Transformers
GGUF
English
dag-reasoning
valiant
valiant-labs
qwen
qwen-3
qwen-3-4b
qwen3-4b-thinking-2507
4b
thinking
reasoning
directed-acyclic-graph
graph
logic
analysis
programming
knowledge
root-cause-analysis
economics
business
business-management
finance
law
supply-chain
logistics
software-engineering
cybersecurity
architecture
energy
politics
problem-solving
creative
analytical
expert
rationality
conversational
chat
instruct
File size: 4,623 Bytes
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---
base_model: sequelbox/Qwen3-4B-Thinking-2507-DAG-Reasoning
datasets:
- sequelbox/DAG-Reasoning-DeepSeek-R1-0528
language:
- en
library_name: transformers
license: apache-2.0
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- dag-reasoning
- valiant
- valiant-labs
- qwen
- qwen-3
- qwen-3-4b
- qwen3-4b-thinking-2507
- 4b
- thinking
- reasoning
- directed-acyclic-graph
- graph
- logic
- analysis
- programming
- knowledge
- root-cause-analysis
- economics
- business
- business-management
- finance
- law
- supply-chain
- logistics
- software-engineering
- cybersecurity
- architecture
- energy
- politics
- problem-solving
- creative
- analytical
- expert
- rationality
- conversational
- chat
- instruct
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
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<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
<!-- ### quants_skip: -->
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static quants of https://huggingface.co/sequelbox/Qwen3-4B-Thinking-2507-DAG-Reasoning
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Qwen3-4B-Thinking-2507-DAG-Reasoning-GGUF).***
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Qwen3-4B-Thinking-2507-DAG-Reasoning-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Qwen3-4B-Thinking-2507-DAG-Reasoning-GGUF/resolve/main/Qwen3-4B-Thinking-2507-DAG-Reasoning.Q2_K.gguf) | Q2_K | 1.8 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-4B-Thinking-2507-DAG-Reasoning-GGUF/resolve/main/Qwen3-4B-Thinking-2507-DAG-Reasoning.Q3_K_S.gguf) | Q3_K_S | 2.0 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-4B-Thinking-2507-DAG-Reasoning-GGUF/resolve/main/Qwen3-4B-Thinking-2507-DAG-Reasoning.Q3_K_M.gguf) | Q3_K_M | 2.2 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-4B-Thinking-2507-DAG-Reasoning-GGUF/resolve/main/Qwen3-4B-Thinking-2507-DAG-Reasoning.Q3_K_L.gguf) | Q3_K_L | 2.3 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-4B-Thinking-2507-DAG-Reasoning-GGUF/resolve/main/Qwen3-4B-Thinking-2507-DAG-Reasoning.IQ4_XS.gguf) | IQ4_XS | 2.4 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-4B-Thinking-2507-DAG-Reasoning-GGUF/resolve/main/Qwen3-4B-Thinking-2507-DAG-Reasoning.Q4_K_S.gguf) | Q4_K_S | 2.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-4B-Thinking-2507-DAG-Reasoning-GGUF/resolve/main/Qwen3-4B-Thinking-2507-DAG-Reasoning.Q4_K_M.gguf) | Q4_K_M | 2.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-4B-Thinking-2507-DAG-Reasoning-GGUF/resolve/main/Qwen3-4B-Thinking-2507-DAG-Reasoning.Q5_K_S.gguf) | Q5_K_S | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-4B-Thinking-2507-DAG-Reasoning-GGUF/resolve/main/Qwen3-4B-Thinking-2507-DAG-Reasoning.Q5_K_M.gguf) | Q5_K_M | 3.0 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-4B-Thinking-2507-DAG-Reasoning-GGUF/resolve/main/Qwen3-4B-Thinking-2507-DAG-Reasoning.Q6_K.gguf) | Q6_K | 3.4 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-4B-Thinking-2507-DAG-Reasoning-GGUF/resolve/main/Qwen3-4B-Thinking-2507-DAG-Reasoning.Q8_0.gguf) | Q8_0 | 4.4 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-4B-Thinking-2507-DAG-Reasoning-GGUF/resolve/main/Qwen3-4B-Thinking-2507-DAG-Reasoning.f16.gguf) | f16 | 8.2 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
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