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Model: GODELEV/Test-1-4000
Source: Original Platform

.gitattributes

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README.md

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+---
+license: mit
+datasets:
+- roneneldan/TinyStories
+language:
+- en
+pipeline_tag: text-generation
+---
+# Test-1-4000 — A 190M Parameter Narrative Engine
+
+---
+
+## Overview
+
+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.
+
+The project focuses on studying how narrative coherence, logical consistency, and language fluency emerge inside small-scale language models through structured training.
+
+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.
+
+---
+
+## Model Highlights
+
+| Feature | Specification |
+|---|---|
+| Architecture | Llama-based Decoder-only Transformer |
+| Parameters | 190.55 Million |
+| Context Window | 2048 Tokens |
+| Final Training Step | 4000 |
+| Final Training Loss | 0.573 |
+| Precision | bfloat16 |
+| Attention Backend | Flash Attention 2 |
+| Compilation | torch.compile |
+| Tokenizer | GPT-2 Tokenizer |
+
+---
+
+## Architecture
+
+| Component | Value |
+|---|---|
+| Hidden Dimension | 768 |
+| Layers | 12 |
+| Attention Heads | 12 |
+| Intermediate Size | 3072 |
+| Activation Function | SwiGLU |
+| Normalization | RMSNorm |
+| Vocabulary Size | 50,257 |
+
+The model uses Rotary Positional Embeddings (RoPE) for stable long-range token relationships across the 2048-token context window.
+
+---
+
+## Training Progression
+
+### Phase 1 — Lexical Learning (0 → 250)
+
+The model learned grammar, sentence formation, and common linguistic patterns.
+
+### Phase 2 — Relational Understanding (250 → 1000)
+
+The model began associating entities, actions, and environments into logically connected sequences.
+
+### Phase 3 — Narrative Coherence (1000 → 2000)
+
+Narrative continuity emerged. Stories developed stable structure, conflict resolution, and reduced contradiction.
+
+### Phase 4 — Emergent Narrative Intelligence (2000 → 3000)
+
+The model improved in emotional consistency, long-range memory, and thematic continuity across generations.
+
+### Phase 5 — Fluent Generative Stability (3000 → 4000)
+
+This final phase marked a transition from structured storytelling into fluent narrative generation.
+
+The model became substantially better at:
+
+- maintaining tone,
+- producing natural sentence flow,
+- avoiding repetitive degeneration,
+- preserving character consistency,
+- and generating smoother transitions between events.
+
+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.
+
+The reduction in loss to **0.573** indicates a major improvement in predictive confidence and language fluency.
+
+---
+
+## Training Configuration
+
+| Parameter | Value |
+|---|---|
+| Optimizer | AdamW |
+| Learning Rate | 5e-4 |
+| Scheduler | OneCycleLR |
+| Weight Decay | 0.01 |
+| Precision | bfloat16 |
+| Effective Batch Size | ~262K tokens/step |
+
+---
+
+## Dataset
+
+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.
+
+This allows the model to focus on:
+
+- causal reasoning,
+- narrative flow,
+- emotional continuity,
+- and long-range coherence.
+
+---
+
+## Usage
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_path = "GODELEV/Test-1-4000"
+
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_path,
+    torch_dtype=torch.bfloat16,
+    device_map="auto"
+)
+
+prompt = "Once upon a time, a boy found a silver key."
+
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+
+output = model.generate(
+    **inputs,
+    max_new_tokens=200,
+    temperature=0.7,
+    top_p=0.9,
+    repetition_penalty=1.1,
+    do_sample=True,
+    eos_token_id=tokenizer.eos_token_id,
+    pad_token_id=tokenizer.pad_token_id
+)
+
+print(tokenizer.decode(output[0], skip_special_tokens=True))
+```
+
+---
+
+## Final Notes
+
+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.
+
+Despite its relatively small size, the model exhibits:
+
+- strong narrative fluency,
+- stable story progression,
+- coherent emotional structure,
+- and reliable long-context generation.
+
+The project serves as an exploration into how efficient language models can develop increasingly sophisticated generative behavior through progressive training refinement.
+
+---
+
+## Citation
+
+```bibtex
+@misc{test14000,
+  title={Test-1-4000: A 190M Parameter Narrative Engine},
+  author={GODELEV},
+  year={2026}
+}
+```

config.json

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+{
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "dtype": "float32",
+  "eos_token_id": 2,
+  "head_dim": 64,
+  "hidden_act": "silu",
+  "hidden_size": 896,
+  "initializer_range": 0.02,
+  "intermediate_size": 2432,
+  "max_position_embeddings": 2048,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 14,
+  "num_hidden_layers": 12,
+  "num_key_value_heads": 2,
+  "pad_token_id": null,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_parameters": {
+    "rope_theta": 10000.0,
+    "rope_type": "default"
+  },
+  "tie_word_embeddings": false,
+  "transformers_version": "5.6.2",
+  "use_cache": false,
+  "vocab_size": 50257
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "output_attentions": false,
+  "output_hidden_states": false,
+  "transformers_version": "5.6.2",
+  "use_cache": false
+}

model.safetensors

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tokenizer_config.json

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+{
+  "add_prefix_space": false,
+  "backend": "tokenizers",
+  "bos_token": "<|endoftext|>",
+  "eos_token": "<|endoftext|>",
+  "errors": "replace",
+  "is_local": false,
+  "local_files_only": false,
+  "model_max_length": 1024,
+  "pad_token": "<|endoftext|>",
+  "tokenizer_class": "GPT2Tokenizer",
+  "unk_token": "<|endoftext|>"
+}