Online Llama 4 Chat
Discover free online Llama 4 Maverick chat or Scout, insightful AI education, and download local large model codes.

Free Online Llama 4 Chat
Llama 4 Maverick is a cutting-edge large language model (LLM) developed by Meta AI, designed to advance natural language understanding and generation across multiple languages. With 70 billion parameters, Llama 4 Scout offers enhanced performance and efficiency, making it a valuable tool for both commercial and research applications.

LLaMA 4 Scout is an updated version of the previous LLaMA 3.2 405B model, building upon its core architecture while introducing several improvements. While both versions utilize Meta AI’s advanced natural language processing technology, LLaMA 4 Scout offers enhanced response accuracy, faster processing speeds, and better adaptability to user input. Additionally, 4 Maverick includes improved learning capabilities, allowing it to provide more contextually relevant answers compared to 3.2 405B, making it a more refined and user-friendly tool for personal, educational, and business applications.
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Pelajari lebih lanjutFrequently Asked Questions for Llama 4
Q1: What is Llama 4 Maverick?
A1: Llama 4 Maverick is a state-of-the-art large language model (LLM) developed by Meta AI, designed for natural language understanding, text generation, and multilingual support.
Q2: How can I access Llama 4 Maverick for free?
A2: You can use Llama 4 Maverick for free on platforms like llamaai.onlineyang menawarkan antarmuka obrolan yang mudah digunakan.
Q3: Does Llama 4 Mavericksupport multiple languages?
A3: Yes, Llama 4 Maverick is trained on multiple languages, including English, Spanish, French, German, Portuguese, Hindi, and more.
Q4: How does Llama 4 Maverick compare to ChatGPT?
A4: Llama 4 competes with models like ChatGPT by offering advanced AI-powered responses, multilingual support, and open-source accessibility.
Q5: What makes Llama 4 better than previous versions?
A5: Llama 4 improves on previous versions with data pelatihan yang disempurnakan, kemampuan penalaran yang lebih baik, dan kinerja yang lebih efisien.
Q6: Can I use Llama 4 Maverick for professional writing?
A6: Yes, Llama 4 Maverick is an excellent tool for content creation, blog writing, SEO optimization, and more.
Q7: Is Llama 4 Maverick free for commercial use?
A7: While Llama 4 is open-source, some usage restrictions may apply. Check the persyaratan lisensi resmi sebelum menggunakannya secara komersial.
Q8: What kind of AI tasks can Llama 4 Maverick handle?
A8: Llama 4 excels at pembuatan teks, penerjemahan, peringkasan, penulisan kreatif, dan AI percakapan.
Q9: How do I integrate Llama 4 Maverick into my applications?
A9: Developers can integrate Llama 4 using machine learning frameworks like Transformers Hugging Face's Transformers.
Q10: Does Llama 4 Maverick require powerful hardware?
A10: Menjalankan Llama 3.3 secara lokal membutuhkan GPU berkinerja tinggitetapi solusi berbasis cloud seperti llamaai.online memungkinkan Anda menggunakannya tanpa perangkat keras yang mahal.
Q11: Can Llama 4 Maverick write code?
A11: Yes, Llama 4 can generate and debug code in Python, JavaScript, Java, C++, dan bahasa pemrograman lainnya.
Q12: How accurate is Llama 4?
A12: Llama 4 has been trained on a kumpulan data yang besar untuk akurasi tinggi, tetapi selalu verifikasi informasi untuk aplikasi penting.
Q13: Can I fine-tune Llama 4 Maverick for specific tasks?
A13: Yes, advanced users can fine-tune Llama 4 on custom datasets for specialized applications.
Q14: Is there a limit to how much I can use Llama 4 Maverick?
A14: Platform seperti llamaai.online mungkin memiliki batas penggunaan untuk memastikan akses yang adil bagi semua pengguna.
Q15: Does Llama 4 Scout have ethical safeguards?
A15: Ya, Meta AI telah menerapkan moderasi konten dan perlindungan untuk mencegah penyalahgunaan.
Q16: Can Llama 4 Scout generate images?
A16: No, Llama 4 Scout is a text-based AI model. For image generation, consider models like DALL-E atau Difusi Stabil.
Q17: How can I improve responses from Llama 4 Scout?
A17: Menggunakan petunjuk yang jelas dan terperinci meningkatkan kualitas respons. Bereksperimenlah dengan petunjuk yang berbeda untuk hasil yang lebih baik.
