Create README.md

README.md

@@ -0,0 +1,75 @@
+---
+license: other
+tags:
+- merge
+- mergekit
+- lazymergekit
+base_model: mlabonne/Meta-Llama-3-120B-Instruct
+---
+
+
+# Meta-Llama-3-120B-Instruct- GGUF
+
+- This is quantized version of [mlabonne/Meta-Llama-3-120B-Instruct](https://huggingface.co/mlabonne/Meta-Llama-3-120B-Instruct) created using llama.cpp
+
+# Model Description
+
+Meta-Llama-3-120B-Instruct is a self-merge with [meta-llama/Meta-Llama-3-70B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct).
+
+It was inspired by large merges like [alpindale/goliath-120b](https://huggingface.co/alpindale/goliath-120b), [nsfwthrowitaway69/Venus-120b-v1.0](https://huggingface.co/nsfwthrowitaway69/Venus-120b-v1.0), [cognitivecomputations/MegaDolphin-120b](https://huggingface.co/cognitivecomputations/MegaDolphin-120b), and [wolfram/miquliz-120b-v2.0](https://huggingface.co/wolfram/miquliz-120b-v2.0).
+
+No eval yet, but it is approved by Eric Hartford: https://twitter.com/erhartford/status/1787050962114207886
+
+## 🧩 Configuration
+
+```yaml
+slices:
+- sources:
+  - layer_range: [0, 20]
+    model: meta-llama/Meta-Llama-3-70B-Instruct
+- sources:
+  - layer_range: [10, 30]
+    model: meta-llama/Meta-Llama-3-70B-Instruct
+- sources:
+  - layer_range: [20, 40]
+    model: meta-llama/Meta-Llama-3-70B-Instruct
+- sources:
+  - layer_range: [30, 50]
+    model: meta-llama/Meta-Llama-3-70B-Instruct
+- sources:
+  - layer_range: [40, 60]
+    model: meta-llama/Meta-Llama-3-70B-Instruct
+- sources:
+  - layer_range: [50, 70]
+    model: meta-llama/Meta-Llama-3-70B-Instruct
+- sources:
+  - layer_range: [60, 80]
+    model: meta-llama/Meta-Llama-3-70B-Instruct
+merge_method: passthrough
+dtype: float16
+```
+
+## 💻 Usage
+
+```python
+!pip install -qU transformers accelerate
+
+from transformers import AutoTokenizer
+import transformers
+import torch
+
+model = "mlabonne/Llama-3-120B"
+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"])
+```