Open-Weight Models

This model is served in https://github.com/xelananv/fhe-ssm: it is a 404,715,520 parameter recurrent language model whose every (!) nonlinearity is a low-degree polynomial with trained coeffs, so that the CKKS encrypted evaluation computes precisely the model's own float64 arithmetic without any post-hoc approximation of softmax or GELU. It succesfully generated text for 64 prompts at once under encryption, autoregressively, for 148 consecutive steps (=> 9,472 tokens) at 128 bit classical parameters, matching its own plaintext top-1 choice 99.7 % of the time. Those are properties of the circuit; this card is about the model, which is small and was trained on a few hundred dollars of…

Model

fiddle

Computational Metabolomics Research Group

Yuhui Hong and FIDDLE contributors. FIDDLE v2.0.0 Orbitrap prediction and rescoring checkpoints. Copyright 2024 Yuhui Hong. Upstream release. Apache-2.0 follows the release's project licence; no separate checkpoint-specific licence statement accompanies the archives. The unchanged upstream licence notice is included as 2.0.0/LICENSE. The software license is reported separately; it does not replace the model/data license. These are byte-identical upstream files. No model conversion was performed.

English financial decision model, approximately 0.42B parameters. This card describes the 6-rounds release (previously labeled four-source). Training data cover CHFS, CGSS, CFPS and CLDS. Macro F1 averages over all candidate classes within each supported question version, then equally over versions. 8,885 input tokens/s, 26.75 ms mean latency, 37.38 decisions/s. Measured on one A100 40GB with FP16, batch and concurrency one, the same three requests, three warmups and 20 timed calls per request; input throughput uses a shared tokenizer. 本页为 6-rounds 训练版本，旧标签为 four-source；数据来源为 CHFS、CGSS、CFPS、CLDS。3-rounds 训练版本使用独立分支模型卡与权重。按训练轮次命名，数据来源单独说明。

A 1.7B on-device language model for Indian bank SMS parsing and personal finance coaching. FinAI SLM is a Qwen/Qwen3-1.7B derivative fine-tuned with QLoRA SFT for the FinnDot expense tracker app. It runs entirely on-device — no cloud calls, no user data leaves the phone. The model handles English, Hindi, Hinglish, Tamil, Telugu, Marathi, and Bengali bank SMS. v1 → v2: R-EM +5.2 pp, chat groundedness +36 pp, false-parse 28.57% → 0.54%. No user SMS. No real financial data. Fully synthetic. Split hygiene: assignment unit is (bank, templateid), not individual SMS. All variants of a template stay in one split. See ml/data/manifest/SHA256SUMS. Pre-registered before looking at test scores…

Action Chunking with Transformers (ACT) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from teleoperated data and often achieves high success rates. This policy has been trained and pushed to the Hub using LeRobot. Learn how to train and run it in the LeRobot act guide, or browse the full documentation. The policy consumes these observation features and produces these action features. Inputs Outputs New to LeRobot? These guides cover the full workflow: - Install LeRobot — set up the lerobot package. - Hardware setup — assemble, wire, and calibrate your robot and cameras. - Record data & train a policy — the end-to-end imitation-learning…

An open, auditable classifier that reads a contract sentence and identifies its financial obligations — payment terms, renewal pricing, liquidated damages, liability caps, termination costs, audit rights, payment security. Every prediction is a lead, not a verdict: the model assists human review; it does not give legal advice and does not decide anything. categories (F1, F3–F8; see the taxonomy). F2 (late fees & interest) has no training data and is not covered. one-vs-rest. Small, deterministic, CPU-only, fully reproducible. or confidential contracts. vectorizer, classifier, label binarizer). - Eval set is small (56); confidence intervals are wide. - Train and eval come from the same CUAD…

static quants of https://huggingface.co/ProsusAI/finbert For a convenient overview and download list, visit our model page for this model. weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files. (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) Here is a handy graph by ikawrakow comparing some lower-quality quant And here…

Model · Text classification

finbert-crypto-stock-news-sentiment

David Kurniawan

Classifies a news headline as positive, negative or neutral for holders of the asset it is filed under. Fine-tuned from ProsusAI/finbert on headlines filed under Bitcoin, gold (the GLD fund) and US stocks (the SPY fund). It describes the tone of the news. It does not forecast prices. Scored once on a held-out test set of 854 headlines from 2026-04-01 to 2026-10-14, after every choice had been made on validation data. The test set holds more positive and negative headlines than the news feed does, so the figures are shown as drawn and also re-weighted to the feed's natural mix. McNemar's exact test against the base model: p = 9e-77. On headlines where both the label and the prediction are…

This model is a fine-tuned version of mistralai/Mistral-7B-v0.3. It has been trained using TRL. This model was trained with SFT.

Model · Zero-shot classification

finecat-nli-ultra-l

Lee Miller

This model is a fine-tune of dmis-lab/UltraBERT-380M, trained on the dleemiller/FineCat-NLI-Reason dataset, a compilation of several high-quality NLI data sources with quality screening and reduction of easy samples in the training split. Also includes additional NLI, fact verification and long document sources (2.05M pairs in total). The training uses logit distillation from an NLI finetune of Qwen3.8-27B. \begin{equation} \mathcal{L} = \alpha \cdot \mathcal{L}{\text{CE}}(z^{(s)}, y) + \beta \cdot T^2 \cdot \mathrm{KL}\left(\sigma(z^{(t)}/T) \,\|\, \sigma(z^{(s)}/T)\right) \end{equation} where \\(z^{(s)}\\) and \\(z^{(t)}\\) are the student and teacher logits, \\(y\\) are the ground truth…

Model

finer-sql-1_5b-origdb-ckpt

Thanh Dat Hoang

Base: griffith-bigdata/Qwen-2.5-Coder-1.5B-SQL-Writer, GRPO checkpoints from ICT job 98674 (s2986280, gpuq2). Reward tree: ORIGINAL BIRD+Spider training DBs (fingerprint 69343e76e07bbbd9aa8c0d1d37ae940e, 69 BIRD + 169 Spider DBs), not the cb821adb CoRE-DB tree -- this is the origdb control arm paired with thanhdath/finer-sql-15b-coredb-cb821adb-ckpt. Corpus: v3 (grposqlwriterorigdatajointoldpromptfullfixed, 18,087 rows), MAXSTEPS=9312, --maxcompletionlength 2048 (locked), GT cache sha256 618a9aa45d4c2a6e2f355900e3fa15280d6e9facfed87a370d1193cdafd0cb72.