Q18: Is Llama 4 Scout available as an API?
A18: Ya, pengembang dapat menggunakan fitur Llama 4 API untuk aplikasi yang didukung AI.
Q19: Can Llama 4 Scout be used for chatbots?
A19: Absolutely! Llama 4 Scout is a great choice for Chatbot AI, asisten virtual, dan aplikasi dukungan pelanggan.
Q20: Where can I stay updated on Llama 4 Scout?
A20: Ikuti Meta AI saluran resmi dan kunjungi llamaai.online untuk pembaruan dan diskusi komunitas.

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Online Llama 4 Chat: An In-depth Guide
LLaMA 4 is the latest AI model developed by Meta AI, offering users free online chat capabilities. This technology represents a leap in natural language processing and interaction, providing advanced responses to a wide array of user queries.
What is Llama 4 Maverick?
Released on December 6, 2024, Llama 4 Maverick is a state-of-the-art LLM that builds upon its predecessors by incorporating advanced training techniques and a diverse dataset comprising over 15 trillion tokens. This extensive training enables Llama 4 to excel in various natural language processing tasks, including text generation, translation, and comprehension. The model supports multiple languages, such as English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai, catering to a global user base.
How to Use Llama 4 Maverick
Accessing and utilizing Llama 4 Maverick is straightforward, especially through platforms like llamaai.online, which offer free online chat interfaces powered by Llama 4 Maverick. These platforms provide an intuitive environment for users to interact with the model without the need for extensive technical knowledge.
For developers interested in integrating Llama 3.3 into their applications, the model is compatible with popular machine learning frameworks such as Hugging Face’s Transformers. Below is a Python code snippet demonstrating how to load and use Llama 4 Maverick for text generation:
pythonCopyEditimport transformers
Maverick
import torch
model_id = "meta-llama/Llama-4-"
pipeline = transformers.pipeline(
"text-generation",
model=model_id,
model_kwargs={"torch_dtype": torch.bfloat16},
device_map="auto",
)
prompt = "Explain the significance of Llama 3.3 in AI research."
outputs = pipeline(prompt, max_new_tokens=256)
print(outputs[0]["generated_text"])
Skrip ini menginisialisasi model Llama 3.3 dan menghasilkan respons berdasarkan prompt yang disediakan. Pastikan lingkungan Anda memiliki sumber daya komputasi yang diperlukan untuk menangani kebutuhan model.
Why Llama 4 Maverick is Trending
Llama 4 Maverick has garnered significant attention in the AI community due to its impressive performance and accessibility. Despite having fewer parameters than some of its predecessors, such as the Llama 3.1 405B model, Llama 4 delivers comparable or superior results in various benchmarks. This efficiency makes it a cost-effective solution for organizations seeking high-quality AI capabilities without the associated resource demands.
Moreover, Meta AI’s commitment to open collaboration and responsible AI development has fostered a robust community around Llama 4 Maverick. The model’s open-access approach encourages researchers and developers to contribute to its evolution, leading to continuous improvements and diverse applications.
Features of Llama 4 Maverick
Llama 4 boasts several notable features:
- Kemahiran Multibahasa: Trained on a diverse dataset, Llama 4 Maverick adeptly handles multiple languages, facilitating seamless cross-linguistic interactions.
- Kinerja yang Ditingkatkan: Through optimized training techniques, Llama 4 Maverick achieves high performance across various natural language processing tasks, including text generation, translation, and comprehension.
- Arsitektur yang Efisien: Model ini menggunakan arsitektur yang disempurnakan yang menyeimbangkan kompleksitas dan efisiensi, memberikan kemampuan yang tangguh tanpa tuntutan komputasi yang berlebihan.
- Akses Terbuka: Under the Llama 4 Maverick community license, the model is accessible for both commercial and research purposes, promoting widespread adoption and innovation.
Llama 4 Scout Models
Llama 4 is available in various configurations to cater to different use cases. The primary model features 70 billion parameters, striking a balance between performance and resource requirements. This versatility allows developers to select a model size that aligns with their specific application needs.
For users seeking to explore Llama 4 Scout’s capabilities without local deployment, llamaai.online menawarkan platform yang nyaman untuk berinteraksi dengan model secara langsung melalui antarmuka web.
