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Seed OSS 36B Instruct by unsloth

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Seed OSS 36B Instruct is an open-source language model by unsloth. Features: 36b LLM, VRAM: 72.2GB, Context: 512K, License: apache-2.0, Instruction-Based, LLM Explorer Score: 0.23.

Base model:bytedance-seed/seed... Base model:finetune:bytedance-...   Conversational   Endpoints compatible   Instruct   Region:us   Safetensors   Seed oss   Sharded   Tensorflow   Unsloth   Vllm
Model Card on HF 🤗: https://huggingface.co/unsloth/Seed-OSS-36B-Instruct 

Seed OSS 36B Instruct Benchmarks

LLME Score: 0.22549
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Seed OSS 36B Instruct (unsloth/Seed-OSS-36B-Instruct)
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Seed OSS 36B Instruct Parameters and Internals

LLM NameSeed OSS 36B Instruct
Repository 🤗https://huggingface.co/unsloth/Seed-OSS-36B-Instruct 
Base Model(s)  Seed OSS 36B Instruct   ByteDance-Seed/Seed-OSS-36B-Instruct
Model Size36b
Required VRAM72.2 GB
Updated2026-04-13
Maintainerunsloth
Model Typeseed_oss
Instruction-BasedYes
Model Files  5.0 GB: 1-of-15   5.0 GB: 2-of-15   4.8 GB: 3-of-15   4.9 GB: 4-of-15   4.8 GB: 5-of-15   4.9 GB: 6-of-15   4.8 GB: 7-of-15   4.9 GB: 8-of-15   4.8 GB: 9-of-15   4.9 GB: 10-of-15   4.8 GB: 11-of-15   4.9 GB: 12-of-15   4.8 GB: 13-of-15   4.9 GB: 14-of-15   4.0 GB: 15-of-15
Model ArchitectureSeedOssForCausalLM
Licenseapache-2.0
Context Length524288
Model Max Length524288
Transformers Version4.55.0
Tokenizer ClassPreTrainedTokenizerFast
Padding Token<seed:pad>
Vocabulary Size155136
Torch Data Typebfloat16

Best Alternatives to Seed OSS 36B Instruct

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Seed OSS 36B Instruct512K / 72.2 GB20833494
...6B Base Instruct Karcher Merge512K / 72.1 GB63
Seed Oss 36B Instruct Fp16 Mlx512K / 72.2 GB402
Seed OSS 36B Instruct 4bit512K / 20.2 GB292
Seed OSS 36B Instruct AWQ512K / 21.1 GB1388
...eed OSS 36B Instruct GPTQ Int4512K / 21.1 GB1105
Note: green Score (e.g. "73.2") means that the model is better than unsloth/Seed-OSS-36B-Instruct.

Rank the Seed OSS 36B Instruct Capabilities

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Instruction Following and Task Automation  
Factuality and Completeness of Knowledge  
Censorship and Alignment  
Data Analysis and Insight Generation  
Text Generation  
Text Summarization and Feature Extraction  
Code Generation  
Multi-Language Support and Translation  

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