What are the hardware requirements for Predictagrid Sft With Reasoning 3epoch?

Predictagrid Sft With Reasoning 3epoch requires approximately 6.2 GB of VRAM and supports a context window of 32K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Predictagrid Sft With Reasoning 3epoch and how large is it?

Predictagrid Sft With Reasoning 3epoch is developed by konvolution, a model with 3b parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

Where can I download or evaluate Predictagrid Sft With Reasoning 3epoch?

Predictagrid Sft With Reasoning 3epoch is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Predictagrid Sft With Reasoning 3epoch by konvolution — VRAM 6.2GB, 32K context

Name: Predictagrid Sft With Reasoning 3epoch
Author: konvolution

Predictagrid Sft With Reasoning 3epoch is an open-source language model by konvolution. Features: 3b LLM, VRAM: 6.2GB, Context: 32K, License: apache-2.0, Quantized, Instruction-Based, LLM Explorer Score: 0.17.

4bit Conversational En Endpoints compatible Instruct Quantized Qwen2 Region:us Safetensors Sft Sharded Tensorflow Trl Unsloth

Model Card on HF 🤗: https://huggingface.co/konvolution/predictagrid-sft-with-reasoning-3epoch

Predictagrid Sft With Reasoning 3epoch Parameters and Internals

LLM Name	Predictagrid Sft With Reasoning 3epoch
Repository 🤗	https://huggingface.co/konvolution/predictagrid-sft-with-reasoning-3epoch
Base Model(s)	unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit
Model Size	3b
Required VRAM	6.2 GB
Updated	2026-04-18
Maintainer	konvolution
Model Type	qwen2
Instruction-Based	Yes
Model Files	5.0 GB: 1-of-2 1.2 GB: 2-of-2
Supported Languages	en
Quantization Type	4bit
Model Architecture	Qwen2ForCausalLM
License	apache-2.0
Context Length	32768
Model Max Length	32768
Transformers Version	4.48.3
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|vision_pad\|>
Vocabulary Size	151936
Torch Data Type	bfloat16
Errors	replace

Rank the Predictagrid Sft With Reasoning 3epoch capabilities

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Instruction Following and Task Automation
Factuality and Completeness of Knowledge
Censorship and Alignment
Data Analysis and Insight Generation
Text Generation
Text Summarization and Feature Extraction
Code Generation
Multi-Language Support and Translation

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Note: green Score (e.g. "73.2") means that the model is better than konvolution/predictagrid-sft-with-reasoning-3epoch.