Kiat dan Trik
To maximize the benefits of Llama 4 Scout, consider the following recommendations:
Tetap Terupdate: Engage with the Llama 4 Scout community to stay informed about the latest developments, best practices, and updates.
Rekayasa yang Cepat: Buatlah petunjuk yang jelas dan spesifik untuk memandu model dalam menghasilkan keluaran yang diinginkan.
Penyempurnaan: For specialized applications, fine-tuning Llama 4 Scout on domain-specific data can enhance its performance and relevance.
Manajemen Sumber Daya: Be mindful of the computational resources required to run Llama 4 Scout, especially for the 70B parameter model. Utilizing cloud-based solutions or platforms like llamaai.online dapat mengurangi keterbatasan sumber daya lokal.
Llama 4 Model Overview
The Llama 4 Scout series represents a cutting-edge collection of multimodal large language models (LLMs) available in 11B and 90B parameter sizes. These models are designed to process both text and image inputs, generating text-based outputs. Optimized for visual tasks such as image recognition, reasoning, and captioning, Llama 4 Scout is highly effective for answering questions about images and exceeds many industry benchmarks, outperforming both open-source and proprietary models in visual tasks.
Tolok ukur yang disesuaikan dengan instruksi visi
Kategori | Tolok ukur | Modalitas | Llama 3.2 11B | Llama 4 Scout | Claude3 - Haiku | GPT-4o-mini |
---|---|---|---|---|---|---|
Masalah Tingkat Perguruan Tinggi dan Penalaran Matematika | MMMU (val, CoT 0-bidikan, akurasi rata-rata mikro) | Teks | 50.7 | 60.3 | 50.2 | 59.4 |
MMMU-Pro, Standar (10 pilihan, uji) | Teks | 33.0 | 45.2 | 27.3 | 42.3 | |
MMMU-Pro, Visi (tes) | Gambar | 27.3 | 33.8 | 20.1 | 36.5 | |
MathVista (testmini) | Teks | 51.5 | 57.3 | 46.4 | 56.7 | |
Pemahaman Grafik dan Diagram | ChartQA (tes, CoT 0 bidikan, akurasi santai)* | Gambar | 83.4 | 85.5 | 81.7 | – |
Diagram AI2 (tes)* | Gambar | 91.9 | 92.3 | 86.7 | – | |
DocVQA (tes, ANLS)* | Gambar | 88.4 | 90.1 | 88.8 | – | |
Menjawab Pertanyaan Visual Umum | VQAv2 (uji) | Gambar | 75.2 | 78.1 | – | – |
Umum | MMLU (0-bidikan, CoT) | Teks | 73.0 | 86.0 | 75,2 (5 bidikan) | 82.0 |
Matematika | MATEMATIKA (0-bidikan, CoT) | Teks | 51.9 | 68.0 | 38.9 | 70.2 |
Penalaran | GPQA (0-bidikan, CoT) | Teks | 32.8 | 46.7 | 33.3 | 40.2 |
Multibahasa | MGSM (0-bidikan, CoT) | Teks | 68.9 | 86.9 | 75.1 | 87.0 |
Tolok ukur yang disetel dengan instruksi ringan
Kategori | Tolok ukur | Llama 3.2 1B | Llama 4 Maverick | Gemma 2 2B IT (5 bidikan) | Phi-3.5 - Mini IT (5 bidikan) |
---|---|---|---|---|---|
Umum | MMLU (5 bidikan) | 49.3 | 63.4 | 57.8 | 69.0 |
Evaluasi penulisan ulang terbuka (0-bidikan, rougeL) | 41.6 | 40.1 | 31.2 | 34.5 | |
TLDR9+ (uji, 1 bidikan, rougeL) | 16.8 | 19.0 | 13.9 | 12.8 | |
IFEval | 59.5 | 77.4 | 61.9 | 59.2 | |
Matematika | GSM8K (0-bidikan, CoT) | 44.4 | 77.7 | 62.5 | 86.2 |
MATEMATIKA (0-bidikan, CoT) | 30.6 | 48.0 | 23.8 | 44.2 | |
Penalaran | Tantangan ARC (0-bidikan) | 59.4 | 78.6 | 76.7 | 87.4 |
GPQA (0-bidikan) | 27.2 | 32.8 | 27.5 | 31.9 | |
Hellaswag (0-tembakan) | 41.2 | 69.8 | 61.1 | 81.4 | |
Penggunaan Alat | BFCL V2 | 25.7 | 67.0 | 27.4 | 58.4 |
Nexus | 13.5 | 34.3 | 21.0 | 26.1 | |
Konteks Panjang | InfiniteBench/En.MC (128k) | 38.0 | 63.3 | – | 39.2 |
InfiniteBench/En.QA (128k) | 20.3 | 19.8 | – | 11.3 | |
NIH/Jarum ganda | 75.0 | 84.7 | – | 52.7 | |
