# SMOLM2Prover by Convergent Intelligence: Open-Weight Model Source: https://savrn.com/models/smolm2prover Markdown alternate of the page above; the site index is https://savrn.com/llms.txt --- ## Runs On What it takes to serve SMOLM2Prover (362M parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them. | Precision | Weights | Memory needed | Cheapest setup | Per hour | Also fits | | --- | --- | --- | --- | --- | --- | | 16-bit | 0.7 GB | 0.9 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 | | 8-bit | 0.4 GB | 0.4 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 | | 4-bit | 0.2 GB | 0.2 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 | Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the [SAVRN Index](https://savrn.com/ai-index/pricing/gpus), read Oct 22, 2026. [SMOLM2Prover on every accelerator the SAVRN Index prices, at every precision](https://savrn.com/models/smolm2prover/gpus) ## Model Card By Convergent Intelligence, published under apache-2.0, revision fca8ea4ab320. ### Model Card for SmolLM2Prover SmolLM2Prover is a specialized, fine-tuned version of [prithivMLmods/SmolLM2-CoT-360M](https://huggingface.co/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](https://github.com/huggingface/trl) library on a curated dataset, enhancing its ability to follow complex instructions and reason through problems. ### Model Details - Base Model: [prithivMLmods/SmolLM2-CoT-360M](https://huggingface.co/prithivMLmods/SmolLM2-CoT-360M) - Fine-tuning Library: TRL (Transformer Reinforcement Learning) - Specialization: Mathematical reasoning, proof generation, Chain-of-Thought (CoT) - Training Data: Fine-tuned on AI-MO/NuminaMath-1.5 and an additional ~1 million tokens of custom-formatted reasoning data. ### How to Use This model is intended to be used for text generation tasks that require logical reasoning or advanced conversation. #### Using the Pipeline The easiest way to use the model is with the transformers pipeline. [Read the full model card (946 words)](https://savrn.com/models/smolm2prover/card) ## Configuration Architecture LlamaForCausalLM Context length (tokens) 8,192 Layers 32 Hidden size 960 Feed-forward size 2,560 Attention heads 15 Key/value heads 5 Head dimension 64 Vocabulary size 49,152 RoPE base 100,000 Model type llama ## Identity and Version Repository reaperdoesntknow/SMOLM2Prover Publisher Convergent Intelligence Task Text generation Modality Text Library transformers Parameters 362M parameters Languages en Revision fca8ea4ab3206ff5c5c28c00c51fdbae57e8de32 First published 2025-09-06 Last updated 2026-09-18 ## Files and Weights 11 files, 1.5 GB in total. The weights are 1 file totalling 1.4 GB in safetensors. Weights1 file · 1.4 GB Configuration3 files · 1.5 KB Tokenizer4 files · 4.8 MB Documentation1 file · 9.7 KB Other1 file · 196 B Repository1 file · 1.5 KB Every file | File | Type | Size | SHA-256 | | --- | --- | --- | --- | | model.safetensors | Weights | 1.4 GB | 36bb97f1fe26 | | config.json | Configuration | 742 B | — | | generation_config.json | Configuration | 142 B | — | | special_tokens_map.json | Configuration | 655 B | — | | README.md | Documentation | 9.7 KB | — | | chat_template.jinja | Other | 196 B | — | | .gitattributes | Repository | 1.5 KB | — | | merges.txt | Tokenizer | 466.4 KB | — | | tokenizer.json | Tokenizer | 3.5 MB | — | | tokenizer_config.json | Tokenizer | 3.6 KB | — | | vocab.json | Tokenizer | 800.7 KB | — | ## License and Download License apache-2.0 Access Open weights, no gate Download size 1.4 GB [Download from Convergent Intelligence](https://huggingface.co/reaperdoesntknow/SMOLM2Prover) Released by Convergent Intelligence through its official repository on Hugging Face. [Read the license](https://www.apache.org/licenses/LICENSE-2.0). ## Built From - Derived from prithivMLmods/SmolLM2-CoT-360M - Trained on (disclosed) AI-MO/NuminaMath-1.5 ## Memory Requirements | Precision | Weights in memory | | --- | --- | | As published | 1.4 GB | | 16-bit | 0.7 GB | | 8-bit | 0.4 GB | | 4-bit | 0.2 GB | Weights only, from the published parameter count; the key-value cache and runtime add to this. ## Questions About SMOLM2Prover ### How much GPU memory does SMOLM2Prover need? About 0.9 GB at 16-bit and 0.2 GB at 4-bit: the weights (362M parameters) plus a working margin. A long context needs more. ### What is the cheapest GPU to run SMOLM2Prover on? At 16-bit, 1x MI300X from $1.85 an hour; at 4-bit, 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists. ### Can I use SMOLM2Prover commercially? Yes. SMOLM2Prover is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors. ### What is SMOLM2Prover's context length? 8,192 tokens, from the maximum position embeddings in its published configuration. ## Similar Models Model · Text generation ### [trainer-coach-smollm2-360m](https://savrn.com/models/trainer-coach-smollm2-360m) [John](https://savrn.com/model-publishers/jhmle) 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 362M parameters 8,192 tokens transformers [View model](https://savrn.com/models/trainer-coach-smollm2-360m) Model · Text generation ### [revonance-drafter-smollm2-360m](https://savrn.com/models/revonance-drafter-smollm2-360m) [Yuan Hao Sum](https://savrn.com/model-publishers/trychoosing) 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 [View model](https://savrn.com/models/revonance-drafter-smollm2-360m) Model · Text generation ### [MyModes-Pretrain](https://savrn.com/models/mymodes-pretrain) [Sasha](https://savrn.com/model-publishers/bigbrvisuals) 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 [View model](https://savrn.com/models/mymodes-pretrain) Model · Text generation ### [title](https://savrn.com/models/title) [Desert Ant Labs](https://savrn.com/model-publishers/desert-ant-labs) 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 [View model](https://savrn.com/models/title) Model · Text generation ### [FreeSolo-B-341M](https://savrn.com/models/freesolo-b-341m) [Zekun Shi](https://savrn.com/model-publishers/zekun-shi) 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. 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Use applychattemplate rather than hand-building the prompt… Open weights apache-2.0 348M parameters 2,048 tokens transformers [View model](https://savrn.com/models/tinybrainbot-350mv3-math) ## Convergent Intelligence [All models and datasets](https://savrn.com/model-publishers/reaperdoesntknow) ## Versions - [fca8ea4ab320](https://savrn.com/models/smolm2prover/versions/fca8ea4ab320) · current 2026-09-18 ## Explore More - [All text generation models](https://savrn.com/models/tasks/text-generation) - [All models under apache-2.0](https://savrn.com/models/licenses/apache-2-0) - [Model comparisons](https://savrn.com/models/comparisons) - [The model directory](https://savrn.com/models) - [Open model prices by host](https://savrn.com/ai-index/pricing/open-models) ## Source - Repository metadata, read 2026-10-21. - [Hugging Face record](https://huggingface.co/reaperdoesntknow/SMOLM2Prover) - [How the hub is built](https://savrn.com/model-hub/methodology)