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