What are the hardware requirements for Neuralfalcon 1B V1?

Neuralfalcon 1B V1 requires approximately 2.8 GB of VRAM and supports a context window of 2K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Neuralfalcon 1B V1 and how large is it?

Neuralfalcon 1B V1 is developed by vihangd, a model with 1b parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

How does Neuralfalcon 1B V1 perform on standard benchmarks?

Neuralfalcon 1B V1 has the following published scores: MMLU 26.22. Compare against reference models on this page or on the LLM Explorer leaderboards.

Neuralfalcon 1B V1 by vihangd — VRAM 2.8GB, 2K context

Name: Neuralfalcon 1B V1
Author: vihangd

Neuralfalcon 1B V1 is an open-source language model by vihangd. Features: 1b LLM, VRAM: 2.8GB, Context: 2K, License: apache-2.0, LLM Explorer Score: 0.15, Arc: 26.8, HellaSwag: 26.6, MMLU: 26.2, GSM8K: 0.2.

Endpoints compatible Falcon Pytorch Region:us Sharded

Model Card on HF 🤗: https://huggingface.co/vihangd/neuralfalcon-1b-v1

Neuralfalcon 1B V1 Parameters and Internals

Training Details

Data Sources:

chatml style datasets

Methodology:

DPO finetune

Model Architecture:

Uses chatml style prompt template

LLM Name	Neuralfalcon 1B V1
Repository 🤗	https://huggingface.co/vihangd/neuralfalcon-1b-v1
Model Size	1b
Required VRAM	2.8 GB
Updated	2026-08-06
Maintainer	vihangd
Model Type	falcon
Model Files	0.4 GB: 1-of-7 0.4 GB: 2-of-7 0.4 GB: 3-of-7 0.4 GB: 4-of-7 0.4 GB: 5-of-7 0.4 GB: 6-of-7 0.4 GB: 7-of-7
Model Architecture	FalconForCausalLM
License	apache-2.0
Context Length	2048
Model Max Length	2048
Transformers Version	4.34.1
Is Biased	1
Tokenizer Class	GPT2Tokenizer
Padding Token	<\|endoftext\|>
Vocabulary Size	50304
Torch Data Type	float16

Rank the Neuralfalcon 1B V1 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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