{
  "eval_loss": 7.388123512268066,
  "eval_runtime": 9.5676,
  "eval_samples_per_second": 26.757,
  "eval_steps_per_second": 3.345,
  "epoch": 0.78125,
  "perplexity": 1616.6696041601817,
  "train_loss": 7.8487934923172,
  "params": 10403040
}

This is intentionally tiny and trained briefly as an architecture POC, not a competitive LM.

Reproduce

bash

pip install "transformers>=4.54.0" datasets torch accelerate trackio
python hybrid_lm_poc.py \
  --max_steps 200 \
  --max_train_samples 2048 \
  --max_eval_samples 256 \
  --batch_size 8 \
  --d_model 96 \
  --n_layer 6 \
  --block_size 64 \
  --learning_rate 8e-4

Sample generation after the short run is in sample_generation.txt.

Generated by ML Intern

This model repository was generated by ML Intern, an agent for machine learning research and development on the Hugging Face Hub.

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Usage

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = 'rahulshetty/hybrid-tiny-wikitext-poc'
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

For non-causal architectures, replace AutoModelForCausalLM with the appropriate AutoModel class.