Text Generation
Transformers
Safetensors
llama
chat
tool-calling
instruction-tuned
edge-device
conversational
text-generation-inference
# 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]:]))Quick Links
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.
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Model tree for thirdeyeai/elevate360m-orca
Base model
HuggingFaceTB/SmolLM2-360M Quantized
HuggingFaceTB/SmolLM2-360M-Instruct
# 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)