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README.md
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---
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base_model:
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library_name: transformers
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tags:
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- mergekit
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- merge
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---
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# BigLlama-3.1-1T-Instruct
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##
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### Merge Method
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* [mlabonne/BigLlama-3.1-681B-Instruct](https://huggingface.co/mlabonne/BigLlama-3.1-681B-Instruct)
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The following YAML configuration was used to produce this model:
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```yaml
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slices:
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model: mlabonne/BigLlama-3.1-681B-Instruct
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merge_method: passthrough
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dtype: bfloat16
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```
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---
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base_model:
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- meta-llama/Meta-Llama-3.1-681B-Instruct
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library_name: transformers
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tags:
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- mergekit
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- merge
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---
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# 🦙✨ BigLlama-3.1-1T-Instruct
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![image/png](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/ywomdgvQYP9cpr-PH1nf7.png)
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<center>🦙⛰️ <i><a href="https://huggingface.co/mlabonne/BigLlama-3.1-681B-Instruct">mlabonne/BigLlama-3.1-681B-Instruct</a></i></center>
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This is an experimental self-merge using [meta-llama/Meta-Llama-3.1-405B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-405B-Instruct) and created with [mergekit](https://github.com/cg123/mergekit).
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This is the direct successor of [Meta-Llama-3-120B-Instruct](https://huggingface.co/mlabonne/Meta-Llama-3-120B-Instruct), a self-merge of Llama 3 70B that produced a decent 120B model for tasks like creative writing.
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I tweaked the range of duplicated layers to hopefully make a sensible model. Use it at your own risk!
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## 🔍 Applications
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I recommend using this model for creative writing with the Llama 3 chat template.
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## ⚡ Quantization
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TBD.
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## 🏆 Evaluation
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TBD.
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## 🧩 Configuration
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This model was merged using the passthrough merge method. The following YAML configuration was used to produce this model:
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```yaml
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slices:
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model: mlabonne/BigLlama-3.1-681B-Instruct
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merge_method: passthrough
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dtype: bfloat16
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```
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Here is the code I've used to generate the config and calculate the number of layers/parameters after passthrough:
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```python
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def generate_yaml_config(range_size, total_layers, nb_parameters):
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new_size = total_layers + total_layers - range_size
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new_param = (nb_parameters / total_layers) * new_size
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print(f"New size = {new_size} layers")
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print(f"New parameters = {new_param:.2f}B")
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yaml_str = "slices:\n"
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for i in range(0, round(total_layers - range_size + 1), range_size // 2):
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start = i
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end = min(start + range_size, total_layers)
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yaml_str += f"- sources:\n"
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yaml_str += f" - layer_range: [{start}, {end}]\n"
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yaml_str += f" model: meta-llama/Meta-Llama-3.1-405B-Instruct\n"
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yaml_str += "merge_method: passthrough\n"
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yaml_str += "dtype: bfloat16\n"
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print(yaml_str)
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return new_size, new_param
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# Example usage
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new_size, new_param = generate_yaml_config(42, 126, 410)
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new_size, new_param = generate_yaml_config(105, new_size, new_param)
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```
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