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.

It's trained on 30 million tokens of 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)