LLM EXPLORER
 LLM Explorer: A Curated Large Language Model Directory and Analytics
Dark Theme
  Agents   Benchmarks   LLM  List    LLM Hosting    LLM Leaderboards    Blog    Newsfeed    Advertise

Snorkel Mistral PairRM DPO by snorkelai

 »  All LLMs  »  snorkelai  »  Snorkel Mistral PairRM DPO   URL Share it on

  706   

Snorkel Mistral PairRM DPO is an open-source language model by snorkelai. Features: LLM, VRAM: 14.4GB, Context: 32K, License: apache-2.0, HF Score: 66.2, LLM Explorer Score: 0.15, Arc: 66, HellaSwag: 85.6, MMLU: 60.9, TruthfulQA: 70.9, WinoGrande: 77.6, GSM8K: 36.2.

  Arxiv:2305.18290   Arxiv:2306.02561   Arxiv:2312.11456   Arxiv:2401.10020   Conversational Dataset:snorkelai/snorkel-mist...   Endpoints compatible   Mistral   Pytorch   Region:us   Sharded
Model Card on HF 🤗: https://huggingface.co/snorkelai/Snorkel-Mistral-PairRM-DPO 

Snorkel Mistral PairRM DPO Benchmarks

ARC: 65.96 vs 96.7 (so35)-31.8%
HellaSwag: 85.63 vs 95.3 (gpt4)-10.1%
MMLU: 60.85 vs 88.3 (so35)-31.1%
TruthfulQA: 70.91 vs 59 (gpt4)20.2%
WinoGrande: 77.58 vs 87.5 (gpt4)-11.3%
GSM8K: 36.16 vs 96.4 (so35)-62.5%
LLME Score: 0.14651
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Snorkel Mistral PairRM DPO (snorkelai/Snorkel-Mistral-PairRM-DPO)
🌟 Advertise your project 🚀

Snorkel Mistral PairRM DPO Parameters and Internals

Model Type 
text-generation
Use Cases 
Limitations:
The model is a quick demonstration and does not have any moderation mechanisms.
Additional Notes 
For enterprise use cases, additional fine-tuning and alignment are necessary. Interested parties can contact Snorkel AI for specialized reward models.
Training Details 
Data Sources:
snorkelai/Snorkel-Mistral-PairRM-DPO-Dataset, UltraFeedback
Methodology:
1. Generate five response variations for each prompt from a subset of 20,000 using the LLM - to start, we used [Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2). 2. Apply [PairRM](https://huggingface.co/llm-blender/PairRM) for response reranking. 3. Update the LLM by applying Direct Preference Optimization (DPO) on the top (chosen) and bottom (rejected) responses. 4. Use this LLM as the base model for the next iteration, repeating three times in total.
Input Output 
Input Format:
[INST] {prompt} [/INST]
Accepted Modalities:
text
Performance Tips:
The model is designed for initial trials and may take time initially to activate on Hugging Face endpoint.
Release Notes 
Version:
GGUF
Notes:
Model version available from community members.
LLM NameSnorkel Mistral PairRM DPO
Repository 🤗https://huggingface.co/snorkelai/Snorkel-Mistral-PairRM-DPO 
Required VRAM14.4 GB
Updated2026-03-30
Maintainersnorkelai
Model Typemistral
Model Files  9.9 GB: 1-of-2   4.5 GB: 2-of-2   0.0 GB
Model ArchitectureMistralForCausalLM
Licenseapache-2.0
Context Length32768
Model Max Length32768
Transformers Version4.34.0
Tokenizer ClassLlamaTokenizer
Padding Token</s>
Vocabulary Size32000
Torch Data Typebfloat16

Quantized Models of the Snorkel Mistral PairRM DPO

Model
Likes
Downloads
VRAM
...dle Snorkel Mistral PairRM DPO012914 GB

Best Alternatives to Snorkel Mistral PairRM DPO

Best Alternatives
Context / RAM
Downloads
Likes
Krutrim 2 Instruct1000K / 49.3 GB14736
Ft V1 Violet1000K / 24.5 GB50
Mistral Large Instruct 2407128K / 226.7 GB7491859
Tiny Random MistralForCausalLM128K / 0 GB32521
Winterreise M732K / 14.4 GB00
Frostwind V2.1 M732K / 14.4 GB00
MistralLite32K / 14.4 GB11345435
K2S3 V0.132K / 28.7 GB60
MistralLite32K / 14.4 GB61777430
...ydaz Web AI Reasoner BaseModel32K / 14.4 GB01
Note: green Score (e.g. "73.2") means that the model is better than snorkelai/Snorkel-Mistral-PairRM-DPO.

Rank the Snorkel Mistral PairRM DPO 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  

What open-source LLMs or SLMs are you in search of? 52721 in total.

Email us: info@extractum.io. Our Privacy Policy  | Terms and Conditions  | Suggest an improvement.
Our Social Media →  
Original data from HuggingFace, OpenCompass and various public git repos.
Check out Ag3ntum — our secure, self-hosted AI agent for server management.
Release v20260328a
  Support LLM Explorer