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

Libraries

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

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

pipe = pipeline("text-generation", model="leafspark/Iridium-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/Iridium-72B-v0.1")
model = AutoModelForCausalLM.from_pretrained("leafspark/Iridium-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/Iridium-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/Iridium-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/Iridium-72B-v0.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

How to use leafspark/Iridium-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/Iridium-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/Iridium-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/Iridium-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/Iridium-72B-v0.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

leafspark commited on Aug 15, 2024

Commit

0d66f37

verified ·

1 Parent(s): ab895a3

docs: update model card

Browse files

Files changed (1) hide show

README.md +2 -3

README.md CHANGED Viewed

@@ -19,7 +19,6 @@ Iridium is a 72B parameter language model created through a merge of Qwen2-72B-I
 ## Features
 - 72 billion parameters
-- Comes in 1,043 individual safetensor files
 - Combines Magnum prose with Calam smarts
 ## Technical Specifications
@@ -28,14 +27,14 @@ Iridium is a 72B parameter language model created through a merge of Qwen2-72B-I
 - `Qwen2ForCasualLM`
 - Models: Qwen2-72B-Instruct (base), calme2.1-72b, magnum-72b-v1
 - Merged layers: 80
-- Total tensors: 1,043
 - Context length: 128k
 ### Tensor Distribution
 - Attention layers: 560 files
 - MLP layers: 240 files
 - Layer norms: 160 files
-- Miscellaneous (embeddings, output): 83 files
 ### Merging
 Custom script utilizing safetensors library.

 ## Features
 - 72 billion parameters
 - Combines Magnum prose with Calam smarts
 ## Technical Specifications
 - `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.