---
language:
- en
license: apache-2.0
library_name: transformers
pipeline_tag: text-generation
tags:
- moe
- mixture-of-experts
- chat
- sft
- fine-tuned
- pytorch
- tiny
- efficient
- trl
base_model: FlameF0X/TinyMoE-100m-2x8-retrained
datasets:
- HuggingFaceTB/smol-smoltalk
- yahma/alpaca-cleaned
- databricks/databricks-dolly-15k
- HuggingFaceH4/ultrachat_200k
- Open-Orca/OpenOrca
- teknium/openhermes
- HuggingFaceH4/no_robots
model-index:
- name: TinyMoE-100m-2x8-chat-stage1
  results: []
---

# TinyMoE-100m-2x8 Chat (Stage 1)

A **chat fine-tuned Mixture of Experts (MoE) language model** — small, efficient, and fully open-source.

TinyMoE is a ~100M parameter transformer using a **Mixture of Experts** architecture, fine-tuned via supervised fine-tuning (SFT) on a diverse blend of high-quality chat and instruction datasets. It's designed to be a compact but capable conversational AI that knows its own identity.

## Model Details

| Property | Value |
|---|---|
| **Architecture** | Mixture of Experts (MoE) Transformer |
| **Parameters** | ~100M total |
| **Experts** | 8 experts, top-2 routing per token |
| **Context Length** | 4,096 tokens (extended via linear RoPE scaling) |
| **Chat Template** | ChatML-style — `<\|system\|>`, `<\|user\|>`, `<\|assistant\|>` |
| **Base Model** | [TinyMoE-100m-2x8-retrained](https://huggingface.co/FlameF0X/TinyMoE-100m-2x8-retrained) |
| **Training Type** | Full-weight SFT (not LoRA) |
| **Precision** | bfloat16 |
| **Creator** | [FlameF0X](https://huggingface.co/FlameF0X) |
| **License** | Apache 2.0 |

## What is Mixture of Experts?

Unlike a standard dense transformer where every token goes through the same large feed-forward network, TinyMoE uses **8 expert sub-networks** with a learned router that selects the top-2 experts per token. This means:

- **More total knowledge capacity** without proportionally increasing compute
- **Sparse activation** — only a fraction of parameters fire per token
- **Efficient inference** — you get more model per FLOP

This is the same architectural family as Mixtral, but at a much smaller scale — proving that MoE works even at ~100M parameters.

## Training Recipe

### Stage 1 — Chat SFT

The base pretrained model was fine-tuned on a carefully curated mixture of chat datasets to teach conversational ability, instruction following, and model identity.

**Hardware:** NVIDIA L4 (24 GB) on [Modal](https://modal.com)  
**Framework:** TRL (Transformer Reinforcement Learning) `SFTTrainer`  
**Max examples:** 80,000 (after length filtering)

| Hyperparameter | Value |
|---|---|
| Epochs | 3 |
| Learning Rate | 2e-5 |
| LR Schedule | Cosine with 5% warmup |
| Optimizer | AdamW (weight decay 0.01) |
| Batch Size | 4 per device × 8 grad accum = 32 effective |
| Max Gradient Norm | 1.0 |
| Packing | Yes |
| Gradient Checkpointing | Yes |

### Training Datasets

| Dataset | Examples | Description |
|---|---|---|
| [SmolTalk](https://huggingface.co/datasets/HuggingFaceTB/smol-smoltalk) | ~4k | Synthetic diverse chat conversations |
| [Alpaca Cleaned](https://huggingface.co/datasets/yahma/alpaca-cleaned) | ~52k | Cleaned instruction-following data |
| [Dolly 15k](https://huggingface.co/datasets/databricks/databricks-dolly-15k) | ~15k | Human-written instruction/response pairs |
| [UltraChat 200k](https://huggingface.co/datasets/HuggingFaceH4/ultrachat_200k) | 12k subset | High-quality multi-turn chat |
| [OpenOrca](https://huggingface.co/datasets/Open-Orca/OpenOrca) | 15k subset | GPT-4 augmented FLAN instructions |
| [OpenHermes](https://huggingface.co/datasets/teknium/openhermes) | 10k subset | Diverse tasks (code, write, reason, roleplay) |
| [No Robots](https://huggingface.co/datasets/HuggingFaceH4/no_robots) | 10k | Hand-curated high-quality SFT examples |
| **TinyMoE Identity** *(custom)* | 44 | Synthetic identity Q&A + multi-turn conversations |

### Identity Training

The model was explicitly taught to know it's **TinyMoE** through two mechanisms:

1. **Dedicated identity dataset** — 44 custom examples covering name, creator, architecture, capabilities, and differentiation from other models (ChatGPT, Claude, Llama, etc.)
2. **System prompt injection** — 15% of all training examples received a system prompt: *"You are TinyMoE, a helpful Mixture of Experts AI assistant created by FlameF0X."*

This means TinyMoE knows who it is — ask it "What's your name?" or "Who created you?" and it will answer correctly.

## Usage

### Chat Format

TinyMoE uses a **ChatML-style** template with special tokens:

```
<|system|>
You are TinyMoE, a helpful Mixture of Experts AI assistant created by FlameF0X.</s>
<|user|>
What's Mixture of Experts?</s>
<|assistant|>
MoE stands for Mixture of Experts! Instead of one big neural network...</s>
```

### Quick Start

```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "FlameF0X/TinyMoE-100m-2x8-chat-stage1"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

messages = [
    {"role": "system", "content": "You are TinyMoE, a helpful Mixture of Experts AI assistant created by FlameF0X."},
    {"role": "user", "content": "What's your name and who made you?"},
]

inputs = tokenizer.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_tensors="pt",
).to(model.device)

outputs = model.generate(
    inputs,
    max_new_tokens=256,
    temperature=0.7,
    do_sample=True,
    top_p=0.9,
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
## Capabilities & Limitations

### ✅ Strengths

- **Efficient** — MoE architecture means more capacity per inference FLOP
- **Conversational** — trained on diverse multi-turn chat data
- **Self-aware** — knows it's TinyMoE, not ChatGPT/Claude/Llama
- **Open-source** — weights, architecture, and training code are all public
- **Fast** — small enough to run on consumer hardware or free-tier GPUs

### ⚠️ Limitations

- **Small model** — at 100M parameters, factual knowledge is limited compared to billion-parameter models
- **Stage 1 only** — this is a direct-answer SFT model; it hasn't undergone RLHF/DPO alignment
- **No CoT** — training explicitly excluded chain-of-thought reasoning traces (saved for future stages)
- **English only** — training data was English-dominant
- **May hallucinate** — like all LLMs, it can generate incorrect information with confidence

### Future Stages (planned)

- **Stage 2:** MCP/Tool usage + RL
- **Longer context** — potential extension beyond 4K tokens