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Model: MihaiPopa-1/CinnabarLM-1.4M-Base
Source: Original Platform

README.md

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+---
+license: apache-2.0
+datasets:
+- HuggingFaceFW/fineweb
+language:
+- en
+pipeline_tag: text-generation
+tags:
+- tiny-model
+- cinnabarlm
+- tiny-llm
+- tiny-lm
+- tinylm
+- tinyllm
+---
+
+# CinnabarLM 1.4M
+What happens if you take the CinnabarLM idea and push it a little more further? You'll get this!
+
+CinnabarLM 1.4M is a tiny, 1.4M-parameter LLM trained for ~26.75 minutes on a T4 GPU (on Colab)! It's only 6 MB in size and now it's Llama-based!
+
+# Why?
+Because it's a good idea to make tiny LLMs. Some people already did with [MicroLM](https://huggingface.co/CromIA/MicroLM-1M), [Spark 4 5M](https://huggingface.co/LH-Tech-AI/Spark-5M-Base-v4) and [Tenete 8M](https://huggingface.co/Harley-ml/Tenete-8M), but not myself!
+
+# Model Configurations
+| Parameter | Value |
+|---|---|
+| Tokenizer | Llama 3's tokenizer (Tiktoken / BPE) |
+| Vocabulary Size | 4096 tokens |
+| Batch Size | 4 x 8 = 32 |
+| Context Window | Maybe 2048 tokens |
+| `hidden_size` | 128 |
+| `intermediate_size` | 128 |
+| `num_hidden_layers` | 4 |
+| `num_attention_heads` | 4 |
+| `max_position_embeddings` | 2048 |
+| `rms_norm_eps` | `1e-5` |
+| `initializer_range` | 0.02 |
+| `use_cache` | True
+| `tie_word_embeddings` | False
+| `rope_theta` | 10000.0
+
+# Training Configurations
+| Hyperparameter | Value |
+|---|---|
+| `output_dir` | "./cinnabarlm-v2" |
+| `max_steps` | 10000 |              
+| `per_device_train_batch_size` | 8 |
+| `gradient_accumulation_steps` | 4 |
+| `learning_rate` | 6e-4 |           
+| `weight_decay` | 0.01 |
+| `warmup_steps` | 500 |
+| `lr_scheduler_type` | "cosine" |
+| `logging_steps` | 100 |
+| `save_steps` | 2000 |
+| `fp16` | True |                    
+| `save_total_limit` | 2 |
+| `prediction_loss_only` | True |
+| `logging_first_step` | True |
+
+# Limitations
+* **Not Instruction-Tuned:** It's only a base model, so it only completes text.
+* **English-Only:** It's trained on English data (FineWeb), it's NOT multilingual.
+# Some other details
+* It's trained on 30 million tokens of [FineWeb](https://huggingface.co/datasets/HuggingFaceFW/fineweb) (CC-MAIN-2025-26 snapshot), and the knowledge cutoff is June 2025.
+* The name "CinnabarLM" that I picked was made by combining "Cinnabar" (the new block from the Chaos Cubed drop in Minecraft) + "LM" (Language Model)