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
```

Model Card for `thirdeyeai/elevate-360m`

Model Summary

360M parameter transformer model trained for efficient chat completion and tool call prediction on edge devices. Suitable for low-latency applications.

Model Details

Developed by: Thirdeye AI
Finetuned from model: HuggingFaceTB/SmolLM2-360M-Instruct
Model type: Causal decoder-only transformer
Language(s): English
License: apache-2.0
Hardware: Trained on 1x A100 GPU
Training time: < 24 hours

Model Sources

Repository: https://huggingface.co/thirdeyeai/elevate-360m

Uses

Direct Use

Primarily for chat completion and tool call prediction in edge environments with constrained resources.

Out-of-Scope Use

Not optimized for multi-language support, long-context reasoning, or open-ended generation without tool grounding.

Bias, Risks, and Limitations

Trained on publicly available instruction-following datasets. May reflect biases present in those datasets. Not suitable for high-stakes or safety-critical applications.

Recommendations

Use only with proper evaluation and safety checks in deployment environments. Validate outputs before taking action.

Downloads last month: 4

Safetensors

Model size

0.4B params

Tensor type

F32

Model tree for thirdeyeai/elevate360m-orca

Base model

HuggingFaceTB/SmolLM2-360M

Quantized

HuggingFaceTB/SmolLM2-360M-Instruct

Finetuned

(143)

this model

thirdeyeai
/

elevate360m-orca

Model Card for `thirdeyeai/elevate-360m`

Model Summary

Model Details

Model Sources

Uses

Direct Use

Out-of-Scope Use

Bias, Risks, and Limitations

Recommendations

Model tree for thirdeyeai/elevate360m-orca

Dataset used to train thirdeyeai/elevate360m-orca

Model Card for thirdeyeai/elevate-360m

Model Summary

Model Details

Model Sources

Uses

Direct Use

Out-of-Scope Use

Bias, Risks, and Limitations

Recommendations

Model tree for thirdeyeai/elevate360m-orca

Dataset used to train thirdeyeai/elevate360m-orca

Model Card for `thirdeyeai/elevate-360m`