---
license: mit
pipeline_tag: text-generation
library_name: transformers
---

# Random-Llama-Small

## Model Overview

**Random-Llama-Small** is a randomly initialized transformer-based language model with approximately 2 billion parameters, built using the LLaMA architecture. It is designed for research purposes, providing a starting point for pretraining or fine-tuning on custom datasets. The model uses the tokenizer from `HuggingFaceTB/SmolLM2-1.7B-Instruct` and is configured for causal language modeling. As a randomly initialized model, it produces incoherent outputs until trained, making it ideal for researchers studying transformer training dynamics or developing custom language models.

---

## Key Details

- **Architecture:** LLaMA (Causal Language Model)  
- **Parameters:** ~2B 
- **Hidden Size:** 2304  
- **Layers:** 22  
- **Attention Heads:** 36 (with 9 key-value heads for grouped-query attention)  
- **Intermediate Size:** 9216  
- **Vocabulary Size:** 128256  
- **Tokenizer:** Imported from `HuggingFaceTB/SmolLM2-1.7B-Instruct`  
- **Precision:** bfloat16  
- **Max Context Length:** 131,072 tokens (with RoPE scaling)  
- **License:** MIT 

---

## LLaMA Architecture

The LLaMA architecture, developed by Meta AI, is a family of efficient transformer-based models optimized for research. Random-Llama-Small follows this design, incorporating several key features:

### Core Components

- **Decoder-Only Transformer:** Predicts the next token in a sequence based on prior tokens, suitable for autoregressive tasks like text generation.
- **Grouped-Query Attention (GQA):** 36 attention heads with only 9 key-value heads, improving efficiency and reducing memory/compute cost.
- **Rotary Position Embeddings (RoPE):** Embeds positional information with scaling, enabling a context length of up to 131,072 tokens.
- **Swiglu Activation:** Uses SiLU (Swish) activation in the FFN for improved expressiveness.
- **RMSNorm:** Root Mean Square Layer Normalization replaces LayerNorm for stability and faster convergence.
- **Tied Embeddings:** Input and output embeddings share weights (`tie_word_embeddings=True`), reducing parameter count by ~295M.

---

## Benefits of LLaMA Architecture

- **Efficiency:** High throughput, low memory use.
- **Scalability:** Works well across model sizes.
- **Flexibility:** Long-context support and task adaptability.
- **Research-Friendly:** Great for exploring attention, positional encoding, and training dynamics.

---

## Random-Llama-Small Specifics

This model uses random weights and:
- Has ~2B parameters across 22 layers.
- Uses a 2304 hidden size and 9216 FFN size.
- Supports 128K+ vocab tokens and bfloat16 precision.
- Supports extended context lengths of 131,072 tokens.

---

## Intended Use

- Research on transformer dynamics, optimization, or architectural changes.
- Baseline for pretraining or task-specific fine-tuning.
- Experimentation with scaling laws or custom architectures.

---

## Out-of-Scope Use

- **Not for direct production deployment.**
- **Not suitable for tasks needing coherence or accuracy without training.**

---

## Usage

### Requirements

- `transformers >= 4.45.0`  
- `torch >= 2.0`  
- GPU with ≥ 6GB VRAM (24GB+ for training)

---

### Inference Example

```python
# Use a pipeline as a high-level helper
from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="reflex-ai/random-llama-small")
print(pipe(messages))
```

> Note: Outputs will be random and incoherent due to the model’s untrained state.

---

### Training Example

```python
from transformers import Trainer, TrainingArguments, DataCollatorForLanguageModeling, LlamaForCausalLM, AutoTokenizer

model = LlamaForCausalLM.from_pretrained("your_username/random-llama-small")
tokenizer = AutoTokenizer.from_pretrained("your_username/random-llama-small")

training_args = TrainingArguments(
    output_dir="./random_llama_small_finetuned",
    per_device_train_batch_size=4,
    num_train_epochs=3,
    fp16=True,
)

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=your_dataset,
    data_collator=DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False),
)

trainer.train()
```

---

## Limitations

- **Random Initialization:** Needs significant training to be useful.
- **Resource Intensive:** High computational cost.
- **No Pretraining Data:** Users must provide their own.
- **Tokenizer Constraint:** May not suit all domains.

---

## Benefits and Potential

- **Customizability:** A blank slate for full control of objectives and data.
- **Research Insights:** Ideal for understanding early-stage LLM behavior.
- **Scalable Baseline:** Balances size and research feasibility.
- **Extended Context:** Useful for long-form tasks post-training.

---

## Model Configuration

```json
{
  "architectures": ["LlamaForCausalLM"],
  "hidden_size": 2304,
  "num_hidden_layers": 22,
  "num_attention_heads": 36,
  "num_key_value_heads": 9,
  "intermediate_size": 9216,
  "vocab_size": 128256,
  "max_position_embeddings": 131072,
  "rope_scaling": {
    "factor": 32.0,
    "high_freq_factor": 4.0,
    "low_freq_factor": 1.0,
    "original_max_position_embeddings": 8192,
    "rope_type": "llama3"
  },
  "torch_dtype": "bfloat16",
  "tie_word_embeddings": true
}
```

---

## Ethical Considerations

- **Untrained Safety:** No immediate harmful outputs, but ethics matter during training.
- **Environmental Impact:** Large-scale training consumes energy; optimize and use green compute.
- **Accessibility:** Resource requirements may limit use by smaller research teams.

---

## Contact

For questions or issues, please open an issue on the Hugging Face repository.

> *Model card created on April 20, 2025.*