Update README.md

README.md

@@ -1,199 +1,133 @@
 ---
 library_name: transformers
-tags: []
+language:
+- en
+base_model:
+- Qwen/Qwen2.5-72B-Instruct
+tags:
+- evaluation
 ---
 
-# Model Card for Model ID
+<div align="center">
 
-<!-- Provide a quick summary of what the model is/does. -->
+# LMUnit: Fine-grained Evaluation with Natural Language Unit Tests
+<img src="Contextual_AI_Brand_Mark_Dark.png" width="10%" alt="Contextual_AI"/>
 
+</div>
 
+<hr>
 
-## Model Details
-
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+<div align="center">
 
-[More Information Needed]
+[![Paper](https://img.shields.io/badge/Paper-LMUnit-blue)](https://arxiv.org/abs/2412.13091)
+[![Blog Post](https://img.shields.io/badge/📝%20Blog-LMUnit-green)](https://contextual.ai/research/lmunit)
+[![GitHub](https://img.shields.io/badge/GitHub-LMUnit-black?logo=github)](https://github.com/ContextualAI/LMUnit)
+[![Hugging Face Collection](https://img.shields.io/badge/🤗%20Hugging%20Face-Model%20Collection-yellow)](https://huggingface.co/collections/ContextualAI/lmunit)
 
-## Evaluation
+</div>
 
-<!-- This section describes the evaluation protocols and provides the results. -->
+**LMUnit** is a state-of-the-art language model that is optimized for evaluating natural language unit tests. It takes three inputs: a prompt, a response, and a unit test. It then produces a continuous score between 1 and 5 where higher scores indicate that the response better satisfies the unit test criteria.
 
-### Testing Data, Factors & Metrics
+The LMUnit model achieves leading averaged performance across preference, direct scoring, and fine-grained unit test evaluation tasks, as measured by FLASK and BiGGen Bench, and performs on par with frontier models for coarse evaluation of long-form responses (per LFQA). The model also demonstrates exceptional alignment with human preferences, ranking in the top 5 of the RewardBench benchmark with 93.5% accuracy and in top #2 of RewardBench2 with 82.1% accuracy.
 
-#### Testing Data
+For more details, please check out the [blogpost](https://contextual.ai/research/lmunit) or the [paper](https://arxiv.org/abs/2412.13091).
 
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
+## Model Details
 
-## Model Card Authors [optional]
+LMUnit is highly performant and versatile because of key methodologies in its training approach:
 
-[More Information Needed]
+- **Multi-Objective Training:** The model simultaneously learns from multiple evaluation signals, including pairwise comparisons between responses, direct quality ratings, and specialized criteria-based judgments.
+- **Synthetic Data Generation:** We developed a sophisticated pipeline to generate training data that captures nuanced, fine-grained evaluation criteria and subtle quality distinctions between responses across a wide range of use cases and scenarios.
+- **Importance Weighting:** We demonstrate that adjusting unit test weights to reflect the relative importance of different criteria achieves results that better align with human preferences.
 
-## Model Card Contact
+### Model Description
 
-[More Information Needed]
+- **Developed by:** Contextual AI
+- **Language(s) (NLP):** English
+- **Finetuned from model:** Qwen2.5-72B
+
+### Model Sources
+
+- **Repository:** https://github.com/ContextualAI/LMUnit
+- **Paper:** https://arxiv.org/abs/2412.13091
+
+## 🚀 Model Quick Start
+
+### Installation
+```bash
+pip install lmunit
+```
+
+### Basic Usage
+```python
+from lmunit import LMUnit
+from vllm import SamplingParams
+
+# Initialize LMUnit
+model = LMUnit(
+    model_path="ContextualAI/LMUnit-qwen2.5-72b",
+    tp_size=4
+)
+
+# Define evaluation
+query = "What is the capital of France?"
+response = "Paris"
+unit_test = "Does the response correctly identify the capital city?"
+
+# Generate score
+sampling_params = SamplingParams(temperature=0.0, max_tokens=10, logprobs=20)
+prompt = f"Query: {query}\n\nResponse: {response}\n\nUnit Test: {unit_test}"
+output = model.generate(prompt, sampling_params)
+print(output)
+```
+
+### Alternative: Using Transformers
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+# Load model
+tokenizer = AutoTokenizer.from_pretrained("ContextualAI/LMUnit-qwen2.5-72b")
+model = AutoModelForCausalLM.from_pretrained("ContextualAI/LMUnit-qwen2.5-72b")
+
+# Prepare prompt
+query = "What is the capital of France?"
+response = "Paris"
+unit_test = "Does the response correctly identify the capital city?"
+content = f"Query: {query}\n\nResponse: {response}\n\nUnit Test: {unit_test}"
+
+messages = [{"role": "user", "content": content}]
+inputs = tokenizer.apply_chat_template(
+    messages,
+    add_generation_prompt=True,
+    tokenize=True,
+    return_dict=True,
+    return_tensors="pt",
+).to(model.device)
+
+# Generate
+outputs = model.generate(**inputs, max_new_tokens=40)
+result = tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])
+print(result)
+```
+
+For more examples, see our [GitHub repository](https://github.com/ContextualAI/LMUnit).
+
+### Evaluation - Results
+
+| Model | Flask | BiGGen-Bench | Human-Internal | InfoBench | RB | LFQA | RB2 |
+|:------|------:|-------------:|---------------:|----------:|----:|------:|----:|
+| **LMUnit-LLaMA-3.1-70B** | 72.03 | 67.69 | 93.63 | 89.00 | 91.56 | 76.15 | 80.5 |
+| **LMUnit-Qwen2.5-72B** | 73.85 | 69.56 | 94.44 | 88.67 | 91.13 | 73.85 | 82.1 |
+
+## Citation
+
+If you find our work helpful, feel free to cite our paper:
+```bibtex
+@inproceedings{saadfalcon2025lmunit,
+      title={{LMUnit}: Fine-grained Evaluation with Natural Language Unit Tests}, 
+      author={Jon Saad-Falcon and Rajan Vivek and William Berrios and Nandita Shankar Naik and Matija Franklin and Bertie Vidgen and Amanpreet Singh and Douwe Kiela and Shikib Mehri},
+      booktitle={Findings of the Association for Computational Linguistics: EMNLP 2025},
+      year={2025},
+      url={https://arxiv.org/abs/2412.13091}
+}
+```