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README.md

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+---
+tags:
+- merge
+- mergekit
+- lazymergekit
+- Kukedlc/NeuralContamination-7B-ties
+- Kukedlc/NeuralTopBench-7B-ties
+- Gille/StrangeMerges_32-7B-slerp
+base_model:
+- Kukedlc/NeuralContamination-7B-ties
+- Kukedlc/NeuralTopBench-7B-ties
+- Gille/StrangeMerges_32-7B-slerp
+---
+
+# Jupiter-k-7B-slerp
+
+Jupiter-k-7B-slerp is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
+* [Kukedlc/NeuralContamination-7B-ties](https://huggingface.co/Kukedlc/NeuralContamination-7B-ties)
+* [Kukedlc/NeuralTopBench-7B-ties](https://huggingface.co/Kukedlc/NeuralTopBench-7B-ties)
+* [Gille/StrangeMerges_32-7B-slerp](https://huggingface.co/Gille/StrangeMerges_32-7B-slerp)
+
+## 🧩 Configuration
+
+```yaml
+models:
+  - model: Kukedlc/NeuralContamination-7B-ties
+    parameters:
+      density: [1, 0.7, 0.1] # density gradient
+      weight: 1.0
+  - model: Kukedlc/NeuralTopBench-7B-ties
+    parameters:
+      density: 0.5
+      weight: [0, 0.3, 0.7, 1] # weight gradient
+  - model: Gille/StrangeMerges_32-7B-slerp
+    parameters:
+      density: 0.33
+      weight:
+        - filter: mlp
+          value: 0.5
+        - value: 0
+merge_method: ties
+base_model: Kukedlc/NeuralMaxime-7B-slerp 
+parameters:
+  normalize: true
+  int8_mask: true
+dtype: bfloat16
+```
+
+## 💻 Usage - Stream
+
+```python
+# Requirements
+!pip install -qU transformers accelerate bitsandbytes
+
+# Imports & settings
+from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
+import warnings
+import os
+os.environ["TOKENIZERS_PARALLELISM"] = "false"
+warnings.filterwarnings('ignore')
+
+# Model & Tokenizer
+MODEL_NAME = "Kukedlc/Jupiter-k-7B-slerp"
+model = AutoModelForCausalLM.from_pretrained(MODEL_NAME, device_map='cuda:1', load_in_4bit=True)
+tok = AutoTokenizer.from_pretrained(MODEL_NAME)
+
+# Inference
+prompt = "I want you to generate a theory that unites quantum mechanics with the theory of relativity and cosmic consciousness
+"
+inputs = tok([prompt], return_tensors="pt").to('cuda')
+streamer = TextStreamer(tok)
+
+# Despite returning the usual output, the streamer will also print the generated text to stdout.
+_ = model.generate(**inputs, streamer=streamer, max_new_tokens=512, do_sample=True, num_beams=1, top_p=0.9, temperature=0.7)
+
+```
+## 💻 Usage - Clasic
+
+```python
+!pip install -qU transformers bitsandbytes accelerate
+
+from transformers import AutoTokenizer
+import transformers
+import torch
+
+model = "Kukedlc/Jupiter-k-7B-slerp"
+
+tokenizer = AutoTokenizer.from_pretrained(model)
+pipeline = transformers.pipeline(
+    "text-generation",
+    model=model,
+    model_kwargs={"torch_dtype": torch.float16, "load_in_4bit": True},
+)
+
+messages = [{"role": "user", "content": "Explain what a Mixture of Experts is in less than 100 words."}]
+prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+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"])
+```

config.json

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+{
+  "_name_or_path": "Kukedlc/NeuralMaxime-7B-slerp",
+  "architectures": [
+    "MistralForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 14336,
+  "max_position_embeddings": 32768,
+  "model_type": "mistral",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 8,
+  "rms_norm_eps": 1e-05,
+  "rope_theta": 10000.0,
+  "sliding_window": 4096,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.38.2",
+  "use_cache": true,
+  "vocab_size": 32000
+}

mergekit_config.yml

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+
+models:
+  - model: Kukedlc/NeuralContamination-7B-ties
+    parameters:
+      density: [1, 0.7, 0.1] # density gradient
+      weight: 1.0
+  - model: Kukedlc/NeuralTopBench-7B-ties
+    parameters:
+      density: 0.5
+      weight: [0, 0.3, 0.7, 1] # weight gradient
+  - model: Gille/StrangeMerges_32-7B-slerp
+    parameters:
+      density: 0.33
+      weight:
+        - filter: mlp
+          value: 0.5
+        - value: 0
+merge_method: ties
+base_model: Kukedlc/NeuralMaxime-7B-slerp 
+parameters:
+  normalize: true
+  int8_mask: true
+dtype: bfloat16

model-00001-of-00002.safetensors

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model.safetensors.index.json

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tokenizer_config.json

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