🧠
llama-4-scout-instruct model by meta
⭐ 16.2
🧠

llama-4-scout-instruct

FNI 16.2
by meta Model

"A 17 billion parameter model with 16 experts"

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Best Scenarios

✨ Innovative Solution

Technical Constraints

Experimental / High Latency
- Params
- Context
3.4M Downloads
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custom License

πŸ•ΈοΈ Neural Graph Explorer

v15.13

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263 Entities
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πŸ“ˆ Interest Trend

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πŸ› οΈ Technical Profile

⚑ Hardware & Scale

Parameters
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Architecture
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Context Length
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🧠 Training & Env

Library
-
Precision
float16
Tokenizer
-

🌐 Cloud & Rights

Source
replicate
License
custom

🏷️ Tags

public

🧠 Architecture Explorer

Neural network architecture

1 Input Layer
2 Hidden Layers
3 Attention
4 Output Layer
Learn about Transformers β†’ Context Length Explained β†’

πŸ› οΈ Technical Profile

⚑ Hardware & Scale

Parameters
-
Architecture
-
Context Length
-

🧠 Training & Env

Library
-
Precision
float16
Tokenizer
-

🌐 Cloud & Rights

Source
replicate
License
custom

πŸ“ Limitations & Considerations

  • β€’ Benchmark scores may vary based on evaluation methodology and hardware configuration.
  • β€’ VRAM requirements are estimates; actual usage depends on quantization and batch size.
  • β€’ FNI scores are relative rankings and may change as new models are added.
  • ⚠ License Unknown: Verify licensing terms before commercial use.

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πŸ“„ Related Papers

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