---
language:
- en
- code
license: apache-2.0
tags:
- smol
- pretraining
- instruct
- 50M
- causal-lm
- gqa
- swiglu
- rmsnorm
datasets:
- HuggingFaceTB/smollm-corpus
metrics:
- perplexity
model-index:
- name: Quark-50m-Instruct
  results: []
pipeline_tag: text-generation
---

# Quark-50m-Instruct

**Quark-50m-Instruct** is a small (≈56M parameters) decoder-only language model, fine-tuned for instruction following.
It is built on the same architecture of “SmolLM” family and was fully pretrained on 5 billion tokens from
[HuggingFaceTB/smollm‑corpus](https://huggingface.co/datasets/HuggingFaceTB/smollm-corpus).

- **Model type:** Causal Language Model (LLaMA‑style decoder)
- **Architecture:** GQA · SwiGLU · RMSNorm · RoPE · Weight‑tying
- **Pretraining tokens:** 5 B
- **Fine‑tuning:** Instruction‑tuned (details below)
- **Creators:** [OvercastLab](https://huggingface.co/OvercastLab) (research & development lab for ML/AI)
- **Release date:** 22 April 2026

## Model Summary

Quark-50m-Instruct is designed to be an efficient assistant that can run on consumer GPUs (e.g., RTX 3070 with 8 GB VRAM)
and even on CPU for light workloads. It is **not** competitive with large models on knowledge‑intensive tasks,
but it excels at:

- Simple conversational tasks
- Code generation and explanation (Python)
- Short text rewriting and summarisation
- On‑device / edge inference

The architecture closely follows the efficient‑small‑LM blueprint popularised by SmolLM:

| Component   | Details                       |
|-------------|-------------------------------|
| Vocab size  | 49,152                        |
| Hidden size | 384                           |
| Layers      | 24                            |
| Attention   | Grouped Query (6 Q heads, 2 KV heads) |
| FFN         | SwiGLU with 1,024 intermediate |
| Position    | RoPE (θ = 10,000)             |
| Normalisation | RMSNorm (pre‑block)         |

Total trainable parameters: **≈48 M** (with weight tying).

### Benchmark Evaluation Metrics

| Category | Benchmark | Metric | Score / Value | Status |
| :--- | :--- | :--- | :---: | :---: |
| **Linguistics & Grammar** | BLiMP | Accuracy | 68.12% | Success |
| **Commonsense & Reasoning** | PIQA | Normalized Accuracy | 57.83% | Success |
| | COPA | Accuracy | 57.00% | Success |
| | BoolQ | Accuracy | 52.17% | Success |
| | WinoGrande | Accuracy | 47.36% | Success |
| | HellaSwag | Normalized Accuracy | 28.49% | Success |
| | RACE | Accuracy | 26.41% | Success |
| | CommonsenseQA | Accuracy | 20.31% | Success |
| **Academic & Knowledge** | SciQ | Normalized Accuracy | 49.00% | Success |
| | ARC-Easy | Normalized Accuracy | 36.49% | Success |
| | MMLU | Accuracy | 25.64% | Success |
| | ARC-Challenge | Normalized Accuracy | 25.17% | Success |
| | OpenBookQA | Normalized Accuracy | 25.40% | Success |
| **Language Modeling** | LAMBADA | Accuracy | 15.87% | Success |
| | WikiText-2 | Word Perplexity | 251.76 | Success |

*Note: The Arithmetic benchmark failed due to outdated script support (`arithmetic.py`), and SocialIQA failed due to a registration tag error (`siqa`). Total baseline execution completed successfully for all other 15 tasks.*


## Uses

### Direct Use
The model can be used via the 🤗 Transformers library for standard text generation.
It expects chat‑formatted input (see example below).

### Downstream Use
Because of the open Apache‑2.0 license, you may fine‑tune Quark-50m‑Instruct on your own data for
domain‑specific tasks – for instance, a customer‑support bot, a code reviewer, or a story writer.

### Limitations
- Limited world knowledge (stopped at mid‑2025 pretraining data).
- Short context window (2,048 tokens).
- Small size means it can make more factual mistakes than larger models.

## How to Get Started

```python
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "ThingAI/Quark-50m-Instruct"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

messages = [
    {"role": "system", "content": "You are Quark, a helpful assistant."},
    {"role": "user", "content": "Explain group query attention in one sentence."}
]

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=128)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))