# summerMC/TRM-text-v4
# TRM-text-v4
Experimental Japanese Reasoning Language Model
## Overview
**TRM-text-v4** is an experimental Japanese language model developed to explore improved reasoning capabilities and extended context handling.
The model is designed to generate intermediate reasoning traces using `` tags before producing final answers. It combines efficient fine-tuning techniques with experimental approaches to context extension.
> **Status:** Research Preview / Experimental Release
---
## Features
### Japanese Chain-of-Thought Reasoning
TRM-text-v4 is fine-tuned to produce structured reasoning steps in Japanese using `` tags.
Example:
```text
User: 富士山の標高は？
Assistant:
質問は富士山の標高についてである。
一般的に知られている標高を確認する。
富士山の標高は3776メートルである。
富士山の標高は3,776メートルです。
```
---
### Extended Context Experiments
The model incorporates experimental RoPE scaling techniques to investigate longer-context capabilities beyond the original configuration.
Current implementation focuses on practical extensions while evaluating numerical stability and hardware constraints.
---
### Efficient Fine-Tuning
TRM-text-v4 was trained using parameter-efficient fine-tuning methods:
* 4-bit NF4 quantization via BitsAndBytes
* LoRA adaptation
* Memory-efficient optimization for consumer GPUs
---
## Model Details
| Item | Value |
| -------------- | -------------------------------------- |
| Model Name | TRM-text-v4 |
| Architecture | MoE-based Causal Language Model |
| Language | Japanese |
| License | Apache-2.0 (or specify actual license) |
| Intended Use | Research and experimentation |
| Release Status | Experimental |
---
## Training Configuration
### Fine-Tuning
* LoRA Rank (`r`): **16**
* LoRA Alpha: **32**
* Target Modules: **All Linear Layers**
* Learning Rate: **3e-4**
* Precision: **FP16**
* Quantization: **4-bit NF4**
### Context Configuration
* RoPE Scaling: **Dynamic**
* RoPE Scaling Factor: **2.0**
* Maximum Position Embeddings: **2048**
> Long-context support beyond this configuration remains under investigation and should be considered experimental.
---
## Datasets
### Instruction Tuning
* **Databricks Dolly-15k-ja**
* Used to improve Japanese instruction-following ability.
### Reasoning Data
* Reasoning templates inspired by datasets such as:
* Magpie
* Sakura Reasoning
> Portions of the reasoning pipeline are experimental and may evolve in future releases.
---
## Usage
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "summerMC/TRM-text-v4"
tokenizer = AutoTokenizer.from_pretrained(
model_id,
trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
model_id,
trust_remote_code=True,
)
prompt = """User: 人工知能とは？
Assistant:
"""
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(
**inputs,
max_new_tokens=256,
do_sample=True,
temperature=0.7,
top_p=0.9,
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
---
## Prompt Format
Recommended prompt template:
```text
User: [Question]
Assistant:
[Reasoning Process]
[Final Answer]
```
---
## Limitations
* Reasoning traces may occasionally contain mixed Japanese and English terminology.
* Generated reasoning processes are not guaranteed to reflect the model's actual internal computation.
* Long-context behavior beyond validated ranges may exhibit instability.
* The model may generate incorrect or fabricated information and should not be used in high-risk domains without human verification.
---
## Intended Use
Recommended uses:
* Research on Japanese reasoning language models
* Prompt engineering experiments
* Educational demonstrations
* Evaluation of Chain-of-Thought prompting strategies
Not recommended for:
* Medical decision-making
* Legal advice
* Financial advice
* Safety-critical applications
---
## Citation
```bibtex
@misc{summermc_trm_text_v4,
title = {TRM-text-v4: Experimental Japanese Reasoning Language Model},
author = {summerMC},
year = {2026},
publisher = {Hugging Face},
howpublished = {\url{https://huggingface.co/summerMC/TRM-text-v4}}
}
```
---
## Acknowledgements
TRM-text-v4 builds upon the open-source ecosystem, including:
* Hugging Face Transformers
* PEFT
* BitsAndBytes
* Databricks Dolly
* The broader open-source LLM community
Special thanks to all researchers and contributors advancing efficient Japanese language modeling.