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Snorkel Mistral PairRM DPO 8.0bpw H8 EXL2 by LoneStriker

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Snorkel Mistral PairRM DPO 8.0bpw H8 EXL2 is an open-source language model by LoneStriker. Features: LLM, VRAM: 7.4GB, Context: 32K, License: apache-2.0, Quantized, LLM Explorer Score: 0.12.

  Arxiv:2305.18290   Arxiv:2306.02561   Arxiv:2401.10020   Autotrain compatible   Conversational Dataset:snorkelai/snorkel-mist...   Endpoints compatible   Exl2   Mistral   Pytorch   Quantized   Region:us
Model Card on HF 🤗: https://huggingface.co/LoneStriker/Snorkel-Mistral-PairRM-DPO-8.0bpw-h8-exl2 

Snorkel Mistral PairRM DPO 8.0bpw H8 EXL2 Benchmarks

LLME Score: 0.11591
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 8.0bpw H8 EXL2 (LoneStriker/Snorkel-Mistral-PairRM-DPO-8.0bpw-h8-exl2)
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Snorkel Mistral PairRM DPO 8.0bpw H8 EXL2 Parameters and Internals

Model Type 
text-generation
Use Cases 
Limitations:
No moderation mechanisms included.
Additional Notes 
Endpoint for initial trials, not ongoing production use.
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. 2. Apply 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]
LLM NameSnorkel Mistral PairRM DPO 8.0bpw H8 EXL2
Repository 🤗https://huggingface.co/LoneStriker/Snorkel-Mistral-PairRM-DPO-8.0bpw-h8-exl2 
Required VRAM7.4 GB
Updated2025-09-23
MaintainerLoneStriker
Model Typemistral
Model Files  7.4 GB   0.0 GB
Quantization Typeexl2
Model ArchitectureMistralForCausalLM
Licenseapache-2.0
Context Length32768
Model Max Length32768
Transformers Version4.34.0
Tokenizer ClassLlamaTokenizer
Padding Token</s>
Vocabulary Size32000
Torch Data Typebfloat16

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Rank the Snorkel Mistral PairRM DPO 8.0bpw H8 EXL2 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  
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