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Model: kaitchup/Llama-3.3-70B-Instruct-AutoRound-GPTQ-4bit
Source: Original Platform

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README.md

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+---
+language:
+- en
+library_name: transformers
+tags:
+- auto-gptq
+- AutoRound
+license: llama3.3
+---
+
+
+## Model Details
+
+This is [meta-llama/Llama-3.3-70B-Instruct](https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct) quantized with [AutoRound](https://github.com/intel/auto-round/tree/main) (symmetric quantization) and serialized with the GPTQ format in 4-bit. The model has been created, tested, and evaluated by The Kaitchup.
+
+Details on the quantization process and how to use the model here:
+[How to Quantize and Run Llama 3.3 70B Instruct on Your GPU](https://kaitchup.substack.com/p/how-to-quantize-and-run-llama-33)
+
+![Llama 3.3 70B Instruct_ Zero-Shot MMLU (Accuracy).png](https://cdn-uploads.huggingface.co/production/uploads/64b93e6bd6c468ac7536607e/0UOK_IsUinziw4GUMqq3_.png)
+![Llama 3.3 70B Instruct_ Model Size.png](https://cdn-uploads.huggingface.co/production/uploads/64b93e6bd6c468ac7536607e/hB8AdtD1DfVC90kOkYowz.png)
+
+- **Developed by:** [The Kaitchup](https://kaitchup.substack.com/)
+- **Language(s) (NLP):** English
+- **License:** Llama 3.3

config.json

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+  "_name_or_path": "./Llama-3.3-70B-Instruct-AutoRound-GPTQ-4bit",
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
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+  "initializer_range": 0.02,
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+  "max_position_embeddings": 131072,
+  "mlp_bias": false,
+  "model_type": "llama",
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+  "num_hidden_layers": 80,
+  "num_key_value_heads": 8,
+  "pretraining_tp": 1,
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+    "bits": 4,
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+    "use_exllama": true
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+    "high_freq_factor": 4.0,
+    "low_freq_factor": 1.0,
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+    "rope_type": "llama3"
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+  "torch_dtype": "float16",
+  "transformers_version": "4.47.0",
+  "use_cache": true,
+  "vocab_size": 128256
+}

generation_config.json

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+  "do_sample": true,
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+  "top_p": 0.9,
+  "transformers_version": "4.47.0"
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