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MoMo-70B-lora-1.8.6-DPO-exl2-4.0bpw

Text Generation
Transformers
Safetensors
English
llama
text-generation-inference
Inference Endpoints
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Files changed (15) hide show
  1. README.md +45 -0
  2. config.json +30 -0
  3. generation_config.json +7 -0
  4. huggingface-metadata.txt +67 -0
  5. merges.txt +0 -0
  6. model.safetensors.index.json +1050 -0
  7. output-00001-of-00005.safetensors +3 -0
  8. output-00002-of-00005.safetensors +3 -0
  9. output-00003-of-00005.safetensors +3 -0
  10. output-00004-of-00005.safetensors +3 -0
  11. output-00005-of-00005.safetensors +3 -0
  12. special_tokens_map.json +5 -0
  13. tokenizer.json +0 -0
  14. tokenizer_config.json +1675 -0
  15. vocab.json +0 -0
README.md ADDED
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+ ---
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+ license: mit
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+ language:
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+ - en
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+ ---
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+ # **Introduction**
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+ MoMo-70B-lora-1.8.6-DPO is trained via Direct Preference Optimization([DPO](https://arxiv.org/abs/2305.18290)) from [MoMo-70B-LoRA-V1.4](https://huggingface.co/moreh/MoMo-70B-LoRA-V1.4) as its base model, with several optimizations in hyperparameters.
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+ [MoMo-70B-LoRA-V1.4](https://huggingface.co/moreh/MoMo-70B-LoRA-V1.4) is trained via Supervised Fine-Tuning (SFT) using [LoRA](https://arxiv.org/abs/2106.09685), with the QWEN-72B model as its base-model.
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+ Note that we did not exploit any form of weight merge.
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+ For leaderboard submission, the trained weight is realigned for compatibility with llama.
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+ MoMo-70B is trained using **[Moreh](https://moreh.io/)**'s [MoAI platform](https://moreh.io/product), which simplifies the training of large-scale models, and AMD's MI250 GPU.
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+
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+
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+ ## Details
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+ ### Used Librarys
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+ - torch
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+ - peft
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+ ### Used Datasets
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+ - [slimorca](Open-Orca/SlimOrca)
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+ - [truthy](https://huggingface.co/datasets/jondurbin/truthy-dpo-v0.1)
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+ - [orca_dpo_pairs](https://huggingface.co/datasets/Intel/orca_dpo_pairs)
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+ - No other dataset was used
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+ - No benchmark test set or the training set are used
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+ - [data contamination check](https://github.com/swj0419/detect-pretrain-code-contamination) result
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+
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+ | Model | ARC | MMLU | TruthfulQA | GSM8K |
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+ |------------------------------|-------|-------|-------|-------|
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+ | **V1.8.6(result < 0.1, %)**| TBU |TBU | 0.73 | TBU |
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+ ### Used Environments
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+ - AMD MI250 & MoAI platform
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+ - Please visit https://moreh.io/product for more information about MoAI platform
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+ - Or, contact us directly [contact@moreh.io](mailto:contact@moreh.io)
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+
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+ ## How to use
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+
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+ ```python
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+ # pip install transformers==4.35.2
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+ import torch
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+ from transformers import AutoModelForCausalLM, AutoTokenizer
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+
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+ tokenizer = AutoTokenizer.from_pretrained("moreh/MoMo-70B-LoRA-V1.8.6")
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+ model = AutoModelForCausalLM.from_pretrained(
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+ "moreh/MoMo-70B-LoRA-V1.8.6"
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+ )
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+ ```
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+ "_name_or_path": "/home/moreh_llm/team_model/junhyeok/tokenizer_new/config_llama.json",
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+ "architectures": [
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+ "LlamaForCausalLM"
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+ "attention_bias": true,
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+ "attention_dropout": 0.0,
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+ "bos_token_id": 1,
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+ "eos_token_id": 2,
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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": 24576,
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+ "max_position_embeddings": 32768,
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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": 64,
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+ "pad_token_id": 151643,
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+ "pretraining_tp": 1,
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+ "rms_norm_eps": 1e-06,
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+ "rope_scaling": null,
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+ "rope_theta": 1000000,
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+ "seq_length": 32768,
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+ "tie_word_embeddings": false,
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+ "torch_dtype": "float32",
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+ "transformers_version": "4.36.0",
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+ "use_cache": true,
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+ "vocab_size": 152064
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+ }
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+ url: https://huggingface.co/moreh/MoMo-70B-lora-1.8.6-DPO
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+ branch: main
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+ download date: 2024-01-17 10:52:08
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vocab.json ADDED
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