Model Details
- Base Model: meta-llama/Llama-3.2-1B-Instruct
- Architecture: llama
- Total Parameters: 0.51B
- Activated Parameters: 0.51B (non-MoE)
Configuration Changes
The following parameters were reduced from the original model:
Table with columns: Parameter, Original, Tiny| Parameter | Original | Tiny |
|---|
| num_hidden_layers | 16 | 4 |
| hidden_size | 2048 | 2048 |
| intermediate_size | 8192 | 8192 |
| num_attention_heads | 32 | 32 |
| num_key_value_heads | 8 | 8 |
Checkpoint Structure
This model uses a single model.safetensors file containing all weights. The checkpoint structure is identical to the original model, with the standard Llama architecture tensors:
model.embed_tokens.weightmodel.layers.*.self_attn.{q,k,v,o}_proj.weightmodel.layers.*.mlp.{gate,up,down}_proj.weightmodel.layers.*.{input,post_attention}_layernorm.weightmodel.norm.weight
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("inference-optimization/Llama-3.2-0.5B-Instruct", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("inference-optimization/Llama-3.2-0.5B-Instruct")
input_ids = tokenizer("According to all known laws", return_tensors="pt").input_ids.to(model.device)
output = model.generate(input_ids, max_new_tokens=20)
print(tokenizer.decode(output[0]))
Validation
Success: 1.0247299671173096 <= 10.0
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Generating sample text:
According to all known laws of aviation, there is no way a bee should be able to fly
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Creation Process
This model was created using the llm-compressor create-tiny-model claude skill:
- Inspected the original model configuration to identify key parameters
- Created a tiny version by reducing
num_hidden_layers from 16 to 4
- Fine-tuned the model on a toy dataset (famous copypastas) to validate learning capability
- Achieved target perplexity of ~1.02 on the validation text
- Validated checkpoint structure matches the original model format
- Confirmed successful loading and inference
Notes
- This model was fine-tuned on a small corpus of internet copypastas to ensure it can learn effectively
- The model maintains the same Llama 3.2 architecture (including RoPE parameters) as the base model, just with fewer layers
- Due to the reduced layer count, this model has approximately 25% of the original model's parameters
- This is intended for development and testing purposes, not production use