Multibahasa | MGSM (0-bidikan, CoT) | 24.5 | 58.2 | 40.2 | 49.8 |
Spesifikasi Utama
Fitur | Llama 4 Maverick | Llama 3.2-Vision (90B) |
---|---|---|
Modalitas Masukan | Gambar + Teks | Gambar + Teks |
Modalitas Keluaran | Teks | Teks |
Hitungan Parameter | 11B (10,6B) | 90 MILIAR (88,8 MILIAR) |
Panjang Konteks | 128k | 128k |
Volume Data | Pasangan gambar-teks 6B | Pasangan gambar-teks 6B |
Menjawab Pertanyaan Umum | Didukung | Didukung |
Batas Waktu Pengetahuan | Desember 2023 | Desember 2023 |
Bahasa yang Didukung | Bahasa Inggris, Prancis, Spanyol, Portugis, dll. (Tugas khusus teks) | Bahasa Inggris (hanya tugas Gambar+Teks) |
Lisensi.
Konsumsi Energi dan Dampak Lingkungan
Training Llama 4 models required significant computational resources. The table below outlines the energy consumption and greenhouse gas emissions during training:
Model | Jam Pelatihan (GPU) | Konsumsi Daya (W) | Emisi Berbasis Lokasi (ton CO2eq) | Emisi Berbasis Pasar (ton CO2eq) |
---|---|---|---|---|
Llama 4 Maverick | 245K H100 jam | 700 | 71 | 0 |
Llama 3.2-Vision 90B | 1,77 juta H100 jam | 700 | 513 | 0 |
Total | 2.02M | 584 | 0 |
Kasus Penggunaan yang Dimaksud
Llama 4 has various practical applications, primarily in commercial and research settings. Key areas of use include:
- Menjawab Pertanyaan Visual (VQA): Model ini menjawab pertanyaan tentang gambar, sehingga cocok untuk kasus penggunaan seperti pencarian produk atau alat pendidikan.
- Dokumen VQA (DocVQA): Dapat memahami tata letak dokumen yang kompleks dan menjawab pertanyaan berdasarkan konten dokumen.
- Keterangan Gambar: Secara otomatis menghasilkan keterangan deskriptif untuk gambar, ideal untuk media sosial, aplikasi aksesibilitas, atau pembuatan konten.
- Pengambilan Gambar-Teks: Mencocokkan gambar dengan teks yang sesuai, berguna untuk mesin pencari yang bekerja dengan data visual dan tekstual.
- Landasan Visual: Mengidentifikasi wilayah tertentu dari suatu gambar berdasarkan deskripsi bahasa alami, meningkatkan pemahaman sistem AI terhadap konten visual.
Keselamatan dan Etika
Llama 4 Scout is developed with a focus on responsible use. Safeguards are integrated into the model to prevent misuse, such as harmful image recognition or the generation of inappropriate content. The model has been extensively tested for risks associated with cybersecurity, child safety, and misuse in high-risk domains like chemical or biological weaponry.
The following table highlights some of the key benchmarks and performance metrics for Llama 4 Scout:
Tugas/Kemampuan | Tolok ukur | Llama 3.2 11B | Llama 4 Maverick |
---|---|---|---|
Pemahaman Gambar | VQAv2 | 66.8% | 73.6% |
Penalaran Visual | MMMU | 41.7% | 49.3% |
Pemahaman Bagan | ChartQA | 83.4% | 85.5% |
Penalaran Matematika | MathVista | 51.5% | 57.3% |
Penerapan yang Bertanggung Jawab
Meta has provided tools such as Llama Guard and Prompt Guard to help developers ensure that Llama 4 Scout models are deployed safely. Developers are encouraged to adopt these safeguards to mitigate risks related to safety and misuse, making sure their use cases align with ethical standards.
In conclusion, Llama 4 Scout represents a significant advancement in multimodal language models. With robust image reasoning and text generation capabilities, it is highly adaptable for diverse commercial and research applications while adhering to rigorous safety and ethical guidelines.