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---
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license: llama2
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library_name: transformers
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tags:
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- mlx
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datasets:
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- aqua_rat
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- microsoft/orca-math-word-problems-200k
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- m-a-p/CodeFeedback-Filtered-Instruction
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---
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# voxmenthe/Smaug-Llama-3-70B-Instruct-mlx-8bit
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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**.
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## Use with mlx
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```bash
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pip install mlx-lm
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```
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```python
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from mlx_lm import load, generate
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model, tokenizer = load("voxmenthe/Smaug-Llama-3-70B-Instruct-mlx-8bit")
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response = generate(model, tokenizer, prompt="hello", verbose=True)
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```
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{
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"architectures": [
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"LlamaForCausalLM"
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],
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"attention_bias": false,
|
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"attention_dropout": 0.0,
|
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+
"bos_token_id": 128000,
|
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+
"eos_token_id": 128001,
|
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+
"hidden_act": "silu",
|
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+
"hidden_size": 8192,
|
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+
"initializer_range": 0.02,
|
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+
"intermediate_size": 28672,
|
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+
"max_position_embeddings": 8192,
|
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"model_type": "llama",
|
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+
"num_attention_heads": 64,
|
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+
"num_hidden_layers": 80,
|
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+
"num_key_value_heads": 8,
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"pretraining_tp": 1,
|
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"quantization": {
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"group_size": 64,
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"bits": 8
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},
|
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"rms_norm_eps": 1e-05,
|
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+
"rope_scaling": null,
|
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+
"rope_theta": 500000.0,
|
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+
"tie_word_embeddings": false,
|
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+
"torch_dtype": "bfloat16",
|
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+
"transformers_version": "4.40.0",
|
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+
"use_cache": true,
|
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"use_flash_attention_2": true,
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"vocab_size": 128256
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+
}
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