What are the hardware requirements for R Model?

R Model requires approximately 7.6 GB of VRAM and supports a context window of 4K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed R Model and how large is it?

R Model is developed by hackint0sh. 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 R Model?

R Model is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

R Model by hackint0sh — VRAM 7.6GB, 4K context

Name: R Model
Author: hackint0sh

R Model is an open-source language model by hackint0sh. Features: LLM, VRAM: 7.6GB, Context: 4K, Quantized, Instruction-Based, LLM Explorer Score: 0.13.

4bit Conversational Endpoints compatible Gguf Instruct Mistral Pytorch Quantized Region:us Sharded

Model Card on HF 🤗: https://huggingface.co/hackint0sh/r_model

R Model Parameters and Internals

LLM Name	R Model
Repository 🤗	https://huggingface.co/hackint0sh/r_model
Required VRAM	7.6 GB
Updated	2026-07-01
Maintainer	hackint0sh
Model Type	mistral
Instruction-Based	Yes
Model Files	5.0 GB: 1-of-2 2.6 GB: 2-of-2 2.3 GB
GGUF Quantization	Yes
Quantization Type	gguf\|4bit
Model Architecture	MistralForCausalLM
Context Length	4096
Model Max Length	4096
Transformers Version	4.40.2
Tokenizer Class	LlamaTokenizer
Padding Token	<\|endoftext\|>
Vocabulary Size	32064
Torch Data Type	float16

Rank the R Model 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 R Model

Best Alternatives	Context / RAM	Downloads	Likes
...istral Nemo Instruct 2407 GGUF	1000K / 4.8 GB	1139	66
...istral Nemo Instruct 2407 GGUF	1000K / 4.8 GB	1293	11
...istral C64Wizard Instruct GGUF	32K / 4.4 GB	19	0
Equall Saul Instruct V1	32K / 5.1 GB	30	0
...eZephir Sft Instruct Ead 16bit	32K / 14.4 GB	56	0
... Instruct Unsloth Merged 16bit	4K / 7.6 GB	6	1
... Mini 4K Instruct Bnb 4bit Ita	4K / 7.6 GB	1181	0
... Mini 4K Instruct Bnb 4bit Ita	4K / 7.6 GB	1527	0
...ini New Model With Lora Merged	4K / 7.6 GB	16	0
Phired	4K / 7.6 GB	12	0

Note: green Score (e.g. "73.2") means that the model is better than hackint0sh/r_model.