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
```

lbourdois commited on Apr 27, 2025

Commit

3f60329

verified ·

1 Parent(s): 8ef0b5b

Improve language tag

Browse files

Hi! As the model is multilingual, this is a PR to add other languages than English to the language tag to improve the referencing. Note that 29 languages are announced in the README, but only 13 are explicitly listed. I was therefore only able to add these 13 languages.

Files changed (1) hide show

README.md +56 -44

README.md CHANGED Viewed

@@ -1,45 +1,57 @@
----
-base_model:
-- Qwen/Qwen2.5-32B
-- Qwen/Qwen2.5-32B-Instruct
-library_name: transformers
-tags:
-- mergekit
-- merge
-license: apache-2.0
-language:
-- en
----
-# Re:ultiima-32B
-This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
-## Merge Details
-### Merge Method
-This model was merged using the [TIES](https://arxiv.org/abs/2306.01708) merge method using Qwen/Qwen2.5-32B as a base.
-### Models Merged
-The following models were included in the merge:
-* Qwen/Qwen2.5-32B-Instruct
-### Configuration
-The following YAML configuration was used to produce this model:
-```yaml
-models:
-  - model: Qwen/Qwen2.5-32B-Instruct
-    parameters:
-      weight: 1
-      density: 1
-merge_method: ties
-base_model: Qwen/Qwen2.5-32B
-parameters:
-  weight: 1
-  density: 1
-  normalize: true
-  int8_mask: true
-dtype: float16
 ```

+---
+base_model:
+- Qwen/Qwen2.5-32B
+- Qwen/Qwen2.5-32B-Instruct
+library_name: transformers
+tags:
+- mergekit
+- merge
+license: apache-2.0
+language:
+- zho
+- eng
+- fra
+- spa
+- por
+- deu
+- ita
+- rus
+- jpn
+- kor
+- vie
+- tha
+- ara
+---
+# Re:ultiima-32B
+This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
+## Merge Details
+### Merge Method
+This model was merged using the [TIES](https://arxiv.org/abs/2306.01708) merge method using Qwen/Qwen2.5-32B as a base.
+### Models Merged
+The following models were included in the merge:
+* Qwen/Qwen2.5-32B-Instruct
+### Configuration
+The following YAML configuration was used to produce this model:
+```yaml
+models:
+  - model: Qwen/Qwen2.5-32B-Instruct
+    parameters:
+      weight: 1
+      density: 1
+merge_method: ties
+base_model: Qwen/Qwen2.5-32B
+parameters:
+  weight: 1
+  density: 1
+  normalize: true
+  int8_mask: true
+dtype: float16
 ```