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README.md

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+---
+language:
+- en
+library_name: transformers
+license: apache-2.0
+tags:
+- pruna-ai
+- safetensors
+---
+
+# Model Card for AINovice2005/SmolLM2-360M-smashed
+
+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 pruna
+```
+
+You can [use the transformers library to load the model](https://huggingface.co/AINovice2005/SmolLM2-360M-smashed?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 pruna import PrunaModel
+
+loaded_model = PrunaModel.from_pretrained(
+    "AINovice2005/SmolLM2-360M-smashed"
+)
+# we can then run inference using the methods supported by the base model
+```
+
+
+For inference, you can use the inference methods of the original model like shown in [the original model card](https://huggingface.co/HuggingFaceTB/SmolLM2-360M?library=transformers).
+ Alternatively, you can visit [the Pruna documentation](https://docs.pruna.ai/en/stable/) for more 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": "torch_compile",
+    "factorizer": null,
+    "kernel": null,
+    "pruner": null,
+    "quantizer": "hqq",
+    "hqq_backend": "torchao_int4",
+    "hqq_compute_dtype": "torch.bfloat16",
+    "hqq_force_hf_implementation": false,
+    "hqq_group_size": 64,
+    "hqq_use_torchao_kernels": true,
+    "hqq_weight_bits": 4,
+    "torch_compile_backend": "inductor",
+    "torch_compile_dynamic": false,
+    "torch_compile_fullgraph": true,
+    "torch_compile_make_portable": false,
+    "torch_compile_max_kv_cache_size": 400,
+    "torch_compile_mode": "max-autotune",
+    "torch_compile_seqlen_manual_cuda_graph": 200,
+    "torch_compile_target": "model",
+    "batch_size": 1,
+    "device": "cuda:0",
+    "device_map": null,
+    "save_fns": [
+        "hqq",
+        "save_before_apply"
+    ],
+    "load_fns": [
+        "hqq"
+    ],
+    "reapply_after_load": {
+        "factorizer": null,
+        "pruner": null,
+        "quantizer": null,
+        "kernel": null,
+        "cacher": null,
+        "compiler": "torch_compile",
+        "batcher": 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.gg/JFQmtFKCjd)
+[![Reddit](https://img.shields.io/reddit/subreddit-subscribers/PrunaAI?style=social)](https://www.reddit.com/r/PrunaAI/)

config.json

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+{
+  "_attn_implementation_autoset": true,
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 0,
+  "eos_token_id": 0,
+  "head_dim": 64,
+  "hidden_act": "silu",
+  "hidden_size": 960,
+  "initializer_range": 0.02,
+  "intermediate_size": 2560,
+  "is_llama_config": true,
+  "max_position_embeddings": 8192,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 15,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 5,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_interleaved": false,
+  "rope_scaling": null,
+  "rope_theta": 100000,
+  "tie_word_embeddings": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.51.0",
+  "use_cache": true,
+  "vocab_size": 49152
+}

qmodel.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:afc3d52dea8d9537dac693075b31e9f3c0e75ff1d0342823d42c577b6a9cc8ba
+size 271683987

smash_config.json

@@ -0,0 +1,42 @@
+{
+    "batcher": null,
+    "cacher": null,
+    "compiler": "torch_compile",
+    "factorizer": null,
+    "kernel": null,
+    "pruner": null,
+    "quantizer": "hqq",
+    "hqq_backend": "torchao_int4",
+    "hqq_compute_dtype": "torch.bfloat16",
+    "hqq_force_hf_implementation": false,
+    "hqq_group_size": 64,
+    "hqq_use_torchao_kernels": true,
+    "hqq_weight_bits": 4,
+    "torch_compile_backend": "inductor",
+    "torch_compile_dynamic": false,
+    "torch_compile_fullgraph": true,
+    "torch_compile_make_portable": false,
+    "torch_compile_max_kv_cache_size": 400,
+    "torch_compile_mode": "max-autotune",
+    "torch_compile_seqlen_manual_cuda_graph": 200,
+    "torch_compile_target": "model",
+    "batch_size": 1,
+    "device": "cuda:0",
+    "device_map": null,
+    "save_fns": [
+        "hqq",
+        "save_before_apply"
+    ],
+    "load_fns": [
+        "hqq"
+    ],
+    "reapply_after_load": {
+        "factorizer": null,
+        "pruner": null,
+        "quantizer": null,
+        "kernel": null,
+        "cacher": null,
+        "compiler": "torch_compile",
+        "batcher": null
+    }
+}