What are the hardware requirements for Qwen3 4B Thinking 2507 DAG Reasoning?

Qwen3 4B Thinking 2507 DAG Reasoning requires approximately 16.1 GB of VRAM and supports a context window of 256K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Qwen3 4B Thinking 2507 DAG Reasoning and how large is it?

Qwen3 4B Thinking 2507 DAG Reasoning is developed by sequelbox, a model with 4b 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 Qwen3 4B Thinking 2507 DAG Reasoning?

Qwen3 4B Thinking 2507 DAG Reasoning is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Qwen3 4B Thinking 2507 DAG Reasoning by sequelbox — VRAM 16.1GB, 256K context

Name: Qwen3 4B Thinking 2507 DAG Reasoning
Author: sequelbox

Qwen3 4B Thinking 2507 DAG Reasoning is an open-source language model by sequelbox. Features: 4b LLM, VRAM: 16.1GB, Context: 256K, License: apache-2.0, Instruction-Based, LLM Explorer Score: 0.19.

4b Analysis Analytical Architecture Base model:finetune:qwen/qwen3... Base model:qwen/qwen3-4b-think... Business Business-management Chat Codegen Conversational Creative Cybersecurity Dag-reasoning Dataset:sequelbox/dag-reasonin... Directed-acyclic-graph Economics En Endpoints compatible Energy Expert Finance Graph Instruct Knowledge Law Logic Logistics Politics Problem-solving Qwen Qwen-3 Qwen-3-4b Qwen3 Qwen3-4b-thinking-2507 Rationality Reasoning Region:us Root-cause-analysis Safetensors Sharded Software-engineering Supply-chain Tensorflow Thinking Valiant Valiant-labs

Model Card on HF 🤗: https://huggingface.co/sequelbox/Qwen3-4B-Thinking-2507-DAG-Reasoning

Qwen3 4B Thinking 2507 DAG Reasoning Parameters and Internals

LLM Name	Qwen3 4B Thinking 2507 DAG Reasoning
Repository 🤗	https://huggingface.co/sequelbox/Qwen3-4B-Thinking-2507-DAG-Reasoning
Base Model(s)	Qwen3 4B Thinking 2507 Qwen/Qwen3-4B-Thinking-2507
Model Size	4b
Required VRAM	16.1 GB
Updated	2026-08-07
Maintainer	sequelbox
Model Type	qwen3
Instruction-Based	Yes
Model Files	5.0 GB: 1-of-4 4.9 GB: 2-of-4 5.0 GB: 3-of-4 1.2 GB: 4-of-4
Supported Languages	en
Model Architecture	Qwen3ForCausalLM
License	apache-2.0
Context Length	262144
Model Max Length	262144
Transformers Version	4.55.0
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|endoftext\|>
Vocabulary Size	151936
Torch Data Type	float32
Errors	replace

Rank the Qwen3 4B Thinking 2507 DAG Reasoning capabilities

Have you tried this model? Rate its performance — this feedback helps the ML community find the right model for their needs.

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

Best Alternatives to Qwen3 4B Thinking 2507 DAG Reasoning

Best Alternatives	Context / RAM	Downloads	Likes
FastContext 1.0 4B SFT	256K / 8.1 GB	5735	357
Qwen3 4B Instruct 2507	256K / 8.1 GB	3109972	911
Fable Traces	256K / 8.1 GB	345	210
FastContext 1.0 4B RL	256K / 8.1 GB	4559	61
Qwen3 4B Instruct 2507 FP8	256K / 5.2 GB	1100744	78
...pt Full Qwen3 4b Instruct 2507	256K / 8 GB	365	1
Nexa AI 4B Instruct	256K / 8 GB	576	2
Neuron 4B Instruct	256K / 8.1 GB	313	1
Agents K1	256K / 8.8 GB	1098	28
FastContext 1.0 4B SFT	256K / 8.1 GB	273	18

Note: green Score (e.g. "73.2") means that the model is better than sequelbox/Qwen3-4B-Thinking-2507-DAG-Reasoning.