What are the hardware requirements for Stablelm 3B 4e1t GGUF?

Stablelm 3B 4e1t GGUF requires approximately 1.2 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Stablelm 3B 4e1t GGUF and how large is it?

Stablelm 3B 4e1t GGUF is developed by afrideva, 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 Stablelm 3B 4e1t GGUF?

Stablelm 3B 4e1t GGUF is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Stablelm 3B 4e1t GGUF by afrideva — VRAM 1.2GB

Name: Stablelm 3B 4e1t GGUF
Author: afrideva

Stablelm 3B 4e1t GGUF is an open-source language model by afrideva. Features: 3b LLM, VRAM: 1.2GB, License: cc-by-sa-4.0, Quantized, LLM Explorer Score: 0.1.

Arxiv:1607.06450 Arxiv:1910.02054 Arxiv:1910.07467 Arxiv:2101.00027 Arxiv:2104.09864 Arxiv:2204.06745 Arxiv:2305.06161 Arxiv:2307.09288 Base model:quantized:stability... Base model:stabilityai/stablel... Dataset:allenai/pes2o Dataset:bigcode/starcoderdata Dataset:carperai/pilev2-dev Dataset:tiiuae/falcon-refinedw... Dataset:togethercomputer/redpa... En Ggml Gguf Q2 Q2 k Q3 k m Q4 k m Q5 k m Q6 k Q8 0 Quantized Region:us Stablelm

Model Card on HF 🤗: https://huggingface.co/afrideva/stablelm-3b-4e1t-GGUF

Stablelm 3B 4e1t GGUF Parameters and Internals

Model Type

decoder-only transformer, auto-regressive language model, causal-lm

Use Cases

Areas:

foundational base model for application-specific fine-tuning

Limitations:

unreliable, unsafe behaviors, offensive content

Considerations:

Evaluate and fine-tune model for safe performance in downstream applications.

Supported Languages

en (primary)

Training Details

Data Sources:

tiiuae/falcon-refinedweb, togethercomputer/RedPajama-Data-1T, CarperAI/pilev2-dev, bigcode/starcoderdata, allenai/peS2o

Data Volume:

1 trillion tokens

Methodology:

bfloat16 precision, AdamW optimization, trained with NeoX tokenizer

Context Length:

4096

Training Time:

30 days

Hardware Used:

256 NVIDIA A100 40GB GPUs (AWS P4d instances)

Model Architecture:

decoder-only, Rotary Position Embeddings, LayerNorm with learned bias

Input Output

Accepted Modalities:

text

Performance Tips:

Fine-tune model for specific applications.

LLM Name	Stablelm 3B 4e1t GGUF
Repository 🤗	https://huggingface.co/afrideva/stablelm-3b-4e1t-GGUF
Model Name	stablelm-3b-4e1t
Model Creator	stabilityai
Base Model(s)	Stablelm 3B 4e1t stabilityai/stablelm-3b-4e1t
Model Size	3b
Required VRAM	1.2 GB
Updated	2026-07-27
Maintainer	afrideva
Model Type	stablelm
Model Files	1.2 GB 1.4 GB 1.7 GB 2.0 GB 2.3 GB 3.0 GB
Supported Languages	en
GGUF Quantization	Yes
Quantization Type	gguf\|q2\|q8_0
Model Architecture	AutoModel
License	cc-by-sa-4.0

Rank the Stablelm 3B 4e1t GGUF 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

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Note: green Score (e.g. "73.2") means that the model is better than afrideva/stablelm-3b-4e1t-GGUF.