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DistilWord2-23k

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 ---
+license: mit
+tags:
+- small-language-model
+- tiny-language-model
+- word-generation
+- distillation
+- synthetic-data
+datasets:
+- Harley-ml/es-en-words
+pipeline_tag: text-generation
 ---
+# DistilWord2-23k
+**DistilWord2-23k** is the second-generation ultra-compact language model (~23k parameters) in the DistilWord series.
+Trained on **12,000 clean, low-entropy synthetic words** generated by [Harley-ml/LargeWord-1.5M](https://huggingface.co/Harley-ml/LargeWord-1.5M), it achieves state-of-the-art sample efficiency and morphology for its size class.
+## Benchmark Progression
+| Model | Parameters | Training Distribution | Val Loss | Val PPL | Signal PSNR | Behavior |
+| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
+| **MicroWord-23k (Original)** | ~23k | 750k Natural Words | 6.4105 | 608.18 | 2.57 dB | Attention collapsed (`wwww...`, `zzzz...`) |
+| **DistilWord-23k (v1)** | ~23k | 3.8k Synthetic (`TinyWord2`) | 3.0633 | 21.40 | 4.37 dB | Stutter eliminated; basic morphology |
+| **DistilWord2-23k (Ours)** | **~23k** | **12k Synthetic (`LargeWord-1.5M`)** | **2.5415** | **12.70** | **4.50 dB** | **Real morphemes (`afields`, `appers`, `zrings`)** |
+---
+## Qualitative Generations
+| Prompt | Original MicroWord-23k | DistilWord2-23k (Ours) | Learned Morphology |
+| :--- | :--- | :--- | :--- |
+| `a` | `a` (Stalled) | **`afields`** | Complete dictionary compound word (*a* + *field* + *-s*) |
+| `app` | `appco` | **`appers`** | Agent noun pluralization (*app* + *-er* + *-s*) |
+| `z` | `zzzzx's` (Stutter loop) | **`zrings`** | Complex inflectional cluster (*-ing* + *-s*) |
+| `dis` | `dis` | **`disers`** | Morpheme suffix attachment |
+| `el` | `elel` (Repeat loop) | **`elys`** | Adverbial suffix (*-ly* + *-s*) |
+| `sub` | `sub` | **`subs`** | Standard plural inflection |
+---
+## Quick Inference
+```python
+import torch
+from transformers import AutoModelForCausalLM, PreTrainedTokenizerFast
+repo_id = "Useruser2statsaltalt/DistilWord2-23k"
+tokenizer = PreTrainedTokenizerFast.from_pretrained(repo_id)
+model = AutoModelForCausalLM.from_pretrained(repo_id)
+model.eval()
+prompt = "app"
+bos = tokenizer.bos_token or ""
+inputs = tokenizer(bos + prompt, return_tensors="pt", add_special_tokens=False)
+with torch.inference_mode():
+    outputs = model.generate(
+        **inputs,
+        max_new_tokens=12,
+        do_sample=True,
+        temperature=0.80,
+        top_p=0.90,
+        top_k=35,
+        eos_token_id=tokenizer.eos_token_id,
+        pad_token_id=tokenizer.pad_token_id,
+    )
+prompt_len = inputs["input_ids"].shape[-1]
+completion = tokenizer.decode(outputs[0][prompt_len:], skip_special_tokens=True)
+print(f"Generated: {prompt + completion}")
+## Architecture Specifications
+Architecture: Qwen Causal LM (Micro-scale)
+Hidden Layers: 1
+Hidden Size: 16
+Attention Heads: 1
+Intermediate (SwiGLU): 56
+Tied Embeddings: True
+Unique Parameters: ~23,024