🧠

Model

Deepicd R1 Zero 32b I1 Gguf

Name: Deepicd R1 Zero 32b I1 Gguf
Author: mradermacher

by mradermacher hf-model--mradermacher--deepicd-r1-zero-32b-i1-gguf

Nexus Index

36.2 Top 100%

S: Semantic 50

A: Authority 0

P: Popularity 33

R: Recency 93

Q: Quality 50

Tech Context

32 Params

4.096K Ctx

Vital Performance

2.4K DL / 30D

0.0%

Source →

Audited 36.2 FNI Score

32B Params

4k Context

2.4K Downloads

H100+ ~27GB Est. VRAM

Restricted OTHER License

Model Information Summary
Entity Passport
Registry ID	hf-model--mradermacher--deepicd-r1-zero-32b-i1-gguf
License	Other
Provider	huggingface

💾

Compute Threshold

~26.5GB VRAM

Interactive

Analyze Hardware

Hardware Compatibility Test

▼

* Static estimation for 4-Bit Quantization.

📜

Cite this model

Academic & Research Attribution

BibTeX

@misc{hf_model__mradermacher__deepicd_r1_zero_32b_i1_gguf,
  author = {mradermacher},
  title = {Deepicd R1 Zero 32b I1 Gguf Model},
  year = {2026},
  howpublished = {\url{https://huggingface.co/mradermacher/deepicd-r1-zero-32b-i1-gguf}},
  note = {Accessed via Free2AITools Knowledge Fortress}
}

APA Style

mradermacher. (2026). Deepicd R1 Zero 32b I1 Gguf [Model]. Free2AITools. https://huggingface.co/mradermacher/deepicd-r1-zero-32b-i1-gguf

🔬Technical Deep Dive

Full Specifications [+]

Quick Commands

🦙 Ollama Run

ollama run deepicd-r1-zero-32b-i1-gguf

🤗 HF Download

huggingface-cli download mradermacher/deepicd-r1-zero-32b-i1-gguf

📦 Install Lib

pip install -U transformers

⚖️ Nexus Index V2.0

Methodology Index Protocol

36.2

TOP 100% SYSTEM IMPACT

Semantic (S) 50

Authority (A) 0

Popularity (P) 33

Recency (R) 93

Quality (Q) 50

💬 Index Insight

FNI V2.0 for Deepicd R1 Zero 32b I1 Gguf: Semantic (S:50), Authority (A:0), Popularity (P:33), Recency (R:93), Quality (Q:50).

Free2AITools Nexus Index

Verification Authority

HuggingFace API GitHub Metadata Arxiv Citation DB System Audit

Unbiased Data Node Refresh: VFS Live

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🚀 What's Next?

📊

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Compare Benchmarks

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⚡

Technical Deep Dive

About

weighted/imatrix quants of https://huggingface.co/DATEXIS/DeepICD-R1-zero-32B

For a convenient overview and download list, visit our model page for this model.

static quants are available at https://huggingface.co/mradermacher/DeepICD-R1-zero-32B-GGUF

Usage

If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs 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	imatrix	0.1	imatrix file (for creating your own quants)
GGUF	i1-IQ1_S	7.4	for the desperate
GGUF	i1-IQ1_M	8.0	mostly desperate
GGUF	i1-IQ2_XXS	9.1
GGUF	i1-IQ2_XS	10.1
GGUF	i1-IQ2_S	10.5
GGUF	i1-IQ2_M	11.4
GGUF	i1-Q2_K_S	11.6	very low quality
GGUF	i1-Q2_K	12.4	IQ3_XXS probably better
GGUF	i1-IQ3_XXS	12.9	lower quality
GGUF	i1-IQ3_XS	13.8
GGUF	i1-Q3_K_S	14.5	IQ3_XS probably better
GGUF	i1-IQ3_S	14.5	beats Q3_K*
GGUF	i1-IQ3_M	14.9
GGUF	i1-Q3_K_M	16.0	IQ3_S probably better
GGUF	i1-Q3_K_L	17.3	IQ3_M probably better
GGUF	i1-IQ4_XS	17.8
GGUF	i1-Q4_0	18.8	fast, low quality
GGUF	i1-Q4_K_S	18.9	optimal size/speed/quality
GGUF	i1-Q4_K_M	20.0	fast, recommended
GGUF	i1-Q4_1	20.7
GGUF	i1-Q5_K_S	22.7
GGUF	i1-Q5_K_M	23.4
GGUF	i1-Q6_K	27.0	practically like static Q6_K

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, for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to @nicoboss for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.

⚠️ Incomplete Data

Some information about this model is not available. Use with Caution - Verify details from the original source before relying on this data.

View Original Source →

📝 Limitations & Considerations

• Benchmark scores may vary based on evaluation methodology and hardware configuration.
• VRAM requirements are estimates; actual usage depends on quantization and batch size.
• FNI scores are relative rankings and may change as new models are added.
⚠ License Unknown: Verify licensing terms before commercial use.

Social Proof

HuggingFace Hub

2.4KDownloads

Hub Discussions

🤗 Data Source: Hugging Face ↗

🔄 Daily sync (03:00 UTC)

AI Summary: Based on Hugging Face metadata. Not a recommendation.

📊 FNI Methodology 📚 Knowledge Baseℹ️ Verify with original source

🛡️ Model Transparency Report

Technical metadata sourced from upstream repositories.

Open Metadata

🆔 Identity & Source

id: hf-model--mradermacher--deepicd-r1-zero-32b-i1-gguf
slug: mradermacher--deepicd-r1-zero-32b-i1-gguf
source: huggingface
author: mradermacher
license: Other
tags: transformers, gguf, clinical-nlp, medical-coding, icd10, icd-10-cm, reasoning, reinforcement-learning, grpo, healthcare, en, base_model:datexis/deepicd-r1-zero-32b, base_model:quantized:datexis/deepicd-r1-zero-32b, license:other, endpoints_compatible, region:us, imatrix, conversational

⚙️ Technical Specs

architecture: null
params billions: 32
context length: 4,096
pipeline tag: reinforcement-learning
vram gb: 26.5
vram is estimated: true
vram formula: VRAM ≈ (params * 0.75) + 2GB (KV) + 0.5GB (OS)

📊 Engagement & Metrics

downloads: 2,444
stars: 0
forks: 0

Data indexed from public sources. Updated daily.

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