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Hippogriff 30B Chat by openaccess-ai-collective

 »  All LLMs  »  openaccess-ai-collective  »  Hippogriff 30B Chat   URL Share it on

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  Autotrain compatible   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   Endpoints compatible   Llama   Pytorch   Region:us   Sharded
Model Card on HF 🤗: https://huggingface.co/openaccess-ai-collective/hippogriff-30b-chat 

Hippogriff 30B Chat Benchmarks

ARC: 64.51 vs 96.7 (so35)-33.3%
HellaSwag: 85.2 vs 95.3 (gpt4)-10.6%
MMLU: 59.09 vs 88.3 (so35)-33.1%
TruthfulQA: 48.42 vs 59 (gpt4)-17.9%
WinoGrande: 80.82 vs 87.5 (gpt4)-7.6%
GSM8K: 10.24 vs 96.4 (so35)-89.4%
LLME Score: 0.12336
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 (openaccess-ai-collective/hippogriff-30b-chat)
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Hippogriff 30B Chat Parameters and Internals

Model Type 
text-generation
Additional Notes 
Hippogriff differs from Manticore due to the exclusion of specific datasets like WizardLM, WizardVicuna, Alpaca, or ShareGPT. The model's performance is better in prose and Q&A but struggles with mathematical tasks.
Supported Languages 
en (Proficient)
Training Details 
Data Sources:
OpenAssistant/oasst1, 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, hellaswag, updated for detailed explanations w 30K+ rows, gsm8k, ewof/code-alpaca-instruct-unfiltered, subset of QingyiSi/Alpaca-CoT for roleplay and CoT, GPTeacher-General-Instruct, ARC-Easy & ARC-Challenge, hellaswag, metaeval/ScienceQA_text_only, openai/summarize_from_feedback
Methodology:
Fine-tuning with specific datasets
Training Time:
12 hours on 8xA100 80GB for 1.5 epochs
Hardware Used:
8xA100 80GB
Input Output 
Input Format:
USER:, ASSISTANT:, and <|system|>, <|user|> and <|model|> tokens
Accepted Modalities:
text
Output Format:
Textual responses
LLM NameHippogriff 30B Chat
Repository 🤗https://huggingface.co/openaccess-ai-collective/hippogriff-30b-chat 
Model Size30b
Required VRAM65.2 GB
Updated2025-09-23
Maintaineropenaccess-ai-collective
Model Typellama
Model Files  9.8 GB: 1-of-7   10.0 GB: 2-of-7   9.9 GB: 3-of-7   9.9 GB: 4-of-7   9.9 GB: 5-of-7   10.0 GB: 6-of-7   5.7 GB: 7-of-7
Supported Languagesen
Model ArchitectureLlamaForCausalLM
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

Quantized Models of the Hippogriff 30B Chat

Model
Likes
Downloads
VRAM
Hippogriff 30B Chat GGUF147813 GB
Hippogriff 30B Chat AWQ11517 GB
Hippogriff 30B Chat GPTQ13916 GB

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

Rank the Hippogriff 30B Chat 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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Release v20241124
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