Instructions to use leafspark/IridiumLlama-72B-v0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use leafspark/IridiumLlama-72B-v0.1 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="leafspark/IridiumLlama-72B-v0.1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("leafspark/IridiumLlama-72B-v0.1")
model = AutoModelForCausalLM.from_pretrained("leafspark/IridiumLlama-72B-v0.1", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use leafspark/IridiumLlama-72B-v0.1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "leafspark/IridiumLlama-72B-v0.1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "leafspark/IridiumLlama-72B-v0.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/leafspark/IridiumLlama-72B-v0.1

SGLang

How to use leafspark/IridiumLlama-72B-v0.1 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "leafspark/IridiumLlama-72B-v0.1" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "leafspark/IridiumLlama-72B-v0.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "leafspark/IridiumLlama-72B-v0.1" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "leafspark/IridiumLlama-72B-v0.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use leafspark/IridiumLlama-72B-v0.1 with Docker Model Runner:
```
docker model run hf.co/leafspark/IridiumLlama-72B-v0.1
```

IridiumLlama-72B-v0.1 / README.md

leafspark

docs: add mradermacher's GGUFs

9e3c635 verified about 2 years ago

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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
  - llama

IridiumLlama-72B-v0.1

Model Description

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.

This is converted from leafspark/Iridium-72B-v0.1 (currently private)

Features

72 billion parameters
Sharded in 31 files (unlike Iridium, which has 963 shards due to the merging process)
Combines Magnum prose with Calam smarts
Llamaified for easy use

Technical Specifications

Architecture

LlamaForCasualLM
Models: Qwen2-72B-Instruct (base), calme2.1-72b, magnum-72b-v1
Merged layers: 80
Total tensors: 1,043
Context length: 32k

Tensor Distribution

Attention layers: 560 files
MLP layers: 240 files
Layer norms: 160 files
Miscellaneous (embeddings, output): 162 files

Merging

Custom script utilizing safetensors library.

Usage

Loading the Model

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model = AutoModelForCausalLM.from_pretrained("leafspark/IridiumLlama-72B-v0.1", 
                                             device_map="auto", 
                                             torch_dtype=torch.float16)
tokenizer = AutoTokenizer.from_pretrained("leafspark/IridiumLlama-72B-v0.1")

GGUFs

Find them here: mradermacher/IridiumLlama-72B-v0.1-GGUF

Hardware Requirements

At least ~150GB of free space
~150GB VRAM