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SlitherCode/
tiny-edu-166m-instruct-v0

Name: tiny-edu-166m-instruct-v0
Rating: 2.5 (9 reviews)

SlitherCode/tiny-edu-166m

finetune

mit

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tiny-edu-166M-instruct-v0

A naively instruction-tuned version of tiny-edu-166M, a 166M parameter language model built on the ParchmentLM architecture and pretrained from scratch on FineWeb.

This is v0 — a baseline instruct model trained on Alpaca-Cleaned with no filtering, curation, or preference optimization. It exists to establish a benchmark before more principled data pipeline work in future versions.

Model Details


Base Model	SlitherCode/tiny-edu-166m
Architecture	ParchmentLM (LLaMA-style, tiktoken cl100k_base tokenizer)
Parameters	166M
Pretraining Data	FineWeb (~4B tokens)
SFT Data	yahma/alpaca-cleaned (52k examples)
Training Epochs	3
Precision	bfloat16

For full architecture details see the base model repo.

Usage

Load the tokenizer from the base model and weights from this repo:

python

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("SlitherCode/tiny-edu-166m", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("SlitherCode/tiny-edu-166m-instruct-v0", trust_remote_code=True)
model.eval()

messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "What is the capital of France?"}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(prompt, return_tensors="pt")
input_len = inputs["input_ids"].shape[1]

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=200,
        do_sample=False,
        repetition_penalty=1.1,
        eos_token_id=tokenizer.eos_token_id,
        pad_token_id=tokenizer.eos_token_id,
    )

response = tokenizer.decode(outputs[0][input_len:], skip_special_tokens=True)
print(response)

Chat Template

This model uses a custom chat template with <|endoftext|> as the turn separator:

system
You are a helpful assistant.<|endoftext|>
user
What is the capital of France?<|endoftext|>
assistant

Limitations

166M parameters — limited factual knowledge and reasoning capacity
Arithmetic and multi-step reasoning are unreliable at this scale
Naively trained on Alpaca-Cleaned with no quality filtering or preference optimization
Not suitable for production use

Training Details

Trained using HuggingFace Trainer with the following configuration:

Optimizer: AdamW
Learning rate: 2e-5 with cosine decay
Warmup ratio: 0.03
Batch size: 32
Precision: bfloat16

Roadmap

v1: Retrain on a curated, category-balanced dataset derived from real-world queries with higher quality responses
v2: Retrain on a synthetically generated and curated dataset with further optimizations

License

The model weights are released under the MIT License, inherited from the base model tiny-edu-166M.

The SFT training data yahma/alpaca-cleaned is licensed under CC-BY-4.0. Per the license terms, attribution is given to the original Alpaca dataset authors (Stanford University) and the cleaned version maintainers.

Taori et al., "Alpaca: A Strong, Replicable Instruction-Following Model", Stanford University, 2023. Cleaned version: https://github.com/gururise/AlpacaDataCleaned