Update README.md

README.md

@@ -2,17 +2,8 @@
 base_model:
 - bluuwhale/L3-SthenoMaidBlackroot-8B-V1
 - Casual-Autopsy/L3-Umbral-Mind-RP-v1.0-8B
-- bluuwhale/L3-SthenoMaidBlackroot-8B-V1
-- Casual-Autopsy/L3-Umbral-Mind-RP-v1.0-8B
-- bluuwhale/L3-SthenoMaidBlackroot-8B-V1
-- Casual-Autopsy/L3-Umbral-Mind-RP-v1.0-8B
-- bluuwhale/L3-SthenoMaidBlackroot-8B-V1
 tags:
 - merge
-- mergekit
-- lazymergekit
-- bluuwhale/L3-SthenoMaidBlackroot-8B-V1
-- Casual-Autopsy/L3-Umbral-Mind-RP-v1.0-8B
 ---
 
 # Llama3-Omphalos-12B
@@ -20,11 +11,6 @@ tags:
 Llama3-Omphalos-12B is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
 * [bluuwhale/L3-SthenoMaidBlackroot-8B-V1](https://huggingface.co/bluuwhale/L3-SthenoMaidBlackroot-8B-V1)
 * [Casual-Autopsy/L3-Umbral-Mind-RP-v1.0-8B](https://huggingface.co/Casual-Autopsy/L3-Umbral-Mind-RP-v1.0-8B)
-* [bluuwhale/L3-SthenoMaidBlackroot-8B-V1](https://huggingface.co/bluuwhale/L3-SthenoMaidBlackroot-8B-V1)
-* [Casual-Autopsy/L3-Umbral-Mind-RP-v1.0-8B](https://huggingface.co/Casual-Autopsy/L3-Umbral-Mind-RP-v1.0-8B)
-* [bluuwhale/L3-SthenoMaidBlackroot-8B-V1](https://huggingface.co/bluuwhale/L3-SthenoMaidBlackroot-8B-V1)
-* [Casual-Autopsy/L3-Umbral-Mind-RP-v1.0-8B](https://huggingface.co/Casual-Autopsy/L3-Umbral-Mind-RP-v1.0-8B)
-* [bluuwhale/L3-SthenoMaidBlackroot-8B-V1](https://huggingface.co/bluuwhale/L3-SthenoMaidBlackroot-8B-V1)
 
 ## 🧩 Configuration
 
@@ -53,29 +39,4 @@ slices:
 - sources:
   - layer_range: [25, 32]
     model: bluuwhale/L3-SthenoMaidBlackroot-8B-V1
-```
-
-## 💻 Usage
-
-```python
-!pip install -qU transformers accelerate
-
-from transformers import AutoTokenizer
-import transformers
-import torch
-
-model = "Tremontaine/Llama3-Omphalos-12B"
-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"])
 ```