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---
library_name: transformers
license: apache-2.0
base_model: Shekswess/trlm-stage-2-sft-final-2
tags:
- trl
- dpo
- preference-alignment
- reasoning
- generated_from_trainer
model-index:
- name: trlm-stage-3-dpo-final-2
results: []
---
# Tiny Reasoning Language Model (trlm-135)
![image/png](https://github.com/user-attachments/assets/5f453496-8180-4cf4-94da-26ebbe1159d4)
## Table of Contents
1. [Model Summary](#model-summary)
2. [Post-Training Pipeline](#post-training-pipeline)
3. [How to use](#how-to-use)
4. [Training](#training)
5. [Evaluation](#evaluation)
6. [Limitations](#limitations)
7. [Acknowledgements](#acknowledgements)
8. [License](#license)
---
## Model Summary
The **Tiny Reasoning Language Model (trlm-135)** is a **135M parameter** research prototype designed to study how small models can learn step-by-step reasoning.
It was built on top of [SmolLM2-135M-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM2-135M-Instruct) and fine-tuned through a **3-stage pipeline**:
* **[Stage 1 SFT](https://huggingface.co/Shekswess/trlm-stage-1-sft-final-2)**: general instruction tuning (non-reasoning).
* **[Stage 2 SFT](https://huggingface.co/Shekswess/trlm-stage-2-sft-final-2)**: reasoning traces with `<think>` tags.
* **[Stage 3 DPO](https://huggingface.co/Shekswess/trlm-stage-3-dpo-final-2)**: preference alignment for reasoning style.
The **code** for everything can be found **[here](https://github.com/Shekswess/tiny-reasoning-language-model/blob/main/README.md)**
---
## Post-Training Pipeline
<img width="1014" height="563" alt="image" src="https://github.com/user-attachments/assets/195ef389-6aa9-4527-b4f0-bea68c0841ae" />
---
## How to use
```bash
pip install -U transformers accelerate
```
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "Shekswess/trlm-135m"
device = "cuda" # or "cpu"
# Load tokenizer & model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
).to(device)
# Example prompt
prompt = "Give me a brief explanation of gravity in simple terms."
messages = [
{"role": "user", "content": prompt}
]
# Apply chat template
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
)
inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
> [!TIP]
> For reasoning-heavy tasks, set `temperature=0.6` and `top_p=0.95`.
---
## Training
### Model
* **Architecture**: Decoder-only transformer (SmolLM2 backbone which infact is Llama 3 based model).
* **Parameters**: ~135M.
* **Precision**: mix-precision (bfloat16) during training.
### Software & Hardware
* **Training Frameworks**: PyTorch (ROCm), Hugging Face Transformers & TRL.
* **Hardware**: AMD MI300X (192GB VRAM, 224GB RAM).
**Special thanks to [@HotAisle](https://x.com/HotAisle)**
### Training Stages
1. **Stage 1 – SFT (non-reasoning)**
* ~58k samples, everyday conversations & instruction following.
2. **Stage 2 – SFT (reasoning)**
* ~78k samples with `<think>` segments.
3. **Stage 3 – DPO (alignment)**
* ~50k preference pairs (chosen vs. rejected reasoning traces).
---
## Evaluation
Evaluation was done with `lm-eval-harness`:
| **Benchmark** | **Tiny Reasoning Language Model (trlm-135M)** | **SmolLM2-135M-Instruct** | **Improvements** |
| -------------------- | ---------------------------- | ------------------------- | ---------------------------- |
| **ARC Challenge** | **40.61** (avg) | 37.3 (avg) | **+3.31** |
| **BBH** | **36.80** (3-shot) | 28.2 (3-shot) | **+8.6** |
| **BoolQ** | **62.17** | – | N/A |
| **GSM8K** | **2.59** (5-shot) | 1.4 (5-shot) | **+1.19** |
| **IFEval** | **35.49** (avg) | 29.9 (avg) | **+5.59** |
| **MMLU** | **34.95** | 29.3 | **+5.65** |
| **PIQA** | **64.91** | 66.3 | **–1.39** |
| **HellaSwag** | – | 40.9 | N/A |
| **MT-Bench** | – | 19.8 | N/A |
---
## Limitations
* **Not production-ready**: hallucinations and logical errors are frequent.
* **Small size**: limited general knowledge and reasoning depth.
* **English-only**: multilingual capabilities not explored.
---
## Acknowledgements
- [@HotAisle](https://x.com/HotAisle) for providing the compute resources to train all three stages on a awesome AMD MI300x setup.
- [@mkurman88](https://x.com/mkurman88) for ideas, feedback and code samples.
- [HuggingFaceTB team](https://huggingface.co/HuggingFaceTB) for SmolLM2-135M-Instruct model and the Smoltalk2 dataset collection.
- [@scottgeng00](https://huggingface.co/scottgeng00) for the OLmO-3-Preference-Mix-Deltas dataset.
- [@eliebakouchi](https://x.com/eliebakouch) for help with the tokenization.
---
## License
[Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0)
---

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{% for message in messages %}
{% if loop.first and messages[0]['role'] != 'system' %}
{{ '<|im_start|>system\nYou are a helpful AI assistant named Tiny Reasoning Language Model, trained by Shekswess. You are an assistant, with the ability to do reasoning. When performing reasoning always perform your full chain of thought inside <think>...</think> before giving a final answer. You are always reasoning so always use <think> </think> tags.<|im_end|>\n' }}
{% endif %}
{{ '<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>\n' }}
{% endfor %}
{% if add_generation_prompt %}
{{ '<|im_start|>assistant\n' }}
{% endif %}

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