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Manticore 30B Chat Pyg Alpha by openaccess-ai-collective

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Manticore 30B Chat Pyg Alpha is an open-source language model by openaccess-ai-collective. Features: 30b LLM, VRAM: 65.2GB, Instruction-Based, HF Score: 58.9, LLM Explorer Score: 0.11, Arc: 64.2, HellaSwag: 84.4, MMLU: 57.5, TruthfulQA: 51.6, WinoGrande: 79.5, GSM8K: 16.1.

Dataset:anon8231489123/sharegp... Dataset:ehartford/wizard vicun... Dataset:ehartford/wizardlm alp... Dataset:ewof/code-alpaca-instr...   Dataset:gsm8k   Dataset:hellaswag Dataset:metaeval/scienceqa tex... Dataset:openai/summarize from ...   Dataset:qingyisi/alpaca-cot   Dataset:riddle sense Dataset:teknium/gpt4-llm-clean... Dataset:teknium/gpteacher-gene...   En   Endpoints compatible   Instruct   Llama   Pytorch   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/openaccess-ai-collective/manticore-30b-chat-pyg-alpha 

Manticore 30B Chat Pyg Alpha Benchmarks

ARC: 64.16 vs 96.7 (so35)-33.7%
HellaSwag: 84.38 vs 95.3 (gpt4)-11.5%
MMLU: 57.49 vs 88.3 (so35)-34.9%
TruthfulQA: 51.57 vs 59 (gpt4)-12.6%
WinoGrande: 79.48 vs 87.5 (gpt4)-9.2%
GSM8K: 16.07 vs 96.4 (so35)-83.3%
LLME Score: 0.10745
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Manticore 30B Chat Pyg Alpha (openaccess-ai-collective/manticore-30b-chat-pyg-alpha)
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Manticore 30B Chat Pyg Alpha Parameters and Internals

Model Type 
text-generation
Additional Notes 
Special thanks to Nanobit for helping with Axolotl, TheBloke for quantizing these models are more accessible to all, ehartford for cleaned datasets, and 0x000011b for the RP dataset.
Training Details 
Data Sources:
anon8231489123/ShareGPT_Vicuna_unfiltered, ehartford/wizard_vicuna_70k_unfiltered, ehartford/WizardLM_alpaca_evol_instruct_70k_unfiltered, QingyiSi/Alpaca-CoT, teknium/GPT4-LLM-Cleaned, teknium/GPTeacher-General-Instruct, metaeval/ScienceQA_text_only, hellaswag, openai/summarize_from_feedback, riddle_sense, gsm8k, ewof/code-alpaca-instruct-unfiltered
Data Volume:
40% of the datasets
Methodology:
fine-tuning
Training Time:
14 hours
Hardware Used:
8xA100 80GB
Model Architecture:
Llama
Input Output 
Input Format:
chat only style prompts using `USER:` and `ASSISTANT:` or `<|system|>, <|user|> and <|model|>` tokens
Accepted Modalities:
text
Output Format:
text
Release Notes 
Notes:
Alpha release of checkpoint before train and eval loss spikes. Some alignment issues were noted.
LLM NameManticore 30B Chat Pyg Alpha
Repository 🤗https://huggingface.co/openaccess-ai-collective/manticore-30b-chat-pyg-alpha 
Model Size30b
Required VRAM65.2 GB
Updated2026-01-23
Maintaineropenaccess-ai-collective
Model Typellama
Instruction-BasedYes
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   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
Model Max Length2048
Transformers Version4.28.0.dev0
Tokenizer ClassLlamaTokenizer
Beginning of Sentence Token<s>
End of Sentence Token</s>
Unk Token<unk>
Vocabulary Size32000
Torch Data Typefloat16

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

Rank the Manticore 30B Chat Pyg Alpha 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  
Multi-Language Support and Translation  

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