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Model: FlameF0X/TinyMoE-100m-2x8-chat-stage1 Source: Original Platform
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README.md
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README.md
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---
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language:
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- en
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license: apache-2.0
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library_name: transformers
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pipeline_tag: text-generation
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tags:
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- moe
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- mixture-of-experts
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- chat
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- sft
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- fine-tuned
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- pytorch
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- tiny
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- efficient
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- trl
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base_model: FlameF0X/TinyMoE-100m-2x8-retrained
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datasets:
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- HuggingFaceTB/smol-smoltalk
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- yahma/alpaca-cleaned
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- databricks/databricks-dolly-15k
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- HuggingFaceH4/ultrachat_200k
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- Open-Orca/OpenOrca
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- teknium/openhermes
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- HuggingFaceH4/no_robots
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model-index:
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- name: TinyMoE-100m-2x8-chat-stage1
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results: []
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---
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# TinyMoE-100m-2x8 Chat (Stage 1)
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A **chat fine-tuned Mixture of Experts (MoE) language model** — small, efficient, and fully open-source.
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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.
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## Model Details
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| Property | Value |
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|---|---|
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| **Architecture** | Mixture of Experts (MoE) Transformer |
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| **Parameters** | ~100M total |
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| **Experts** | 8 experts, top-2 routing per token |
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| **Context Length** | 4,096 tokens (extended via linear RoPE scaling) |
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| **Chat Template** | ChatML-style — `<\|system\|>`, `<\|user\|>`, `<\|assistant\|>` |
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| **Base Model** | [TinyMoE-100m-2x8-retrained](https://huggingface.co/FlameF0X/TinyMoE-100m-2x8-retrained) |
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| **Training Type** | Full-weight SFT (not LoRA) |
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| **Precision** | bfloat16 |
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| **Creator** | [FlameF0X](https://huggingface.co/FlameF0X) |
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| **License** | Apache 2.0 |
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## What is Mixture of Experts?
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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:
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- **More total knowledge capacity** without proportionally increasing compute
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- **Sparse activation** — only a fraction of parameters fire per token
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- **Efficient inference** — you get more model per FLOP
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This is the same architectural family as Mixtral, but at a much smaller scale — proving that MoE works even at ~100M parameters.
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## Training Recipe
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### Stage 1 — Chat SFT
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The base pretrained model was fine-tuned on a carefully curated mixture of chat datasets to teach conversational ability, instruction following, and model identity.
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**Hardware:** NVIDIA L4 (24 GB) on [Modal](https://modal.com)
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**Framework:** TRL (Transformer Reinforcement Learning) `SFTTrainer`
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**Max examples:** 80,000 (after length filtering)
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| Hyperparameter | Value |
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|---|---|
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| Epochs | 3 |
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| Learning Rate | 2e-5 |
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| LR Schedule | Cosine with 5% warmup |
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| Optimizer | AdamW (weight decay 0.01) |
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| Batch Size | 4 per device × 8 grad accum = 32 effective |
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| Max Gradient Norm | 1.0 |
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| Packing | Yes |
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| Gradient Checkpointing | Yes |
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### Training Datasets
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| Dataset | Examples | Description |
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|---|---|---|
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| [SmolTalk](https://huggingface.co/datasets/HuggingFaceTB/smol-smoltalk) | ~4k | Synthetic diverse chat conversations |
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| [Alpaca Cleaned](https://huggingface.co/datasets/yahma/alpaca-cleaned) | ~52k | Cleaned instruction-following data |
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| [Dolly 15k](https://huggingface.co/datasets/databricks/databricks-dolly-15k) | ~15k | Human-written instruction/response pairs |
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| [UltraChat 200k](https://huggingface.co/datasets/HuggingFaceH4/ultrachat_200k) | 12k subset | High-quality multi-turn chat |
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| [OpenOrca](https://huggingface.co/datasets/Open-Orca/OpenOrca) | 15k subset | GPT-4 augmented FLAN instructions |
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| [OpenHermes](https://huggingface.co/datasets/teknium/openhermes) | 10k subset | Diverse tasks (code, write, reason, roleplay) |
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| [No Robots](https://huggingface.co/datasets/HuggingFaceH4/no_robots) | 10k | Hand-curated high-quality SFT examples |
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| **TinyMoE Identity** *(custom)* | 44 | Synthetic identity Q&A + multi-turn conversations |
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### Identity Training
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The model was explicitly taught to know it's **TinyMoE** through two mechanisms:
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1. **Dedicated identity dataset** — 44 custom examples covering name, creator, architecture, capabilities, and differentiation from other models (ChatGPT, Claude, Llama, etc.)
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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."*
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This means TinyMoE knows who it is — ask it "What's your name?" or "Who created you?" and it will answer correctly.
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## Usage
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### Chat Format
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TinyMoE uses a **ChatML-style** template with special tokens:
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```
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<|system|>
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You are TinyMoE, a helpful Mixture of Experts AI assistant created by FlameF0X.</s>
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<|user|>
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What's Mixture of Experts?</s>
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<|assistant|>
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MoE stands for Mixture of Experts! Instead of one big neural network...</s>
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```
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### Quick Start
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```python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_id = "FlameF0X/TinyMoE-100m-2x8-chat-stage1"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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torch_dtype=torch.bfloat16,
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device_map="auto",
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)
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messages = [
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{"role": "system", "content": "You are TinyMoE, a helpful Mixture of Experts AI assistant created by FlameF0X."},
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{"role": "user", "content": "What's your name and who made you?"},
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]
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inputs = tokenizer.apply_chat_template(
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messages,
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tokenize=True,
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add_generation_prompt=True,
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return_tensors="pt",
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).to(model.device)
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outputs = model.generate(
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inputs,
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max_new_tokens=256,
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temperature=0.7,
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do_sample=True,
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top_p=0.9,
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)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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## Capabilities & Limitations
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### ✅ Strengths
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- **Efficient** — MoE architecture means more capacity per inference FLOP
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- **Conversational** — trained on diverse multi-turn chat data
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- **Self-aware** — knows it's TinyMoE, not ChatGPT/Claude/Llama
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- **Open-source** — weights, architecture, and training code are all public
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- **Fast** — small enough to run on consumer hardware or free-tier GPUs
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### ⚠️ Limitations
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- **Small model** — at 100M parameters, factual knowledge is limited compared to billion-parameter models
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- **Stage 1 only** — this is a direct-answer SFT model; it hasn't undergone RLHF/DPO alignment
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- **No CoT** — training explicitly excluded chain-of-thought reasoning traces (saved for future stages)
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- **English only** — training data was English-dominant
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- **May hallucinate** — like all LLMs, it can generate incorrect information with confidence
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### Future Stages (planned)
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- **Stage 2:** MCP/Tool usage + RL
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- **Longer context** — potential extension beyond 4K tokens
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