102 lines
3.2 KiB
Markdown
102 lines
3.2 KiB
Markdown
---
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license: apache-2.0
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tags:
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- merge
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- mergekit
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- lazymergekit
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- automerger
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base_model:
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- automerger/YamShadow-7B
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- yam-peleg/Experiment28-7B
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---
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# 🧪 YamshadowExperiment28-7B
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**🎉 YamshadowExperiment28-7B is currently the best-performing 7B model on the Open LLM Leaderboard (08 Apr 24). Use it with caution, as it is likely a sign of overfitting the benchmarks.**
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YamshadowExperiment28-7B is an automated merge created by [Maxime Labonne](https://huggingface.co/mlabonne) using the following configuration.
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* [automerger/YamShadow-7B](https://huggingface.co/automerger/YamShadow-7B)
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* [yam-peleg/Experiment28-7B](https://huggingface.co/yam-peleg/Experiment28-7B)
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## 🔍 Applications
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This model uses a context window of 8k. I recommend using it with the Alpaca chat template (works perfectly with LM Studio).
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The model can sometimes break and output a lot of "INST". From my experience, its excellent results on the Open LLM Leaderboard are probably a sign of overfitting.
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## ⚡ Quantized models
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* **GGUF**: https://huggingface.co/automerger/YamshadowExperiment28-7B-GGUF
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## 🏆 Evaluation
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### Open LLM Leaderboard
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YamshadowExperiment28-7B is currently the best-performing 7B model on the Open LLM Leaderboard (08 Apr 24).
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### EQ-bench
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Thanks to [Samuel J. Paech](https://twitter.com/sam_paech), who kindly ran the evaluation.
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### Nous
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Evaluation performed using [LLM AutoEval](https://github.com/mlabonne/llm-autoeval). See the entire leaderboard [here](https://huggingface.co/spaces/mlabonne/Yet_Another_LLM_Leaderboard).
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## 🌳 Model Family Tree
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## 🧩 Configuration
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```yaml
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slices:
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- sources:
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- model: automerger/YamShadow-7B
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layer_range: [0, 32]
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- model: yam-peleg/Experiment28-7B
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layer_range: [0, 32]
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merge_method: slerp
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base_model: automerger/YamShadow-7B
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parameters:
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t:
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- filter: self_attn
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value: [0, 0.5, 0.3, 0.7, 1]
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- filter: mlp
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value: [1, 0.5, 0.7, 0.3, 0]
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- value: 0.5
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dtype: bfloat16
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random_seed: 0
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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 = "automerger/YamshadowExperiment28-7B"
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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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``` |