---
base_model: unsloth/gemma-4-31B-it
tags:
- text-generation-inference
- transformers
- unsloth
- gemma4
- trl
- lora
license: apache-2.0
language:
- en
datasets:
- PawanKrd/claude-fable-5-code
- victor/fable-5-boeing-747-trace
- armand0e/claude-fable-5-claude-code
---

# Gemma 4 31B it - Claude Fable 5 Distilled

The following model was trained on claude-code traces, with some chat data provided by the community. 

I recommend using the model with claude-code or pi, though other harnesses should work without issues.


The data for this model was easily extracted, formatted, and masked for training with [Teich](https://github.com/TeichAI/teich) <img src="https://cdn-avatars.huggingface.co/v1/production/uploads/6837935ac3b7ffe0d2559ce9/-AxyvV4wfUY8uo87kNKkK.png" width="20" height="20" style="display: inline-block; vertical-align: middle; margin: 0 3px;">


## 📋 Stage Details & Benchmarks

*Reasoning was left un-touched*

*Benchmarks coming soon*

> **Deep Dive Analysis:** For more comprehensive insights regarding the base capabilities of the Gemma 4 architecture, please refer to [this Analysis Document](https://huggingface.co/TeichAI/gemma-4-31B-it-Claude-Opus-Distill/resolve/main/Gemma%204%20Analysis.pdf).


## 🌟 Core Skills & Capabilities

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

1.  **💻 Coding:** Advanced code generation, debugging, and software architecture planning.
2.  **🔬 Science:** Deep scientific reasoning, hypothesis evaluation, and analytical problem-solving.
3.  **🔎 Deep Research:** Navigating complex, multi-step research queries and synthesizing vast amounts of information.
4.  **🧠 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](https://blog.google/innovation-and-ai/technology/developers-tools/gemma-4/).
  - **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}}
}
```