Create README.md
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README.md
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---
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license: apache-2.0
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datasets:
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- HuggingFaceFW/fineweb
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language:
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- en
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pipeline_tag: text-generation
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tags:
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- tiny-model
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- cinnabarlm
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- tiny-llm
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- tiny-lm
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- tinylm
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- tinyllm
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---
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# CinnabarLM 1.4M
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What happens if you take the CinnabarLM idea and push it a little more further? You'll get this!
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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!
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# Why?
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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!
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# Model Configurations
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| Parameter | Value |
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|---|---|
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| Tokenizer | Llama 3's tokenizer (Tiktoken / BPE) |
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| Vocabulary Size | 4096 tokens |
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| Batch Size | 4 x 8 = 32 |
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| Context Window | Maybe 2048 tokens |
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| `hidden_size` | 128 |
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| `intermediate_size` | 128 |
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| `num_hidden_layers` | 4 |
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| `num_attention_heads` | 4 |
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| `max_position_embeddings` | 2048 |
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| `rms_norm_eps` | `1e-5` |
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| `initializer_range` | 0.02 |
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| `use_cache` | True
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| `tie_word_embeddings` | False
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| `rope_theta` | 10000.0
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# Training Configurations
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| Hyperparameter | Value |
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|---|---|
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| `output_dir` | "./cinnabarlm-v2" |
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| `max_steps` | 10000 |
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| `per_device_train_batch_size` | 8 |
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| `gradient_accumulation_steps` | 4 |
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| `learning_rate` | 6e-4 |
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| `weight_decay` | 0.01 |
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| `warmup_steps` | 500 |
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| `lr_scheduler_type` | "cosine" |
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| `logging_steps` | 100 |
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| `save_steps` | 2000 |
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| `fp16` | True |
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| `save_total_limit` | 2 |
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| `prediction_loss_only` | True |
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| `logging_first_step` | True |
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# Limitations
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* **Not Instruction-Tuned:** It's only a base model, so it only completes text.
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* **English-Only:** It's trained on English data (FineWeb), it's NOT multilingual.
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# Some other details
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* It's trained on 50 million tokens of [FineWeb](https://huggingface.co/datasets/HuggingFaceFW/fineweb) (CC-MAIN-2025-26 snapshot), and the knowledge cutoff is June 2025.
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* The name "CinnabarLM" that I picked was made by combining "Cinnabar" (the new block from the Chaos Cubed drop in Minecraft) + "LM" (Language Model)
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