MemTensor/MemT-1.0-Flash
text-generation model by MemTensor
Older snapshot: check the original source for current information.
From the publisher
Model card & documentation
Source previewRead the publisher’s intended use, setup instructions, evaluations and limitations. The original model card is the source of truth.
MemT-1.0 is a new generation of memory foundation models that makes persistent, dynamically evolving memory an intrinsic part of model computation. MemT-1.0 integrates dynamic local memory and trainable hyper memory to retain and leverage historical information without accessing original context at inference. Its persistent state remains constant in size, reducing storage by approximately 66× versus full-context KV cache at 128K history length. The project makes three main contributions: A Native Memory Block runs alongside the backbone attention and contains two components: During generation, memory attention reads from the current memory state, normalizes and gates the readout, and adds it to the backbone attention output. At the end of each turn or segment, a stable GDN-2 performs token-level memory updates the memory state for subsequent steps. Across 11 memory benchmarks, MemT-1.0-Flash and MemT-1.0-Nano rank first and second among the evaluated parametric-memory methods, respectively. Strong textual-memory systems remain ahead overall, particularly on…
Read the full model card ↗ · Preview checked 2026-10-30T11:21:29.741Z
Inside the original model card — Document outline
- MemT-1.0-Flash
- Overview
- Model Family
- Architecture
- Evaluation Results
- Experience-Guided Agentic Coding
- Quick Start
- Installation
- Load the Model
- A Conversation with Memory
- Save and Resume
- Standard Chat
Headings are captured from the source. Links open the publisher’s document, not a locally hosted copy.
Links from the model card
References supplied by the publisher, not independently verified endorsements. Check the destination before downloading files or entering credentials.
Documentation belongs to its respective authors. Reported project/model license: mit. A listing is not a grant of reuse or training rights. Confirm the document’s own terms at the source.
Model overview
MemTensor/MemT-1.0-Flash is a text-generation model repository published by MemTensor on Hugging Face. The source reports the transformers library.
This page summarizes Hub metadata. For intended use, training data, evaluation results and limitations, consult the original model card.
Model facts
- Task
- text-generation
- Library
- transformers
- Recent downloads (30 days)
- 32,706
- Cumulative likes
- 107
- Hugging Face trending score
- 64
- Reported safetensors parameters
- 287,943,175,511
- Architecture
- MemTDeepseekV4ForCausalLM
- License
- mit
- Access
- Not gated by Hugging Face
- Created
- 2026-10-17T15:45:17.000Z
- Last modified
- 2026-10-20T05:49:56.000Z
Compatibility and lineage
Language coverage was not reported.
Reported base models: deepseek-ai/DeepSeek-V4-Flash
Parameter count is not a RAM/VRAM requirement. Check precision, quantization, context length and runtime compatibility in the model card; no hardware or API-cost claim is inferred here.
Use and evaluate
Check license terms and access requirements first. Review the model card’s documented loading instructions, evaluations and safety limitations. Benchmark scores are not imported by this directory, and popularity is not an accuracy ranking.
No model weights are downloaded or executed by AltAPIs. Never enable remote model code without reviewing it.
Source and freshness
Source: Hugging Face Hub. Metadata observed 2026-10-27T06:46:19.176Z. Daily imports are snapshots, not real-time monitoring.
Popularity and source listings do not establish security, suitability, licensing rights or benchmark performance.