Deep Dive Analysis: For more comprehensive insights regarding the base capabilities of the Gemma 4 architecture, please refer to this Analysis Document.

🌟 Core Skills & Capabilities

Thanks to its robust base model and high-effort reasoning distillation, this model is highly optimized for the following use cases:

💻 Coding: Advanced code generation, debugging, and software architecture planning.
🔬 Science: Deep scientific reasoning, hypothesis evaluation, and analytical problem-solving.
🔎 Deep Research: Navigating complex, multi-step research queries and synthesizing vast amounts of information.
🧠 General Purpose: Highly capable instruction-following for everyday tasks requiring high logical coherence.

Getting Started

You can use all Gemma 4 models with the latest version of Transformers. To get started, install the necessary dependencies in your environment:

pip install -U transformers torch accelerate

Once you have everything installed, you can proceed to load the model with the code below:

python

from transformers import AutoProcessor, AutoModelForCausalLM

MODEL_ID = "google/gemma-4-31B-it"

# Load model
processor = AutoProcessor.from_pretrained(MODEL_ID)
model = AutoModelForCausalLM.from_pretrained(
    MODEL_ID,
    dtype="auto",
    device_map="auto"
)

Once the model is loaded, you can start generating output:

python

# Prompt
messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "Write a short joke about saving RAM."},
]

# Process input
text = processor.apply_chat_template(
    messages, 
    tokenize=False, 
    add_generation_prompt=True, 
    enable_thinking=False
)
inputs = processor(text=text, return_tensors="pt").to(model.device)
input_len = inputs["input_ids"].shape[-1]

# Generate output
outputs = model.generate(**inputs, max_new_tokens=1024)
response = processor.decode(outputs[0][input_len:], skip_special_tokens=False)

# Parse output
processor.parse_response(response)

To enable reasoning, set enable_thinking=True and the parse_response function will take care of parsing the thinking output.

Best Practices

For the best performance, use these configurations and best practices:

1. Sampling Parameters

Use the following standardized sampling configuration across all use cases:

temperature=1.0
top_p=0.95
top_k=64

2. Thinking Mode Configuration

Compared to Gemma 3, the models use standard system, assistant, and user roles. To properly manage the thinking process, use the following control tokens:

Trigger Thinking: Thinking is enabled by including the <|think|> token at the start of the system prompt. To disable thinking, remove the token.
Standard Generation: When thinking is enabled, the model will output its internal reasoning followed by the final answer using this structure:
<|channel>thought\n[Internal reasoning]<channel|>
Disabled Thinking Behavior: For all models except for the E2B and E4B variants, if thinking is disabled, the model will still generate the tags but with an empty thought block:
<|channel>thought\n<channel|>[Final answer]

[!Note] Note that many libraries like Transformers and llama.cpp handle the complexities of the chat template for you.

🙏 Acknowledgements

Google: For providing an exceptional open weights model. Read more about Gemma 4 on the Google Innovation Blog.
Unsloth: For assembling ready-to-use, cutting-edge fine-tuning environments that make this work possible.
PawanKrd, victor and armand0e: For creating and sharing their awesome Fable datasets with the community.

📖 Citation

If you use this model in your research or projects, please cite:

bibtex

@misc{teichai_gemma4_31b_fable_5_agent_distilled,
  title        = {TeichAI/Gemma-4-31B-Fable-5-Agent-Distill},
  author       = {TeichAI},
  year         = {2026},
  publisher    = {Hugging Face},
  howpublished = {\url{https://huggingface.co/TeichAI/Gemma-4-31B-Fable-5-Agent-Distill}}
}

TeichAI/Gemma-4-31B-Fable-5-Agent-Distill-LoRA

Gemma 4 31B it - Claude Fable 5 Distilled

📋 Stage Details & Benchmarks