SmolLM2Prover is a specialized, fine-tuned version of prithivMLmods/SmolLM2-CoT-360M. While retaining the strong conversational abilities of its base model, this version has been specifically enhanced to excel at deep thinking, logical reasoning, and higher-level mathematics, with a focus on generating step-by-step proofs and explanations (Chain-of-Thought). The model was fine-tuned using multiple rounds of Supervised Fine-Tuning (SFT) with the TRL library on a curated dataset, enhancing its ability to follow complex instructions and reason through problems. This model is intended to be used for text generation tasks that require logical reasoning or advanced conversation. The easiest way…
Open weights
apache-2.0
362M parameters
8,192 tokens
transformers
The Drafter agent of a LangGraph multi-agent support system for the fictional shop ReVonance. Base model HuggingFaceTB/SmolLM2-360M-Instruct, fine-tuned with LoRA (merged). Stage: sft (sft = supervised on the response simulator; dpo = then aligned with human and critic preferences via Direct Preference Optimisation). Knowledge-base fingerprint: f9cdb07b5df6. Demo model trained on synthetic data; it only knows the toy policies of ReVonance.
Open weights
apache-2.0
362M parameters
8,192 tokens
transformers
S
Model · Text generation
Sasha
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).
Open weights
355M parameters
transformers
Suggest a title and description for any text. On-device titles and descriptions: a short factual title and a one- to two-sentence description for any passage of text. Swift (requirements) Then add the Title product to your target. The MLX trait is required: without it the module compiles as a stub. Get a title and a one or two sentence description for any passage of text, on device. Fine-tuned on transcript clips, but it works on any prose. The register is deliberately plain, with no emoji, no hashtags and no clickbait, and a description is meant to identify this passage rather than its topic. An MLX model directory. Load the folder, not a single file. The chat template is not incidental. A…
Open weights
other
352M parameters
32,768 tokens
mlx
RoPE-free long-range attention, trained at 4k with zero-shot length extrapolation. FS-B searches full-prefix and norm-filtered forests of chunk mean keys, reranks their candidates, and reads four chunks. Its attention is superlinear and subquadratic. This 341M research base model was trained on FineWeb-Edu at a 4,096-token context. The short retrieval example asks for the hidden number 5437923. Start with CUDA-enabled PyTorch; Usage gives the tested installation and speed-replication command. FreeSolo learns simple chunk mean keys that support search, norm pruning, and hierarchical summarization. RoPE stays in the local window; long-range attention reads selected original tokens without…
Open weights
mit
374M parameters
pytorch
Try it in your browser: the new tinybrainbot-350mV3-math-Q80.gguf (371 MB) runs locally on your device with WebGPU, and nothing you type is sent anywhere. A 348M-parameter decoder-only model fine-tuned to do column arithmetic with a written-out scratchpad. Successor to the TinyBrainBot 320M V2 Math. TL;DR: 99.4% average across the nine GPT-3 arithmetic sub-tasks, beating the 320M V2 Math (91.5%). Adds cleanly to 14 digits, does 3-digit multipliers (98.0%), and handles negative results (85% overall, but only 58% at 5 digits - see below). It is a calculator with a scratchpad, not a word-problem solver — see the honest limits below. Use applychattemplate rather than hand-building the prompt…
Open weights
apache-2.0
348M parameters
2,048 tokens
transformers
