BwETAFv3-97M

🧾 BwETAFv3-97M: Model Card

Boring’s Experimental Transformer for Autoregression (Flax) A 97M parameter autoregressive language model built using a custom Flax pipeline, loads of tuning, and a sprinkle of existential dread.

Trained on determination, fueled by suffering, powered by free TPUs.

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📌 Model Summary

• Model Name: BwETAFv3-97M
• Parameters: 97,609,728
• Training Tokens: 3,390,046,208
• Training Time: 4.82 TPUv3-8 Hours
• Framework: Flax / JAX
• Max Context Length: 2048 tokens
• Tokenizer: Custom-trained BPE tokenizer (vocab_size = 16,384)
• Dataset Used: Fineweb (For training and validation) and TinyStories (For training only)

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🧪 Hyperparameters

{
  "vocab_size": 16384,
  "max_len": 2048,
  "dtype": "bfloat16",
  "num_heads": 8,
  "attention_dim": 768,
  "attn_chunks": 1,
  "gqa_repeats": 2,
  "use_flash_attention": false,
  "num_blocks": 12,
  "ff_dim": 2304,
  "dropout_rate": 0.05,
  "emb_init_range": 0.02,
}

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🛠 Optimizer Settings

{
  "peaklr":1.5e-3,
  "warmup_percent":0.02,
  "min_value":1.8e-4,
  "training_decay":"cosine",
  "weight_decay": 0.1,
  "min_warmup_value":6e-4,
  "b1": 0.95,
  "b2": 0.98,
  "eps": 1.5e-8,
  "opt_dtype": "bfloat16"
}

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📈 Performance

• Final Validation Loss: 3.02

• Training-Validation loss Graphs:

• For detailed stats, refer to stats.json in the model files.

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⚡ Quickstart

!pip install BwETAF==0.6
# If you are having any troubles with compatablility issues with the model like jax, hf and BwETAF run
!pip install --upgrade jax jaxlib flax tiktoken jax_cuda12_plugin datasets flash-attention-jax

from BwETAF.api.predictv2 import KV_caching
import BwETAF
import jax
from BwETAF.tokenizer.main import load

model = BwETAF.load_hf("WICKED4950/BwETAFv3-97M",jax.numpy.bfloat16)
tokenizer = load("Loaded_model")

thing = KV_caching(model, top_p=0.92,temperature=0.8)
prompt = """The night sky was alive with lightning, each flash revealing the jagged cliffs ahead. I gripped the letter tighter, knowing it held the answer to everything. The wind screamed as I took a step closer to the edge"""
input = tokenizer.encode(prompt)
print("The input is",input)
the_thing = thing(jax.numpy.array(input),max_len=128)
print(prompt,end="")
for i in the_thing:
    print(tokenizer.decode(i), end="")
print()


# To get the params or jax struct
params = model.trainable_variables
structure = model.model_struct

☁️ Colab support and examples coming soon!

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🧭 Intended Use

This model is primarily released for research and experimentation,
but it can also be fine-tuned and adapted for downstream tasks.
Users are encouraged to evaluate it carefully for their specific use cases.

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📚 Paper

This model is described in detail in the accompanying paper:
📄 BwETAFv3: Efficient Autoregressive Language Modeling with 97M Parameters

The paper covers:

• Architecture overview
• Training methodology
• Regularization & stability techniques
• Scaling considerations
• Evaluation results
• Contact info

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📬 Contact Me

• 📸 Instagram: boring._.wicked
• 💬 Discord: fused_computation.1 (if you spot me lurking in any AI-related servers)

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