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Model: GODELEV/Test-1-4000 Source: Original Platform
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README.md
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README.md
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---
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license: mit
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datasets:
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- roneneldan/TinyStories
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language:
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- en
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pipeline_tag: text-generation
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---
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# Test-1-4000 — A 190M Parameter Narrative Engine
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---
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## Overview
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Test-1-4000 is the final training checkpoint of a compact decoder-only Transformer model built on the Llama architecture and trained on the TinyStories dataset.
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The project focuses on studying how narrative coherence, logical consistency, and language fluency emerge inside small-scale language models through structured training.
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By Step 4000, the model reaches a significantly higher level of generative stability and narrative fluency compared to earlier checkpoints, achieving a final training loss of **0.573** after nearly two full epochs of training.
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---
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## Model Highlights
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| Feature | Specification |
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|---|---|
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| Architecture | Llama-based Decoder-only Transformer |
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| Parameters | 190.55 Million |
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| Context Window | 2048 Tokens |
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| Final Training Step | 4000 |
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| Final Training Loss | 0.573 |
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| Precision | bfloat16 |
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| Attention Backend | Flash Attention 2 |
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| Compilation | torch.compile |
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| Tokenizer | GPT-2 Tokenizer |
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---
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## Architecture
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| Component | Value |
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|---|---|
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| Hidden Dimension | 768 |
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| Layers | 12 |
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| Attention Heads | 12 |
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| Intermediate Size | 3072 |
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| Activation Function | SwiGLU |
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| Normalization | RMSNorm |
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| Vocabulary Size | 50,257 |
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The model uses Rotary Positional Embeddings (RoPE) for stable long-range token relationships across the 2048-token context window.
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---
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## Training Progression
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### Phase 1 — Lexical Learning (0 → 250)
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The model learned grammar, sentence formation, and common linguistic patterns.
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### Phase 2 — Relational Understanding (250 → 1000)
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The model began associating entities, actions, and environments into logically connected sequences.
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### Phase 3 — Narrative Coherence (1000 → 2000)
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Narrative continuity emerged. Stories developed stable structure, conflict resolution, and reduced contradiction.
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### Phase 4 — Emergent Narrative Intelligence (2000 → 3000)
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The model improved in emotional consistency, long-range memory, and thematic continuity across generations.
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### Phase 5 — Fluent Generative Stability (3000 → 4000)
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This final phase marked a transition from structured storytelling into fluent narrative generation.
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The model became substantially better at:
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- maintaining tone,
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- producing natural sentence flow,
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- avoiding repetitive degeneration,
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- preserving character consistency,
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- and generating smoother transitions between events.
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By this stage, generations began feeling less mechanically predicted and more organically written. Dialogue improved noticeably, pacing became more natural, and narrative structure stabilized across longer outputs.
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The reduction in loss to **0.573** indicates a major improvement in predictive confidence and language fluency.
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---
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## Training Configuration
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| Parameter | Value |
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|---|---|
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| Optimizer | AdamW |
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| Learning Rate | 5e-4 |
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| Scheduler | OneCycleLR |
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| Weight Decay | 0.01 |
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| Precision | bfloat16 |
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| Effective Batch Size | ~262K tokens/step |
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---
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## Dataset
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The model was trained on TinyStories, a synthetic storytelling dataset designed to teach language models reasoning and narrative structure using simplified vocabulary and clean writing patterns.
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This allows the model to focus on:
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- causal reasoning,
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- narrative flow,
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- emotional continuity,
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- and long-range coherence.
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---
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## Usage
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```python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_path = "GODELEV/Test-1-4000"
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tokenizer = AutoTokenizer.from_pretrained(model_path)
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model = AutoModelForCausalLM.from_pretrained(
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model_path,
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torch_dtype=torch.bfloat16,
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device_map="auto"
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)
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prompt = "Once upon a time, a boy found a silver key."
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inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
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output = model.generate(
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**inputs,
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max_new_tokens=200,
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temperature=0.7,
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top_p=0.9,
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repetition_penalty=1.1,
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do_sample=True,
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eos_token_id=tokenizer.eos_token_id,
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pad_token_id=tokenizer.pad_token_id
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)
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print(tokenizer.decode(output[0], skip_special_tokens=True))
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```
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---
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## Final Notes
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Test-1-4000 demonstrates that coherent and fluent narrative behavior can emerge in compact Transformer models when training is focused on clean, structured data and long-form consistency.
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Despite its relatively small size, the model exhibits:
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- strong narrative fluency,
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- stable story progression,
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- coherent emotional structure,
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- and reliable long-context generation.
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The project serves as an exploration into how efficient language models can develop increasingly sophisticated generative behavior through progressive training refinement.
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---
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## Citation
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```bibtex
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@misc{test14000,
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title={Test-1-4000: A 190M Parameter Narrative Engine},
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author={GODELEV},
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year={2026}
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}
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```
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config.json
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config.json
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{
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"architectures": [
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"LlamaForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 1,
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"dtype": "float32",
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"eos_token_id": 2,
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"head_dim": 64,
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"hidden_act": "silu",
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"hidden_size": 896,
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"initializer_range": 0.02,
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"intermediate_size": 2432,
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"max_position_embeddings": 2048,
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"mlp_bias": false,
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"model_type": "llama",
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"num_attention_heads": 14,
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"num_hidden_layers": 12,
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"num_key_value_heads": 2,
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"pad_token_id": null,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-05,
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"rope_parameters": {
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"rope_theta": 10000.0,
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"rope_type": "default"
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},
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"tie_word_embeddings": false,
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"transformers_version": "5.6.2",
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"use_cache": false,
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"vocab_size": 50257
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}
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generation_config.json
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{
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"_from_model_config": true,
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"bos_token_id": 1,
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"eos_token_id": 2,
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"output_attentions": false,
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"output_hidden_states": false,
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"transformers_version": "5.6.2",
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"use_cache": false
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:9b393f254c37483d4ce7607e78206924aae731276c649a0bf08ad09c50c29915
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size 762210848
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250306
tokenizer.json
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tokenizer.json
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tokenizer_config.json
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{
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"add_prefix_space": false,
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"backend": "tokenizers",
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"bos_token": "<|endoftext|>",
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"eos_token": "<|endoftext|>",
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"errors": "replace",
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"is_local": false,
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"local_files_only": false,
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"model_max_length": 1024,
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"pad_token": "<|endoftext|>",
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"tokenizer_class": "GPT2Tokenizer",
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"unk_token": "<|endoftext|>"
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}
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