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README.md ADDED
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+ ---
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+ license: apache-2.0
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+ tags:
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+ - trl
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+ - orpo
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+ - generated_from_trainer
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+ - mlx
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+ base_model: mistral-community/Mixtral-8x22B-v0.1
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+ datasets:
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+ - argilla/distilabel-capybara-dpo-7k-binarized
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+ inference:
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+ parameters:
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+ temperature: 0.7
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+ model-index:
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+ - name: zephyr-orpo-141b-A35b-v0.1
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+ results: []
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+ ---
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+
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+ # mlx-community/zephyr-orpo-141b-A35b-v0.1-8bit
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+ This model was converted to MLX format from [`HuggingFaceH4/zephyr-orpo-141b-A35b-v0.1`]() using mlx-lm version **0.9.0**.
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+ Refer to the [original model card](https://huggingface.co/HuggingFaceH4/zephyr-orpo-141b-A35b-v0.1) for more details on the model.
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+ ## Use with mlx
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+
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+ ```bash
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+ pip install mlx-lm
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+ ```
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+
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+ ```python
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+ from mlx_lm import load, generate
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+
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+ model, tokenizer = load("mlx-community/zephyr-orpo-141b-A35b-v0.1-8bit")
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+ response = generate(model, tokenizer, prompt="hello", verbose=True)
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+ ```
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+ {
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+ "add_cross_attention": false,
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+ "architectures": [
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+ "MixtralForCausalLM"
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+ ],
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+ "0": "LABEL_0",
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+ },
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+ "initializer_range": 0.02,
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+ "length_penalty": 1.0,
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+ "model_type": "mixtral",
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28
+ }
29
+ },
30
+ "additional_special_tokens": [],
31
+ "bos_token": "<s>",
32
+ "chat_template": "{% for message in messages %}\n{% if message['role'] == 'user' %}\n{{ '<|user|>\n' + message['content'] + eos_token }}\n{% elif message['role'] == 'system' %}\n{{ '<|system|>\n' + message['content'] + eos_token }}\n{% elif message['role'] == 'assistant' %}\n{{ '<|assistant|>\n' + message['content'] + eos_token }}\n{% endif %}\n{% if loop.last and add_generation_prompt %}\n{{ '<|assistant|>' }}\n{% endif %}\n{% endfor %}",
33
+ "clean_up_tokenization_spaces": false,
34
+ "eos_token": "</s>",
35
+ "legacy": true,
36
+ "model_max_length": 2048,
37
+ "pad_token": "</s>",
38
+ "sp_model_kwargs": {},
39
+ "spaces_between_special_tokens": false,
40
+ "tokenizer_class": "LlamaTokenizer",
41
+ "unk_token": "<unk>",
42
+ "use_default_system_prompt": false
43
+ }