NVIDIA

Nemotron Mini 4B

Name: Nemotron Mini 4B
Rating: 50 (193 reviews)
Author: NVIDIA

Actual

HuggingFace

376.4KDescargas182Me gustaAug 2024Publicado4K tokensContextoNVIDIA Open ModelLicencia6 EntradaCalidad

Nemotron Mini 4B (4B parameters) requires approximately 6.2 GB of VRAM with Q4_K_M quantization. For the best balance of quality and speed, we recommend hardware with at least 8 GB of VRAM.

Comenzar

— copia y pega para ejecutar en local

Copy-paste commands to run Nemotron Mini 4B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
  --hf-repo "nvidia/Nemotron-Mini-4B-Instruct" \
  --hf-file "Nemotron-Mini-4B-Instruct-Q4_K_M.gguf" \
  -c 4096 -ngl 99

Quick specs

Parameters4B

Architecturedense

Context4K tokens

Modalitytext

Min RAM1.6 GB

Rec. RAM2.4 GB (Q4_K_M)

LicenseNVIDIA Open Model

FamilyNemotron

✓ Chat

About this model

Nemotron-Mini-4B-Instruct is a model for generating responses for roleplaying, retrieval augmented generation, and function calling. It is a small language model (SLM) optimized through distillation, pruning and quantization for speed and on-device deployment. It is a fine-tuned version of nvidia/Minitron-4B-Base, which was pruned and distilled from Nemotron-4 15B using our LLM compression technique. This instruct model is optimized for roleplay, RAG QA, and function calling in English. It supports a context length of 4,096 tokens. This model is ready for commercial use.

•Garak, is an automated LLM vulnerability scanner that probes for common weaknesses, including prompt injection and data leakage
•AEGIS, is a content safety evaluation dataset and LLM based content safety classifier model, that adheres to a broad taxonomy of 13 categories of...
•Human Content Red Teaming leveraging human interaction and evaluation of the models' responses

Modelos relacionados

Selecciones rápidas

Mejor económicoB

Intel Arc A580 8GB~$179 — 56 tok/s

Mejor en generalB

RTX 5060 8GB~$299 — 76 tok/s

Mejor hardware

Mejores opciones para Nemotron Mini 4B

Ejecutar este modelo

Nemotron Mini 4B on RTX 5060 8GB Nemotron Mini 4B on RTX 5060 Ti 8GB Nemotron Mini 4B on RTX 5050 8GB

Opciones de cuantización

Estimaciones de VRAM por nivel de cuantización

No hardware detected — fit column shows raw VRAM estimates

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	1.6 GB	Low	—
Q3_K_S	3	2.0 GB	Low	—
NVFP4	4	2.2 GB	Medium	—
Q4_K_M	4	2.4 GB	Medium	—
Q5_K_M	5	2.9 GB	High	—
Q6_K	6	3.3 GB	High	—
Q8_0	8	4.3 GB	Very High	—
F16	16	8.2 GB	Maximum	—

Quality benchmarks

Nemotron Mini 4B benchmark scores

Benchmark verified

Coding

SWE-bench Verified—

HumanEval+23.3%

Aider Polyglot—

LiveCodeBench—

Reasoning

MMLU-Pro18.1%

GPQA Diamond4.0%

MATH-5002.6%

ARC Challenge50.9%

General

Chatbot Arena—

IFEval66.7%

Source: official · 2024-07-17

Compatibilidad de hardware

Estimaciones de encaje en todo el hardware

Abrir calculadora

Computing compatibility...

Desglose de memoria

Reference: RTX 2060 6GB

Weights2.4 GB

KV Cache2.0 GB

Runtime1.2 GB

Headroom0.6 GB

Preguntas frecuentes

FAQ — Nemotron Mini 4B

Ver también

Guía de cuantización Metodología de puntuación Abrir calculadora