omniomni
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omni-0-mini-preview

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 ---
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 ---
+license: apache-2.0
+pipeline_tag: text-generation
 library_name: transformers
+tags:
+- vllm
+- stem
+language:
+- en
+base_model:
+- HuggingFaceTB/SmolLM2-1.7B-Instruct
 ---
+<p align="center">
+  <img alt="Omni 0 preview models header" src="https://github.com/omniomni-ai/omni-0-preview-models/raw/refs/heads/main/omni-0-preview-models-image.png">
+</p>
+<p align="center">
+  <a href="https://github.com/omniomni-ai"><strong>GitHub</strong></a> &nbsp
+   <a href="https://omniomni.framer.website/"><strong>Website</strong></a> &nbsp
+    <strong>Paper (Coming Soon)</strong>
+</p>
+<br>
+# Omni 0 Preview Models
+[Omni-0-Mini-Preview (Omni)](https://huggingface.co/omniomni/omni-0-mini-preview) is an 1.7-billion parameter model merged between 4 expert LLMs, each finetuned for expertise in STEM subfields: [Science Expert](https://huggingface.co/omniomni/omni-0-science-preview),  [Technology Expert](https://huggingface.co/omniomni/omni-0-technology-preview), [Engineering Expert](https://huggingface.co/omniomni/omni-0-engineering-preview),  [Math Expert](https://huggingface.co/omniomni/omni-0-math-preview). Through DARE-TIES merging, Omni is able to achieve state-of-the-art efficiency and domain benchmark results among alternative optimization techniques, as well as optimal compute-knowledge efficiency across similar models.
+<p align="center">
+  <figure>
+  <img src="
+https://github.com/omniomni-ai/omni-0-preview-models/raw/refs/heads/main/compute-knowledge-efficiency-plot.png"
+       alt="Omni compute-knowledge efficiency plot">
+  <figcaption>Omni achieves optimal compute-knowledge efficiency compared to alternative models</figcaption>
+</figure>
+</p>
+# Models
+Omni comes in a total of 5 models:
+**Merged Model**
+- `omni-0-mini-preview` - Merged output of all four experts through DARE-TIES, delivering large improvements in performance in STEM domains compared to its base.
+**Experts**
+- `omni-0-science-preview` - Science expert finetuned on corpora of scientific wikipedia texts and academic papers, as well as a chat-templated scientific Q&A dataset
+- `omni-0-technology-preview` - Technology expert finetuned on chat-templated code generation data and stack exchange questions and top-voted answers
+- `omni-0-engineering-preview` - Engineering expert finetuned on corpora of engineering-related wikipedia texts and academic papers
+- `omni-0-math-preview` - Math expert finetuned on chat-templated math Q&A data
+All Omni experts are finetuned from their base model: [SmolLM2 1.7B Instruct](https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B-Instruct) on H100/Ada 6000/A100 GPUs, improving by 25.32% on average over all tested STEM benchmarks.
+> This model card is for [omni-0-mini-preview](https://huggingface.co/omniomni/omni-0-mini-preview). All other models can be found on [Omni's HuggingFace page](https://huggingface.co/omniomni)
+# Features
+**Made for all** Omni is a series of highly efficient Large Language Models aimed at expanding the accessibility of AI fill in the gaps left behind by technological advancement.
+**Efficient** Omni operates at optimal compute-knowledge efficiency compared to similar models.
+**Merged architecture** Omni uses merging to provide the collective accuracy of specialized models, leveraging their capabilities to collectively enhance the final merged model.
+**Multi-disciplinary** Omni's first variant achieves state-of-the-art performance across STEM compared to alternative optimization techniques.
+---
+# Inference
+## Transformers
+Transformers is a framework by HuggingFace unifying model development and inference, allowing for simple and seamless interactions with models found on HuggingFace.
+To get started with running inference using Omni, install transformers
+```bash
+pip install transformers
+```
+After transformers has been installed, run the following code to generate outputs from any Omni model
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+checkpoint = "omniomni/omni-0-mini-preview" # Can be any Omni model
+device = "cuda" # For GPU usage or "cpu" for CPU usage
+tokenizer = AutoTokenizer.from_pretrained(checkpoint)
+# For multiple GPUs install accelerate and do `model = AutoModelForCausalLM.from_pretrained(checkpoint, device_map="auto")`
+# However, Omni is small enough to run on individual commodity GPUs and low-resource devices
+model = AutoModelForCausalLM.from_pretrained(checkpoint).to(device)
+messages = [{"role": "user", "content": "What is STEM?"}]
+input_text=tokenizer.apply_chat_template(messages, tokenize=False)
+inputs = tokenizer.encode(input_text, return_tensors="pt").to(device)
+outputs = model.generate(inputs, max_new_tokens=256, temperature=0.7, do_sample=True)
+print(tokenizer.decode(outputs[0]))
+```
+## vLLM
+vLLM provides fast LLM inference to models spanning vast amounts of architectures through both server and in-file implementations.
+First, install vLLM's package
+```bash
+uv pip install vllm --torch-backend=auto
+```
+After that, run this command to start a server with Omni via vLLM
+```bash
+vllm  serve  omniomni/omni-0-mini-preview
+```
+To use Omni with vLLM without creating a server, run this code to generate outputs within a Python file
+```python
+# vLLM automatically uses a GPU unless built with CPU wheels, so no need to specify a device
+from vllm import LLM, SamplingParams
+prompts = [
+    "Hello, my name is",
+    "The president of the United States is",
+    "The capital of France is",
+    "The future of AI is",
+]
+sampling_params = SamplingParams(temperature=0.7)
+llm = LLM(model="omniomni/omni-0-mini-preview")
+outputs = llm.generate(prompts, sampling_params)
+for output in outputs:
+    prompt = output.prompt
+    generated_text = output.outputs[0].text
+    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
+```