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language:
- en
license: mit
library_name: transformers
pipeline_tag: text-generation
tags:
- tiny-models
- custom-architecture
- story-generation
- experimental
---
# Spin-80k
**Spin-80k** is a lightweight, 80k-parameter decoder-only language model built from scratch by **Quantech** to demonstrate custom Transformer architecture
---
## Model Specifications
* **Organization:** Quantech
* **Architecture:** Custom Decoder-only Transformer
* **Total Parameters:** ~80,112
* **Layers:** 2
* **Hidden Dimension ($d_{\text{model}}$):** 48
* **Attention Heads:** 4
* **Feed-Forward Dimension ($d_{\text{ff}}$):** 128
* **Positional Encoding:** Rotary Position Embeddings (RoPE)
* **Normalization:** RMSNorm ($\epsilon = 10^{-5}$)
* **Activation:** SwiGLU
* **Vocabulary:** 512 Byte-Pair Encoding (BPE) tokens
* **Context Length:** 256 tokens
---
## Quickstart
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
repo_id = "Quantech/spin-80k"
# Load Tokenizer & Model
tokenizer = AutoTokenizer.from_pretrained(repo_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(repo_id, trust_remote_code=True)
model.eval()
# ChatML Format
prompt = "<|im_start|>user\nWrite a short story about a dog.<|im_end|>\n<|im_start|>assistant\n"
inputs = tokenizer(prompt, return_tensors="pt")
with torch.no_grad():
outputs = model.generate(
**inputs,
max_new_tokens=50,
temperature=0.7,
do_sample=True,
pad_token_id=tokenizer.eos_token_id
)
print(tokenizer.decode(outputs[0])) |