for the full note. Sections labeled as plans or hypotheses should not be interpreted as experimental results. If results are added later, they should include dataset versions, commands, seeds, hardware, and raw…

A merged (LoRA-finetuned) variant of Qwen/Qwen2.5-1.5B-Instruct, fine-tuned for Cisco-related assistant tasks. This repository contains full merged weights — not a PEFT adapter. Load it directly with transformers; no peft and no adapter merging step is required. The model is tagged endpointscompatible and can be deployed directly to for a dedicated, OpenAI-compatible /v1/chat/completions API. Direct use: [e.g. answering Cisco product, configuration, and troubleshooting questions.] Out of scope: This model is not production-grade for safety- or compliance-critical networking decisions. Verify all configuration and command output before applying it to real hardware. Do not use it as the sole…

This model is a fine-tuned version of distilbert/distilroberta-base on an unknown dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 2e-05 - trainbatchsize: 8 - evalbatchsize: 8 - lrschedulertype: linear - numepochs: 3 - Transformers 5.17.0 - Pytorch 2.11.0+cu130 - Datasets 4.8.5 - Tokenizers 0.23.2

Nautilus 独立判读侧现役判分器(LoRA on Qwen3-1.7B)。 - 三态判读 pass / fail / insufficientevidence,J1 binary acc 88.51%(生产评测,ECE 0.072) - 部署纪律(PRECOR B/C 实测 291 题,2026-10-07):只允许 bf16/fp16——fp16 对 bf16 判定一致率 1.0000,int8 0.9828 FAIL,int4 0.9416 FAIL(禁用) - 判定表:docs/metering/PRECORBCVERDICT20261007.md(nautilus-compass 主仓) - 归属:NACRE 机制·判读实例 v1(机制名+实例版本号,组织对齐账 2026-10-06)

Model · Text to image

nanites-keypunch-z-image-1.0

Nanites

LoRA adapter for Z-Image (Tongyi-MAI, Apache-2.0), fine-tuned on 20 public-domain reference stills of mid-century data instrumentation and paperwork (FSA/OWI, Harris & Ewing, National Photo Company, HABS/HAER surveys) in the idiom of 1930s-1960s U.S. government documentary film stills. Stark black-and-white: punched cards, tabulating machines and card sorters, strip-chart recorders, dial gauges, ledgers, dense tabular reports. Harsh light, deep blacks, film grain. Use keypunch in your prompt to invoke the idiom. See SOURCES.md in the repo for the per-file rights basis.

Model · Text generation

nanites-smoky-1b-chat

Nanites

A Llama-3.2-1B-Instruct model fine-tuned to play the role of an old resident of the Great Smoky Mountains of the 1930s in conversation. You talk to it; the old-timer answers in period voice with regional vocabulary. This is the chat-tuned sibling of the CosyVoice2 gender-pooled speech voices (Earl / male at Nanite-Labs/nanites-smoky-speech-earl, Ethel / female at Nanite-Labs/nanites-smoky-speech-ethel). The chat model provides the language behind the TTS; pair them for a text-and-speech experience. Given a modern-English question about life in the Smokies (work, family, food, music, religion, hardships, the mountains themselves), the model answers in 1-3 sentences in the voice of an old…

Model · Text generation

nanites-smoky-1b-chat-gguf

Nanites

Q4KM quant of the merged nanites-smoky-1b-chat model — a Llama-3.2-1B-Instruct fine-tune that plays an old resident of the Great Smoky Mountains of the 1930s. For Ollama / LM Studio / llama.cpp users who don't want the base-model + adapter merge step. Adapter (train output) lives at Nanite-Labs/nanites-smoky-1b-chat; this repo holds the merged + quantized GGUF for direct use. Alpaca-style (### Instruction: / ### Input: / ### Response:). Example prompt

Model · Text generation

nanites-whaler-1b-chat

Nanites

A Llama-3.2-1B-Instruct model fine-tuned to play the role of a 19th-century American whaling first mate in conversation. You talk to it; the mate answers in period voice. This is the chat-tuned sibling of Nanite-Labs/nanites-whaler-1b, which is a text completer that continues 19th-century whaling logbook entries. The chat version is a different animal — don't mix them up. Talk to it like a sailor would talk to the mate on deck. It answers in the voice of a 19th-c. ship's mate, brief and direct, using "try-works", "by D.R.", "the Captain", "no speak of it", etc. Multi-turn is supported — the mate sees your prior Q&A in the context window and can refer to earlier exchanges. period voice) + 8…

Model · Text generation

nanites-whaler-1b-chat-gguf

Nanites

Q4KM quant of the merged nanites-whaler-1b-chat model — a Llama-3.2-1B-Instruct fine-tune that plays a 19th-century American whaling first mate. For Ollama / LM Studio / llama.cpp users who don't want the base-model + adapter merge step. Adapter (train output) lives at Nanite-Labs/nanites-whaler-1b-chat; this repo holds the merged + quantized GGUF for direct use. Alpaca-style (### Instruction: / ### Input: / ### Response:). Example prompt

A tiny pirate-themed GPT trained from scratch on a piratized version of TinyStories, then SFT-tuned. Built as a learning project — closer to nanoGPT than to a production LM. - model.safetensors — model weights. - config.json — architecture config (load into training.config.Config). - piratebpe.json — tokenizer (load with tokenizers.Tokenizer.fromfile). - trainingmetadata.json — full training config + metrics snapshot. - banner.png — the banner above. This model is not a transformers model — it uses the custom GPT class from this repo. - Trained on a small synthetic corpus (TinyStories, piratized). Vocabulary, grammar, and world knowledge are extremely narrow. - Short context window (256…

Four checkpoints, not one model. This repository is a controlled experiment: two training arms × two seeds, 10,000 steps each, identical in every respect except how packed training rows are masked. The comparison is the artifact. Any single checkpoint here is a weak 230M research model and is not meant for use. Each arm lives on its own branch: Weights are fp32, exactly as trained — these are the tensors every number below was computed from. Optimizer state is not included. nanoVLM packs several unrelated samples into one 4096-token row to avoid padding waste. Upstream's packing applies a single causal mask over the whole row, so a sample attends into the earlier, unrelated samples packed…

RKNN model artifacts and a checksum-pinned weights index for nanobot-channel-voice. The repository includes four official openWakeWord phrase heads, each available as an original-ONNX package for CPU/ONNX-capable hosts and as a validated RV1126B hybrid package: Choose one variant for the target host. Every fetch is a complete package: ONNX entries install the three original upstream graphs plus a phrase sidecar; RV1126B entries install the phrase head, exact Python-mel filterbank, and static RKNN embedding. The runtime fetches a complete model entry through nanobot-voice: weights-index.json is the authoritative manifest of available keys, source provenance, checksums, and acceptance…

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).