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Model: inference-optimization/Llama-3.2-0.5B-Instruct
Source: Original Platform

README.md

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+---
+license: mit
+base_model:
+- meta-llama/Llama-3.2-1B-Instruct
+library_name: transformers
+---
+
+# Llama-3.2-0.5B-Instruct
+
+This is a tiny version of [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct) created for testing and development.
+
+## 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:
+
+| 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.weight`
+- `model.layers.*.self_attn.{q,k,v,o}_proj.weight`
+- `model.layers.*.mlp.{gate,up,down}_proj.weight`
+- `model.layers.*.{input,post_attention}_layernorm.weight`
+- `model.norm.weight`
+
+## Usage
+
+```python
+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
+
+==================================================
+Generating sample text:
+According to all known laws of aviation, there is no way a bee should be able to fly
+==================================================
+```
+
+## Creation Process
+
+This model was created using the llm-compressor `create-tiny-model` claude skill:
+
+1. Inspected the original model configuration to identify key parameters
+2. Created a tiny version by reducing `num_hidden_layers` from 16 to 4
+3. Fine-tuned the model on a toy dataset (famous copypastas) to validate learning capability
+4. Achieved target perplexity of ~1.02 on the validation text
+5. Validated checkpoint structure matches the original model format
+6. 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