Update README.md

README.md

@@ -1,47 +1,36 @@
 ---
-base_model:
-- Ppoyaa/LlumiLuminRP-8B-Instruct-262k-v0.3
-- ChaoticNeutrals/Poppy_Porpoise-v0.7-L3-8B
 library_name: transformers
 tags:
 - mergekit
 - merge
-
+license: apache-2.0
 ---
-# merge
-
-This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
-
-## Merge Details
-### Merge Method
-
-This model was merged using the SLERP merge method.
-
-### Models Merged
-
-The following models were included in the merge:
-* [Ppoyaa/LlumiLuminRP-8B-Instruct-262k-v0.3](https://huggingface.co/Ppoyaa/LlumiLuminRP-8B-Instruct-262k-v0.3)
-* [ChaoticNeutrals/Poppy_Porpoise-v0.7-L3-8B](https://huggingface.co/ChaoticNeutrals/Poppy_Porpoise-v0.7-L3-8B)
-
-### Configuration
-
-The following YAML configuration was used to produce this model:
-
-```yaml
-slices:
-  - sources:
-      - model: ChaoticNeutrals/Poppy_Porpoise-v0.7-L3-8B
-        layer_range: [0, 32]
-      - model: Ppoyaa/LlumiLuminRP-8B-Instruct-262k-v0.3
-        layer_range: [0, 32]
-merge_method: slerp
-base_model: Ppoyaa/LlumiLuminRP-8B-Instruct-262k-v0.3
-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
-```
+# LlumiLuminRP-8B-Instruct-262k-v0.4
+![1715297915105.png](https://cdn-uploads.huggingface.co/production/uploads/65f158693196560d34495d54/SoMXojKFU1ZseLPfalfy0.png)
+***
+## Description
+An update to v0.3 to improve coherence and roleplaying experience. This model is the result of merging a bunch of Llama-3-8B RP/ERP models and is using a context window of 262k.
+***
+## 💻 Usage
+```python
+!pip install -qU transformers accelerate
+
+from transformers import AutoTokenizer
+import transformers
+import torch
+
+model = "Ppoyaa/LlumiLuminRP-8B-Instruct-262k-v0.4"
+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"])
+```