70 lines
2.2 KiB
Markdown
70 lines
2.2 KiB
Markdown
---
|
|
license: apache-2.0
|
|
datasets:
|
|
- OLMo-Coding/starcoder-python-instruct
|
|
language:
|
|
- en
|
|
pipeline_tag: text-generation
|
|
tags:
|
|
- tiny-model
|
|
- cinnabarlm
|
|
- python
|
|
- code
|
|
- tiny-llm
|
|
- tiny-lm
|
|
- tinylm
|
|
- tinyllm
|
|
---
|
|
|
|
# CinnabarLM Python
|
|
CinnabarLM Python is a tiny, 4M-parameter code LLM trained for ~38 minutes on a T4 GPU (on Colab)! It's only 16 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!
|
|
|
|
# Differences from Preview
|
|
* Now it's Llama-based, Preview was a custom model
|
|
* And of course, it's stable now (it doesn't generate gibberish / mess of words anymore)!
|
|
|
|
# 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` | 192 |
|
|
| `intermediate_size` | 192 |
|
|
| `num_hidden_layers` | 6 |
|
|
| `num_attention_heads` | 6 |
|
|
| `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.
|
|
* **Python-Only:** It's trained on Python code (The Stack).
|
|
# Some other details
|
|
* It's trained on ~70 million tokens of [The Stack](https://huggingface.co/datasets/OLMo-Coding/starcoder-python-instruct)
|
|
* The name "CinnabarLM" that I picked was made by combining "Cinnabar" (the new block from the Chaos Cubed drop in Minecraft) + "LM" (Language Model) |