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Model: MihaiPopa-1/CinnabarLM-4M-Python-Base
Source: Original Platform

.gitattributes

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README.md

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+---
+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)

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": 32,
+  "hidden_act": "silu",
+  "hidden_size": 192,
+  "initializer_range": 0.02,
+  "intermediate_size": 512,
+  "max_position_embeddings": 2048,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 6,
+  "num_hidden_layers": 6,
+  "num_key_value_heads": 6,
+  "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": "5.0.0",
+  "use_cache": false,
+  "vocab_size": 4096
+}

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": "5.0.0",
+  "use_cache": true
+}

model.safetensors

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tokenizer_config.json

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+{
+  "add_prefix_space": null,
+  "backend": "tokenizers",
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "</s>",
+  "is_local": true,
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "</s>",
+  "tokenizer_class": "TokenizersBackend",
+  "unk_token": "<unk>",
+  "use_default_system_prompt": false
+}

training_args.bin

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