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README.md

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+---
+library_name: transformers
+tags:
+- src-ai
+---
+
+# Model Card for PrunaAI/test-save-tiny-random-llama3-smashed-pro
+
+This model was created using the [pruna](https://github.com/PrunaAI/pruna) library. Pruna is a model optimization framework built for developers, enabling you to deliver more efficient models with minimal implementation overhead.
+
+## Usage
+
+First things first, you need to install the pruna library:
+
+```bash
+pip install src
+```
+
+You can [use the transformers library to load the model](https://huggingface.co/PrunaAI/test-save-tiny-random-llama3-smashed-pro?library=transformers) but this might not include all optimizations by default.
+
+To ensure that all optimizations are applied, use the pruna library to load the model using the following code:
+
+```python
+from src import PrunaProModel
+
+loaded_model = PrunaProModel.from_hub(
+    "PrunaAI/test-save-tiny-random-llama3-smashed-pro"
+)
+```
+
+After loading the model, you can use the inference methods of the original model. Take a look at the [documentation](https://pruna.readthedocs.io/en/latest/index.html) for more usage information.
+
+## Smash Configuration
+
+The compression configuration of the model is stored in the `smash_config.json` file, which describes the optimization methods that were applied to the model.
+
+```bash
+{
+    "batcher": null,
+    "cacher": null,
+    "compiler": null,
+    "distiller": null,
+    "distributer": null,
+    "enhancer": null,
+    "factorizer": null,
+    "pruner": null,
+    "quantizer": null,
+    "recoverer": null,
+    "batch_size": 1,
+    "device": "cpu",
+    "device_map": null,
+    "save_fns": [],
+    "load_fns": [
+        "transformers"
+    ],
+    "reapply_after_load": {
+        "factorizer": null,
+        "pruner": null,
+        "quantizer": null,
+        "distiller": null,
+        "cacher": null,
+        "recoverer": null,
+        "distributer": null,
+        "compiler": null,
+        "batcher": null,
+        "enhancer": null
+    }
+}
+```
+
+## 🌍 Join the Pruna AI community!
+
+[![Twitter](https://img.shields.io/twitter/follow/PrunaAI?style=social)](https://twitter.com/PrunaAI)
+[![GitHub](https://img.shields.io/github/followers/PrunaAI?label=Follow%20%40PrunaAI&style=social)](https://github.com/PrunaAI)
+[![LinkedIn](https://img.shields.io/badge/LinkedIn-Connect-blue)](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
+[![Discord](https://img.shields.io/badge/Discord-Join%20Us-blue?style=social&logo=discord)](https://discord.com/invite/rskEr4BZJx)
+[![Reddit](https://img.shields.io/reddit/subreddit-subscribers/PrunaAI?style=social)](https://www.reddit.com/r/PrunaAI/)

config.json

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+{
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "head_dim": 4,
+  "hidden_act": "silu",
+  "hidden_size": 16,
+  "initializer_range": 0.02,
+  "intermediate_size": 64,
+  "max_position_embeddings": 2048,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 4,
+  "num_hidden_layers": 2,
+  "num_key_value_heads": 1,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 10000.0,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.53.0",
+  "use_cache": true,
+  "vocab_size": 128256
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "transformers_version": "4.53.0"
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:714bbd246d0de5fcbe1f18a3c2469a9f497bd6276b2979ac5db7766cd22550f9
+size 16449008

smash_config.json

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+{
+    "batcher": null,
+    "cacher": null,
+    "compiler": null,
+    "distiller": null,
+    "distributer": null,
+    "enhancer": null,
+    "factorizer": null,
+    "pruner": null,
+    "quantizer": null,
+    "recoverer": null,
+    "batch_size": 1,
+    "device": "cpu",
+    "device_map": null,
+    "save_fns": [],
+    "load_fns": [
+        "transformers"
+    ],
+    "reapply_after_load": {
+        "factorizer": null,
+        "pruner": null,
+        "quantizer": null,
+        "distiller": null,
+        "cacher": null,
+        "recoverer": null,
+        "distributer": null,
+        "compiler": null,
+        "batcher": null,
+        "enhancer": null
+    }
+}