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README.md

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+---
+license: llama2
+library_name: transformers
+tags:
+- mlx
+datasets:
+- aqua_rat
+- microsoft/orca-math-word-problems-200k
+- m-a-p/CodeFeedback-Filtered-Instruction
+---
+
+# voxmenthe/Smaug-Llama-3-70B-Instruct-mlx-8bit
+
+The Model [voxmenthe/Smaug-Llama-3-70B-Instruct-mlx-8bit](https://huggingface.co/voxmenthe/Smaug-Llama-3-70B-Instruct-mlx-8bit) was converted to MLX format from [abacusai/Smaug-Llama-3-70B-Instruct](https://huggingface.co/abacusai/Smaug-Llama-3-70B-Instruct) using mlx-lm version **0.13.1**.
+
+## Use with mlx
+
+```bash
+pip install mlx-lm
+```
+
+```python
+from mlx_lm import load, generate
+
+model, tokenizer = load("voxmenthe/Smaug-Llama-3-70B-Instruct-mlx-8bit")
+response = generate(model, tokenizer, prompt="hello", verbose=True)
+```

config.json

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+{
+    "architectures": [
+        "LlamaForCausalLM"
+    ],
+    "attention_bias": false,
+    "attention_dropout": 0.0,
+    "bos_token_id": 128000,
+    "eos_token_id": 128001,
+    "hidden_act": "silu",
+    "hidden_size": 8192,
+    "initializer_range": 0.02,
+    "intermediate_size": 28672,
+    "max_position_embeddings": 8192,
+    "model_type": "llama",
+    "num_attention_heads": 64,
+    "num_hidden_layers": 80,
+    "num_key_value_heads": 8,
+    "pretraining_tp": 1,
+    "quantization": {
+        "group_size": 64,
+        "bits": 8
+    },
+    "rms_norm_eps": 1e-05,
+    "rope_scaling": null,
+    "rope_theta": 500000.0,
+    "tie_word_embeddings": false,
+    "torch_dtype": "bfloat16",
+    "transformers_version": "4.40.0",
+    "use_cache": true,
+    "use_flash_attention_2": true,
+    "vocab_size": 128256
+}

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