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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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- qwen2 |
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--- |
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# Iridium-72B-v0.1 |
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## Model Description |
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Iridium 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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## Features |
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- 72 billion parameters |
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- Combines Magnum prose with Calam smarts |
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## Technical Specifications |
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### Architecture |
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- `Qwen2ForCasualLM` |
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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: 963 |
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- Context length: 128k |
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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): 3 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/Iridium-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/Iridium-72B-v0.1") |
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``` |
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### GGUFs |
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Find them here: [leafspark/Iridium-72B-v0.1-GGUF](https://huggingface.co/leafspark/Iridium-72B-v0.1-GGUF) |
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### Optimal Sampling Parameters |
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I found these to work well: |
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```json |
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{ |
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"temperature": 1 |
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"min_p": 0.08 |
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"top_p": 1 |
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"top_k": 40 |
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"repetition_penalty": 1 |
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} |
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``` |
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### Hardware Requirements |
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- At least 135GB of free space |
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- ~140GB VRAM/RAM |
