87 lines
2.3 KiB
Markdown
87 lines
2.3 KiB
Markdown
---
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tags:
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- merge
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- mergekit
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- lazymergekit
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- FelixChao/WestSeverus-7B-DPO-v2
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- jsfs11/WestOrcaNeuralMarco-DPO-v2-DARETIES-7B
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- mlabonne/Daredevil-7B
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base_model:
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- FelixChao/WestSeverus-7B-DPO-v2
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- jsfs11/WestOrcaNeuralMarco-DPO-v2-DARETIES-7B
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- mlabonne/Daredevil-7B
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license: apache-2.0
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---
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# WONMSeverusDevil-TIES-7B
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WONMSeverusDevil-TIES-7B is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
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* [FelixChao/WestSeverus-7B-DPO-v2](https://huggingface.co/FelixChao/WestSeverus-7B-DPO-v2)
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* [jsfs11/WestOrcaNeuralMarco-DPO-v2-DARETIES-7B](https://huggingface.co/jsfs11/WestOrcaNeuralMarco-DPO-v2-DARETIES-7B)
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* [mlabonne/Daredevil-7B](https://huggingface.co/mlabonne/Daredevil-7B)
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```
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# Open-LLM Benchmark Results:
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WONMSeverusDevil-TIES-7B LLM AutoEval📑
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| Model |AGIEval|GPT4All|TruthfulQA|Bigbench|Average|
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|------------------------|------:|------:|---------:|-------:|------:|
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|WONMSeverusDevil-TIES-7B| 45.26| 77.07| 72.47| 48.85| 60.91|
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```
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# 🧩 Configuration
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```yaml
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models:
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- model: FelixChao/WestSeverus-7B-DPO-v2
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parameters:
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density: [1, 0.7, 0.1] # density gradient
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weight: 1.0
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- model: jsfs11/WestOrcaNeuralMarco-DPO-v2-DARETIES-7B
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parameters:
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density: 0.65
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weight: [0, 0.3, 0.7, 1] # weight gradient
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- model: mlabonne/Daredevil-7B
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parameters:
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density: 0.33
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weight:
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- filter: mlp
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value: 0.5
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- value: 0
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merge_method: ties
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base_model: mistralai/Mistral-7B-v0.1
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parameters:
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normalize: true
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int8_mask: true
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dtype: float16
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```
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## 💻 Usage
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```python
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!pip install -qU transformers accelerate
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from transformers import AutoTokenizer
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import transformers
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import torch
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model = "jsfs11/WONMSeverusDevil-TIES-7B"
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messages = [{"role": "user", "content": "What is a large language model?"}]
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tokenizer = AutoTokenizer.from_pretrained(model)
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prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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pipeline = transformers.pipeline(
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"text-generation",
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model=model,
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torch_dtype=torch.float16,
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device_map="auto",
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)
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outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
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print(outputs[0]["generated_text"])
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``` |