Upload folder using huggingface_hub

README.md

@@ -0,0 +1,73 @@
+---
+tags:
+- merge
+- mergekit
+- lazymergekit
+- hfl/llama-3-chinese-8b-instruct-v2
+- NousResearch/Hermes-2-Theta-Llama-3-8B
+base_model:
+- hfl/llama-3-chinese-8b-instruct-v2
+- NousResearch/Hermes-2-Theta-Llama-3-8B
+- hfl/llama-3-chinese-8b-instruct-v2
+- NousResearch/Hermes-2-Theta-Llama-3-8B
+- hfl/llama-3-chinese-8b-instruct-v2
+---
+
+# Llama3-15B-lingyang-v0.1
+
+Llama3-15B-lingyang-v0.1 is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
+* [hfl/llama-3-chinese-8b-instruct-v2](https://huggingface.co/hfl/llama-3-chinese-8b-instruct-v2)
+* [NousResearch/Hermes-2-Theta-Llama-3-8B](https://huggingface.co/NousResearch/Hermes-2-Theta-Llama-3-8B)
+* [hfl/llama-3-chinese-8b-instruct-v2](https://huggingface.co/hfl/llama-3-chinese-8b-instruct-v2)
+* [NousResearch/Hermes-2-Theta-Llama-3-8B](https://huggingface.co/NousResearch/Hermes-2-Theta-Llama-3-8B)
+* [hfl/llama-3-chinese-8b-instruct-v2](https://huggingface.co/hfl/llama-3-chinese-8b-instruct-v2)
+
+## 🧩 Configuration
+
+```yaml
+slices:
+  - sources:
+      - model: "hfl/llama-3-chinese-8b-instruct-v2"
+        layer_range: [0, 10]
+  - sources:
+      - model: "NousResearch/Hermes-2-Theta-Llama-3-8B"
+        layer_range: [0, 20]
+  - sources:
+      - model: "hfl/llama-3-chinese-8b-instruct-v2"
+        layer_range: [10, 20]
+  - sources:
+      - model: "NousResearch/Hermes-2-Theta-Llama-3-8B"
+        layer_range: [20, 32]
+  - sources:
+      - model: "hfl/llama-3-chinese-8b-instruct-v2"
+        layer_range: [20, 32]
+
+merge_method: passthrough
+base_model: "NousResearch/Hermes-2-Theta-Llama-3-8B"
+dtype: bfloat16
+```
+
+## 💻 Usage
+
+```python
+!pip install -qU transformers accelerate
+
+from transformers import AutoTokenizer
+import transformers
+import torch
+
+model = "wwe180/Llama3-15B-lingyang-v0.1"
+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"])
+```