概览
主要功能
- 多语言文本生成
- 指令训练后的聊天变体
- 长文本支持
- 编码和推理能力
- 开放权重可进行微调
- 兼容主流推断框架
价格
- 模型
- Free
- 分类
- 微为架的给布系统
- 评分
- 4.8 / 5 (5)
使用场景
语言翻译
Llama 3.3 可以准确翻译不同语言的文本。
内容生成
该模型可以生成各种应用的高质量文本，包括文章、产品描述等。
文本摘要
Llama 3.3 可以将长篇大论的文本Condense到更易于消化的摘要。
优点 & 缺点
优点
- 开放权重支持自主部署
- 强大的多语言表现
- 高效与更大模型相比
- 广泛的生态和工具支持
缺点
- 需要大量GPU资源
- 对于极大部署的授权限制
- 知识截止可能会影响最近信息
对决战绩
在万神殿中参与了 2 对决。
Last 2 battles
评测
5 个评分的平均值。
登录以留下评测。
Solid for our team
We rolled this out across the team last quarter and strong multilingual performance. Open weights for fine-tuning fits neatly into how we already work, and open weights for fine-tuning removed a step we used to do by hand. but it has held up under daily use.
Skeptical, then convinced
I went in skeptical — most tools in this space overpromise. It actually delivers on long-context support, and efficient compared to larger models caught me off guard. Licensing restrictions for very large deployments is why this isn't a perfect score, still, I'd recommend giving it a real trial.
Solid for our team
We rolled this out across the team last quarter and efficient compared to larger models. Instruction-tuned chat variant fits neatly into how we already work, and instruction-tuned chat variant removed a step we used to do by hand. but it has held up under daily use.
Does the job
Pretty happy overall. Coding and reasoning capabilities just works and efficient compared to larger models. Licensing restrictions for very large deployments can be annoying, but no dealbreakers — I'd recommend it to a friend without hesitating.
Does the job
Pretty happy overall. Open weights for fine-tuning just works and broad ecosystem and tooling support. but no dealbreakers — I'd recommend it to a friend without hesitating.
问答
What are the main limitations I should consider before adopting Llama 3.3?
Running Llama 3.3 requires substantial GPU resources, its licensing imposes restrictions on very large‑scale deployments, and its knowledge cutoff means it won’t contain information about events after its training data cut‑off.
Asked by Urszula Kowalczyk · May 22, 2026
Which frameworks can I use to run Llama 3.3 in my application?
The model is compatible with major inference frameworks such as PyTorch, TensorFlow, and the popular inference runtimes used by cloud providers, making integration into existing pipelines straightforward.
Asked by Hiroshi Tanaka · Apr 27, 2026
What deployment options does Llama 3.3 offer and how does that affect cost?
Llama 3.3’s open weights let you self‑host on‑premises or run it via major cloud and inference providers, so you only pay for the compute resources you use; there’s no separate model licensing fee, but GPU costs can be significant.
Asked by Ivana Novak · Mar 18, 2026
提问
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