Text Generation
PEFT
Safetensors
English
Russian
reasoning
math
logic
unsloth
gemma-4
lora
conversational
Eval Results (legacy)
Instructions to use Zhantas/DeepGemma-E4B-Reasoning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Zhantas/DeepGemma-E4B-Reasoning with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/gemma-4-e4b-it-unsloth-bnb-4bit") model = PeftModel.from_pretrained(base_model, "Zhantas/DeepGemma-E4B-Reasoning") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use Zhantas/DeepGemma-E4B-Reasoning with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for Zhantas/DeepGemma-E4B-Reasoning to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for Zhantas/DeepGemma-E4B-Reasoning to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Zhantas/DeepGemma-E4B-Reasoning to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="Zhantas/DeepGemma-E4B-Reasoning", max_seq_length=2048, )
Update README.md
Browse files
README.md
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- Roman1111111/claude-opus-4.6-10000x
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- Jackrong/GLM5.1-Reasoning-1M-Cleaned
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pipeline_tag: text-generation
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---
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- Roman1111111/claude-opus-4.6-10000x
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- Jackrong/GLM5.1-Reasoning-1M-Cleaned
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pipeline_tag: text-generation
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model-index:
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- name: DeepGemma-E4B-Reasoning
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results:
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: GSM8K
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type: openai/gsm8k
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config: main
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split: test
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metrics:
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- type: accuracy
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value: 62.0
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name: accuracy
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verified: false
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: HellaSwag
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type: Rowan/hellaswag
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split: validation
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metrics:
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- type: accuracy
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value: 86.0
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name: accuracy
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verified: false
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: ARC-Challenge
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type: allenai/ai2_arc
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config: ARC-Challenge
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split: test
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metrics:
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- type: accuracy
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value: 96.0
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name: accuracy
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verified: false
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: TruthfulQA
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type: truthfulqa/truthful_qa
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config: multiple_choice
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split: validation
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metrics:
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- type: accuracy
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value: 79.0
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name: accuracy
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verified: false
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: MMLU
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type: cais/mmlu
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config: all
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split: test
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metrics:
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- type: accuracy
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value: 69.0
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name: accuracy
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verified: false
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---
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