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Moss Moon 003 Sft Plugin by fnlp

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Moss Moon 003 Sft Plugin is an open-source language model by fnlp. Features: LLM, VRAM: 33.5GB, License: agpl-3.0, LLM Explorer Score: 0.07.

  Arxiv:2203.13474   Autotrain compatible   Custom code   Dataset:fnlp/moss-002-sft-data   En   Moss   Pytorch   Region:us   Sharded   Zh
Model Card on HF 🤗: https://huggingface.co/fnlp/moss-moon-003-sft-plugin 

Moss Moon 003 Sft Plugin Benchmarks

LLME Score: 0.0702
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Moss Moon 003 Sft Plugin (fnlp/moss-moon-003-sft-plugin)
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Moss Moon 003 Sft Plugin Parameters and Internals

Model Type 
conversational, plugin-augmented
Use Cases 
Areas:
research, commercial applications
Applications:
conversational agents, AI assistance
Primary Use Cases:
instruction following, dialogue systems
Limitations:
Possible misleading, incorrect, or biased information generation
Considerations:
Users should verify model outputs for correctness and bias.
Additional Notes 
Model is in continuous development with new features and improvements being implemented.
Supported Languages 
languages_supported (en, zh), proficiency_level (fluent)
Training Details 
Data Sources:
fnlp/moss-002-sft-data, fnlp/moss-003-sft-data, gpt-3.5-turbo
Data Volume:
100B Chinese tokens, 20B English tokens
Methodology:
Pre-trained on a large-scale dataset, followed by supervised fine-tuning and preference-aware training with multi-turn conversational data and plugin augmentation.
Context Length:
2048
Hardware Used:
A100 GPU, NVIDIA 3090 GPU
Model Architecture:
Pre-trained on multi-turn conversational data and plugin-augmented data with supervised and preference-aware fine-tuning.
Safety Evaluation 
Methodologies:
red-teaming
Risk Categories:
misinformation, bias
Ethical Considerations:
Acknowledges possible biased, misleading, or incorrect information generation.
Responsible Ai Considerations 
Fairness:
Training includes a diverse set of conversation data to address bias.
Transparency:
Model details and code are open-sourced.
Accountability:
Fudan University
Mitigation Strategies:
Fine-tuning on preference data to improve factuality and safety.
Input Output 
Input Format:
Text input following conversational format.
Accepted Modalities:
text
Output Format:
Text output with capabilities to use plugins for specialized tasks.
Performance Tips:
Use INT-4/8 models for lower memory requirements; align text input for best context understanding.
Release Notes 
Version:
moss-moon-003-base
Date:
N/A
Notes:
Base pre-trained model with 16B parameters, intended for conversational use.
Version:
moss-moon-003-sft
Date:
N/A
Notes:
Supervised fine-tuned version capable of handling multi-turn dialogues.
Version:
moss-moon-003-sft-plugin
Date:
N/A
Notes:
Plugin-augmented for enhanced functionality in specific tasks.
LLM NameMoss Moon 003 Sft Plugin
Repository 🤗https://huggingface.co/fnlp/moss-moon-003-sft-plugin 
Required VRAM33.5 GB
Updated2025-10-06
Maintainerfnlp
Model Typemoss
Model Files  9.8 GB: 1-of-4   9.9 GB: 2-of-4   9.8 GB: 3-of-4   4.0 GB: 4-of-4
Supported Languagesen zh
Model ArchitectureMossForCausalLM
Licenseagpl-3.0
Model Max Length2048
Transformers Version4.25.1
Tokenizer ClassGPT2Tokenizer
Beginning of Sentence Token<|endoftext|>
End of Sentence Token<eom>
Unk Token<|endoftext|>
Vocabulary Size107008
Torch Data Typefloat16
Activation Functiongelu_new
Errorsreplace

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Note: green Score (e.g. "73.2") means that the model is better than fnlp/moss-moon-003-sft-plugin.

Rank the Moss Moon 003 Sft Plugin Capabilities

🆘 Have you tried this model? Rate its performance. This feedback would greatly assist ML community in identifying the most suitable model for their needs. Your contribution really does make a difference! 🌟

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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