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Gemma2 9B It Psy10k Mental Health by ehristoforu

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Gemma2 9B It Psy10k Mental Health is an open-source language model by ehristoforu. Features: 9b LLM, VRAM: 18.6GB, Context: 8K, License: apache-2.0, LLM Explorer Score: 0.22.

  Autotrain compatible Base model:ehristoforu/gemma2-... Base model:finetune:ehristofor...   Conversational Dataset:jtatman/mental health ...   En   Endpoints compatible   Gemma2   Pytorch   Region:us   Safetensors   Sft   Sharded   Tensorflow   Trl   Unsloth
Model Card on HF 🤗: https://huggingface.co/ehristoforu/Gemma2-9B-it-psy10k-mental_health 

Gemma2 9B It Psy10k Mental Health Benchmarks

MMLU Pro: 31.43
GPQA: 11.63
MUSR: 9.34
BBH: 35.57
IFEval: 58.87 vs 88 (so35)-33.1%
MATH Lvl 5: 16.31
LLME Score: 0.22232
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Gemma2 9B It Psy10k Mental Health (ehristoforu/Gemma2-9B-it-psy10k-mental_health)
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Gemma2 9B It Psy10k Mental Health Parameters and Internals

Model Type 
text-generation-inference
Additional Notes 
This Gemma2 model was trained 2x faster with Unsloth and Huggingface's TRL library.
Supported Languages 
en (high)
LLM NameGemma2 9B It Psy10k Mental Health
Repository 🤗https://huggingface.co/ehristoforu/Gemma2-9B-it-psy10k-mental_health 
Base Model(s)  ehristoforu/Gemma2-9B-it-psy10k   ehristoforu/Gemma2-9B-it-psy10k
Model Size9b
Required VRAM18.6 GB
Updated2025-09-23
Maintainerehristoforu
Model Typegemma2
Model Files  4.9 GB: 1-of-4   5.0 GB: 2-of-4   5.0 GB: 3-of-4   3.7 GB: 4-of-4   4.9 GB: 1-of-4   5.0 GB: 2-of-4   5.0 GB: 3-of-4   3.7 GB: 4-of-4
Supported Languagesen
Model ArchitectureGemma2ForCausalLM
Licenseapache-2.0
Context Length8192
Model Max Length8192
Transformers Version4.42.4
Tokenizer ClassGemmaTokenizer
Padding Token<pad>
Vocabulary Size256000
Torch Data Typefloat16

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Note: green Score (e.g. "73.2") means that the model is better than ehristoforu/Gemma2-9B-it-psy10k-mental_health.

Rank the Gemma2 9B It Psy10k Mental Health 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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