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 14, 2024

Commit

76bdbc3

verified ·

1 Parent(s): 418e43b

docs: update model with new name

Browse files

Files changed (1) hide show

README.md +7 -6

README.md CHANGED Viewed

@@ -1,5 +1,7 @@
 ---
 license: other
 pipeline_tag: text-generation
 language:
 - en
@@ -10,10 +12,10 @@ tags:
 - qwen2
 ---
-# FeatherQwen2-72B-v0.1
 ## Model Description
-FeatherQwen 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
@@ -44,15 +46,14 @@ Custom script utilizing safetensors library.
 from transformers import AutoModelForCausalLM, AutoTokenizer
 import torch
-model = AutoModelForCausalLM.from_pretrained("leafspark/FeatherQwen2-72B-v0.1",
                                              device_map="auto",
                                              torch_dtype=torch.float16)
-tokenizer = AutoTokenizer.from_pretrained("leafspark/FeatherQwen2-72B-v0.1")
 ```
 ### GGUFs
-Find them here: [leafspark/FeatherQwen2-72B-v0.1-GGUF](https://huggingface.co/leafspark/FeatherQwen2-72B-v0.1-GGUF)
 ### Hardware Requirements
 - Minimum ~140GB of storage

 ---
 license: other
+license_name: tongyi-qianwen
+license_link: https://huggingface.co/Qwen/Qwen2-72B-Instruct/blob/main/LICENSE
 pipeline_tag: text-generation
 language:
 - en
 - 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
 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](https://huggingface.co/leafspark/Iridium-72B-v0.1-GGUF)
 ### Hardware Requirements
 - Minimum ~140GB of storage