willitrun·ai

InternScience

Agents-A1 4B

前沿
65.2K下载量64点赞Jul 2026发布日期262K tokens上下文Apache 2.0许可证62 良好质量

Agents-A1 4B (4.5B parameters) requires approximately 4.7 GB of VRAM with Q4_K_M quantization. For the best balance of quality and speed, we recommend hardware with at least 6 GB of VRAM.

快速开始

— 复制粘贴即可本地运行

Copy-paste commands to run Agents-A1 4B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \ --hf-repo "InternScience/Agents-A1-4B" \ --hf-file "Agents-A1-4B-Q4_K_M.gguf" \ -c 4096 -ngl 99

Quick specs

Parameters4.5B
Architecturedense
Context262K tokens
Modalitytext+vision
Min RAM1.8 GB
Rec. RAM2.7 GB (Q4_K_M)
LicenseApache 2.0
FamilyAgents-A1
Reasoning

About this model

Agents-A1-4B is the compact dense member of InternScience's Agents-A1 family — a 4.5B vision-capable agentic model distilled from the same long-horizon agent training recipe as the 35B MoE. It targets edge and laptop deployment while keeping the family's 262K context and hybrid linear/full attention backbone.

  • 4.5B dense and vision-capable — fits in ~3GB at Q4.
  • 262K context, unusually long for a 4B-class model.
  • Hybrid linear + full attention (1-in-4 layers) keeps long-context KV cache small.
  • Shares the Agents-A1 long-horizon agentic training recipe.

相关模型

你的硬件

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最佳硬件

Agents-A1 4B 的最佳选择

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量化选项

各量化级别的 VRAM 估算

No hardware detected — fit column shows raw VRAM estimates

QuantBitsVRAMQualityFit
Q1_0_G128
1.125
0.6 GB
Very Low
Q2_0_G128
1.71
1.2 GB
Low
Q2_K
2
1.8 GB
Low
Q3_K_S
3
2.2 GB
Low
NVFP4
4
2.5 GB
Medium
Q4_K_M
4
2.7 GB
Medium
Q5_K_M
5
3.2 GB
High
Q6_K
6
3.7 GB
High
Q8_0
8
4.8 GB
Very High
F16
16
9.2 GB
Maximum

Quality benchmarks

Agents-A1 4B benchmark scores

Benchmark verified

Coding

SWE-bench Verified
HumanEval+
Aider Polyglot
LiveCodeBench59.6%

General

Chatbot Arena
IFEval94.8%

Source: official · 2026-07-13

硬件兼容性

全部硬件的适配估算

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Computing compatibility...

内存详细分析

Reference: RTX 2060 6GB

Weights2.7 GB
KV Cache0.5 GB
Runtime0.9 GB
Headroom0.6 GB

常见问题

FAQ — Agents-A1 4B

另请参阅