Instructions to use thirdeyeai/elevate360m-orca with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use thirdeyeai/elevate360m-orca with Transformers:

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

pipe = pipeline("text-generation", model="thirdeyeai/elevate360m-orca")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("thirdeyeai/elevate360m-orca")
model = AutoModelForCausalLM.from_pretrained("thirdeyeai/elevate360m-orca")
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 thirdeyeai/elevate360m-orca with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "thirdeyeai/elevate360m-orca"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "thirdeyeai/elevate360m-orca",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/thirdeyeai/elevate360m-orca

SGLang

How to use thirdeyeai/elevate360m-orca 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 "thirdeyeai/elevate360m-orca" \
    --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": "thirdeyeai/elevate360m-orca",
		"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 "thirdeyeai/elevate360m-orca" \
        --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": "thirdeyeai/elevate360m-orca",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use thirdeyeai/elevate360m-orca with Docker Model Runner:
```
docker model run hf.co/thirdeyeai/elevate360m-orca
```

sloshywings commited on Apr 4, 2025

Commit

0fe5bd3

verified ·

1 Parent(s): bf11c3c

Update README.md

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README.md +11 -8

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@@ -1,11 +1,16 @@
 ---
 library_name: transformers
 tags:
-  - chat
-  - tool-calling
-  - instruction-tuned
-  - edge-device
-  - conversational
 ---
 # Model Card for `thirdeyeai/elevate-360m`
@@ -44,6 +49,4 @@ Trained on publicly available instruction-following datasets. May reflect biases
 ### Recommendations
-Use only with proper evaluation and safety checks in deployment environments. Validate outputs before taking action.

 ---
 library_name: transformers
 tags:
+- chat
+- tool-calling
+- instruction-tuned
+- edge-device
+- conversational
+license: mit
+datasets:
+- Open-Orca/OpenOrca
+base_model:
+- HuggingFaceTB/SmolLM2-360M-Instruct
 ---
 # Model Card for `thirdeyeai/elevate-360m`
 ### Recommendations
+Use only with proper evaluation and safety checks in deployment environments. Validate outputs before taking action.