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Hippogriff 30B Chat GPTQ by TheBloke

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Hippogriff 30B Chat GPTQ is an open-source language model by TheBloke. Features: 30b LLM, VRAM: 16.9GB, Context: 2K, License: other, Quantized, LLM Explorer Score: 0.08.

  4-bit Base model:openaccess-ai-colle... Base model:quantized:openacces...   Dataset:gsm8k   Dataset:hellaswag Dataset:metaeval/scienceqa tex... Dataset:openai/summarize from ...   Dataset:openassistant/oasst1   Dataset:qingyisi/alpaca-cot   Dataset:riddle sense Dataset:teknium/gpt4-llm-clean... Dataset:teknium/gpteacher-gene...   En   Gptq   Llama   Quantized   Region:us   Safetensors
Model Card on HF 🤗: https://huggingface.co/TheBloke/hippogriff-30b-chat-GPTQ 

Hippogriff 30B Chat GPTQ Benchmarks

LLME Score: 0.07673
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Hippogriff 30B Chat GPTQ (TheBloke/hippogriff-30b-chat-GPTQ)
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Hippogriff 30B Chat GPTQ Parameters and Internals

Model Type 
llama, text-generation
Use Cases 
Areas:
research, chat applications
Limitations:
Can produce problematic outputs., Struggles with tasks related to math., May produce socially unacceptable text.
Considerations:
We recommend having a human curate or filter the outputs before releasing them, both to censor undesirable content and to improve the quality of the results.
Additional Notes 
Build Hippogriff was built with Axolotl on 8xA100 80GB for 1.5 epochs taking approximately 12 hours.
Training Details 
Data Sources:
OpenAssistant/oasst1 - cleaned dataset, similar to Guanaco, synthetic jokes generation and explanation derived from reddit jokes dataset, synthetic prose generation and rewriting self-chat, Q&A; based on provided context, self instruct augmented logic_inference_oa, de-duped pygmalion dataset, filtered down to RP data, cleaned, english only, 25%, riddle_sense - instruct augmented, hellaswag, updated for detailed explanations w 30K+ rows, gsm8k - instruct augmented, ewof/code-alpaca-instruct-unfiltered synthetic self chat dataset derived from about 1000 rows, subset of QingyiSi/Alpaca-CoT for roleplay and CoT, GPTeacher-General-Instruct, ARC-Easy & ARC-Challenge - instruct augmented for detailed responses, hellaswag - 5K row subset of instruct augmented for concise responses, metaeval/ScienceQA_text_only - instruct for concise responses, openai/summarize_from_feedback - instruct augmented tl;dr summarization
Training Time:
12 hours for 1.5 epochs
Hardware Used:
8xA100 80GB
Safety Evaluation 
Ethical Considerations:
Hippogriff has not been aligned to human preferences with techniques like RLHF or deployed with in-the-loop filtering of responses. The model may produce problematic outputs.
Input Output 
Input Format:
A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: {prompt} ASSISTANT:
Performance Tips:
Never depend upon Hippogriff to produce factually accurate output.
LLM NameHippogriff 30B Chat GPTQ
Repository 🤗https://huggingface.co/TheBloke/hippogriff-30b-chat-GPTQ 
Model NameHippogriff 30B Chat
Model CreatorOpen Access AI Collective
Base Model(s)  Hippogriff 30B Chat   openaccess-ai-collective/hippogriff-30b-chat
Model Size30b
Required VRAM16.9 GB
Updated2026-03-26
MaintainerTheBloke
Model Typellama
Model Files  16.9 GB
Supported Languagesen
GPTQ QuantizationYes
Quantization Typegptq
Model ArchitectureLlamaForCausalLM
Licenseother
Context Length2048
Model Max Length2048
Transformers Version4.30.0.dev0
Tokenizer ClassLlamaTokenizer
Beginning of Sentence Token<s>
End of Sentence Token</s>
Unk Token<unk>
Vocabulary Size32000
Torch Data Typebfloat16

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Note: green Score (e.g. "73.2") means that the model is better than TheBloke/hippogriff-30b-chat-GPTQ.

Rank the Hippogriff 30B Chat GPTQ 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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