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.ipynb_checkpoints/README-checkpoint.md

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+---
+base_model:
+- NousResearch/Hermes-3-Llama-3.1-70B
+- Fizzarolli/L3.1-70b-glitz-v0.2
+- cyberagent/Llama-3.1-70B-Japanese-Instruct-2407
+- Sao10K/L3-70B-Euryale-v2.1
+tags:
+- merge
+- mergekit
+- lazymergekit
+- NousResearch/Hermes-3-Llama-3.1-70B
+- Fizzarolli/L3.1-70b-glitz-v0.2
+- cyberagent/Llama-3.1-70B-Japanese-Instruct-2407
+- Sao10K/L3-70B-Euryale-v2.1
+---
+
+# L3.1-70b-Ginny
+
+ 
+
+L3.1-70b-Ginny is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
+* [NousResearch/Hermes-3-Llama-3.1-70B](https://huggingface.co/NousResearch/Hermes-3-Llama-3.1-70B)
+* [Fizzarolli/L3.1-70b-glitz-v0.2](https://huggingface.co/Fizzarolli/L3.1-70b-glitz-v0.2)
+* [cyberagent/Llama-3.1-70B-Japanese-Instruct-2407](https://huggingface.co/cyberagent/Llama-3.1-70B-Japanese-Instruct-2407)
+* [Sao10K/L3-70B-Euryale-v2.1](https://huggingface.co/Sao10K/L3-70B-Euryale-v2.1)
+
+I really liked Glitz and Euryale. Though they can get kinda wish-washy and don't follow structure well enough. 
+I used Hermes as a base as it has rather good instruct following but it's way too instruct focused. 
+I find myself running into Japanese text too. Which neither 3 models are like superb at, so I used cyberagent's Japanese Instruct to give it a boost.
+
+## 🧩 Configuration
+
+```yaml
+
+models:
+  - model: NousResearch/Hermes-3-Llama-3.1-70B
+    parameters:
+      density: 0.33
+      weight: 0.25
+  - model: Fizzarolli/L3.1-70b-glitz-v0.2
+    parameters:
+      density: 0.7
+      weight: 0.5
+  - model: cyberagent/Llama-3.1-70B-Japanese-Instruct-2407
+    parameters:
+      density: 0.5
+      weight: 0.25
+  - model: Sao10K/L3-70B-Euryale-v2.1
+    parameters:
+      density: 0.7
+      weight: 0.5
+
+merge_method: ties
+base_model: NousResearch/Hermes-3-Llama-3.1-70B
+parameters:
+  normalize: true
+dtype: bfloat16
+```
+
+## 💻 Usage
+
+```python
+!pip install -qU transformers accelerate
+
+from transformers import AutoTokenizer
+import transformers
+import torch
+
+model = "KaraKaraWitch/L3.1-70b-Ginny"
+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"])
+```

model-00142-of-00162.safetensors

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+size 771785544