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--- |
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license: other |
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license_name: tongyi-qianwen |
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license_link: https://huggingface.co/Qwen/Qwen2-72B-Instruct/blob/main/LICENSE |
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pipeline_tag: text-generation |
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language: |
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- en |
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- zh |
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library_name: transformers |
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tags: |
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- mergekit |
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- llama |
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--- |
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# IridiumLlama-72B-v0.1 |
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## Model Description |
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IridiumLlama is a 72B parameter language model created through a merge of Qwen2-72B-Instruct, calme2.1-72b, and magnum-72b-v1 using `model_stock`. |
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This is converted from [leafspark/Iridium-72B-v0.1](https://huggingface.co/leafspark/Iridium-72B-v0.1) (currently private) |
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## Features |
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- 72 billion parameters |
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- Sharded in 31 files (unlike Iridium, which has 963 shards due to the merging process) |
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- Combines Magnum prose with Calam smarts |
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- Llamaified for easy use |
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## Technical Specifications |
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### Architecture |
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- `LlamaForCasualLM` |
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- Models: Qwen2-72B-Instruct (base), calme2.1-72b, magnum-72b-v1 |
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- Merged layers: 80 |
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- Total tensors: 1,043 |
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- Context length: 32k |
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### Tensor Distribution |
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- Attention layers: 560 files |
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- MLP layers: 240 files |
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- Layer norms: 160 files |
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- Miscellaneous (embeddings, output): 162 files |
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### Merging |
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Custom script utilizing safetensors library. |
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## Usage |
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### Loading the Model |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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import torch |
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model = AutoModelForCausalLM.from_pretrained("leafspark/IridiumLlama-72B-v0.1", |
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device_map="auto", |
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torch_dtype=torch.float16) |
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tokenizer = AutoTokenizer.from_pretrained("leafspark/IridiumLlama-72B-v0.1") |
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``` |
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### GGUFs |
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Find them here: [mradermacher/IridiumLlama-72B-v0.1-GGUF](https://huggingface.co/mradermacher/IridiumLlama-72B-v0.1-GGUF) |
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### Hardware Requirements |
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- At least ~150GB of free space |
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- ~150GB VRAM |
