metadata
license: other
license_name: tongyi-qianwen
license_link: https://huggingface.co/Qwen/Qwen2-72B-Instruct/blob/main/LICENSE
pipeline_tag: text-generation
language:
- en
- zh
library_name: transformers
tags:
- mergekit
- qwen2
Iridium-72B-v0.1
Model Description
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.
Features
- 72 billion parameters
- Combines Magnum prose with Calam smarts
Technical Specifications
Architecture
Qwen2ForCasualLM- Models: Qwen2-72B-Instruct (base), calme2.1-72b, magnum-72b-v1
- Merged layers: 80
- Total tensors: 963
- Context length: 128k
Tensor Distribution
- Attention layers: 560 files
- MLP layers: 240 files
- Layer norms: 160 files
- Miscellaneous (embeddings, output): 3 files
Merging
Custom script utilizing safetensors library.
Usage
Loading the Model
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model = AutoModelForCausalLM.from_pretrained("leafspark/Iridium-72B-v0.1",
device_map="auto",
torch_dtype=torch.float16)
tokenizer = AutoTokenizer.from_pretrained("leafspark/Iridium-72B-v0.1")
GGUFs
Find them here: leafspark/Iridium-72B-v0.1-GGUF
Optimal Sampling Parameters
I found these to work well:
{
"temperature": 1
"min_p": 0.08
"top_p": 1
"top_k": 40
"repetition_penalty": 1
}
Hardware Requirements
- At least 135GB of free space
- ~140GB VRAM/RAM