Instructions to use TeamDelta/Re-ultiima-32B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TeamDelta/Re-ultiima-32B with Transformers:

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

pipe = pipeline("text-generation", model="TeamDelta/Re-ultiima-32B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("TeamDelta/Re-ultiima-32B")
model = AutoModelForCausalLM.from_pretrained("TeamDelta/Re-ultiima-32B")
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

vLLM

How to use TeamDelta/Re-ultiima-32B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TeamDelta/Re-ultiima-32B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TeamDelta/Re-ultiima-32B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/TeamDelta/Re-ultiima-32B

SGLang

How to use TeamDelta/Re-ultiima-32B 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 "TeamDelta/Re-ultiima-32B" \
    --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": "TeamDelta/Re-ultiima-32B",
		"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 "TeamDelta/Re-ultiima-32B" \
        --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": "TeamDelta/Re-ultiima-32B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use TeamDelta/Re-ultiima-32B with Docker Model Runner:
```
docker model run hf.co/TeamDelta/Re-ultiima-32B
```

Holy-fox commited on Jan 29, 2025

Commit

8ef0b5b

verified ·

1 Parent(s): a4f406e

Delete mergekit_config.yml

Browse files

Files changed (1) hide show

mergekit_config.yml +0 -13

mergekit_config.yml DELETED Viewed

@@ -1,13 +0,0 @@
-models:
-  - model: /home/ubuntu/.cache/huggingface/hub/models--Qwen--Qwen2.5-32B-Instruct/snapshots/5ede1c97bbab6ce5cda5812749b4c0bdf79b18dd
-    parameters:
-      weight: 1
-      density: 1
-merge_method: ties
-base_model: /home/ubuntu/.cache/huggingface/hub/models--Qwen--Qwen2.5-32B/snapshots/1818d35814b8319459f4bd55ed1ac8709630f003
-parameters:
-  weight: 1
-  density: 1
-  normalize: true
-  int8_mask: true
-dtype: float16