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Model: Gille/StrangeMerges_30-7B-slerp
Source: Original Platform

README.md

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+---
+license: apache-2.0
+tags:
+- merge
+- mergekit
+- lazymergekit
+- Gille/StrangeMerges_21-7B-slerp
+- yam-peleg/Experiment26-7B
+base_model:
+- Gille/StrangeMerges_21-7B-slerp
+- yam-peleg/Experiment26-7B
+---
+
+# StrangeMerges_30-7B-slerp
+
+Given the benchmark score distribution this model might jump several spots if trained on something like orca-math or thruthy datasets.
+Anyone got a good walkthrough vid with about how long training takes/colab costs, etc?
+
+StrangeMerges_30-7B-slerp is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
+* [Gille/StrangeMerges_21-7B-slerp](https://huggingface.co/Gille/StrangeMerges_21-7B-slerp)
+* [yam-peleg/Experiment26-7B](https://huggingface.co/yam-peleg/Experiment26-7B)
+
+## 🧩 Configuration
+
+```yaml
+slices:
+  - sources:
+      - model: Gille/StrangeMerges_21-7B-slerp
+        layer_range: [0, 32]
+      - model: yam-peleg/Experiment26-7B
+        layer_range: [0, 32]
+merge_method: slerp
+base_model: Gille/StrangeMerges_21-7B-slerp
+parameters:
+  t:
+    - filter: self_attn
+      value: [0, 0.5, 0.3, 0.7, 1]
+    - filter: mlp
+      value: [1, 0.5, 0.7, 0.3, 0]
+    - value: 0.5
+dtype: bfloat16
+```
+
+## 💻 Usage
+
+```python
+!pip install -qU transformers accelerate
+
+from transformers import AutoTokenizer
+import transformers
+import torch
+
+model = "Gille/StrangeMerges_30-7B-slerp"
+messages = [{"role": "user", "content": "What is a large language model?"}]
+
+tokenizer = AutoTokenizer.from_pretrained(model)
+prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+pipeline = transformers.pipeline(
+    "text-generation",
+    model=model,
+    torch_dtype=torch.float16,
+    device_map="auto",
+)
+
+outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
+print(outputs[0]["generated_text"])
+```