Improve model card

README.md

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 ---
 library_name: transformers
+pipeline_tag: text-generation
+license: mit # Please verify license in repository
 tags:
 - unsloth
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
+# Model Card for LocAgent - Qwen2.5-Coder-Instruct-32B
 
+This model is a fine-tuned version of Qwen-2.5-Coder-Instruct-32B designed for code localization, as described in the paper [LocAgent: Graph-Guided LLM Agents for Code Localization](https://huggingface.co/papers/2503.09089). LocAgent leverages graph-based code representation to enhance the accuracy of identifying code sections relevant to natural language problem descriptions.
 
 
 ## 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.
+LocAgent uses a graph-based representation of codebases (files, classes, functions, and their dependencies) to enable efficient code localization.  This allows LLMs to reason across hierarchical structures and dependencies to identify relevant code sections for changes.
 
-- **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]
+- **Developed by:** The Gerstein Lab
+- **Model type:** Code LLM
+- **Language(s) (NLP):** English (primarily, depending on the codebase)
+- **License:** MIT (Please verify in repository LICENSE file)
+- **Finetuned from model:** Qwen-2.5-Coder-Instruct-32B
 
-### Model Sources [optional]
+### Model Sources
 
-<!-- Provide the basic links for the model. -->
+- **Repository:** [https://huggingface.co/czlll/Qwen2.5-Coder-32B-CL](https://huggingface.co/czlll/Qwen2.5-Coder-32B-CL)
+- **Paper:** [https://huggingface.co/papers/2503.09089](https://huggingface.co/papers/2503.09089)
+- **Code:** [https://github.com/gersteinlab/LocAgent](https://github.com/gersteinlab/LocAgent)
 
-- **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]
+LocAgent can be used directly to identify relevant code sections within a codebase given a natural language description of the problem.  The model requires a graph representation of the codebase as input.
 
-### Out-of-Scope Use
+### Downstream Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+The fine-tuned LocAgent model can be integrated into IDEs or other software development tools to assist developers in code localization tasks.
 
-[More Information Needed]
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
+LocAgent's performance is dependent on the quality of the codebase's graph representation.  Inaccurate or incomplete graphs can lead to inaccurate localization.  The model's performance may also vary depending on the complexity and size of the codebase and the clarity of the natural language description.  Further, the model inherits biases present in the training data.
 
 ### Recommendations
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+Carefully construct the codebase graph representation.  Provide clear and concise natural language descriptions of the problem. Be aware of potential biases in the model's output.
 
-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. -->
+The following code snippet demonstrates how to use the LocAgent model (replace placeholders with actual paths and adapt for specific model size):
 
-[More Information Needed]
+```python
+# Requires installation of necessary libraries (see Setup section in README)
+from transformers import AutoTokenizer, AutoModelForCausalLM  # Assuming Transformers compatibility
 
-### Training Procedure
+model_id = "czlll/Qwen2.5-Coder-7B-CL" # Replace with the actual model ID, e.g., "czlll/Qwen2.5-Coder-32B-CL"
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id)
 
-#### Preprocessing [optional]
+# ... (Code to load codebase graph and formulate prompt based on natural language description) ...
 
-[More Information Needed]
+inputs = tokenizer(prompt, return_tensors="pt") # Replace 'prompt' with your formatted prompt
+outputs = model.generate(**inputs)
 
+# ... (Code to process model output and identify relevant code sections) ...
+```
 
-#### 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 -->
+## Training Details
 
-#### Speeds, Sizes, Times [optional]
+This section would be populated with details from the training procedure described in the paper and the Github README.  It would include information about the datasets, preprocessing steps, hyperparameters, and training infrastructure.
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
 
-[More Information Needed]
 
 ## Evaluation
 
-<!-- This section describes the evaluation protocols and provides the results. -->
-
 ### Testing Data, Factors & Metrics
 
-#### Testing Data
-
-<!-- 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]
+This section would describe the evaluation datasets used (like Loc-Bench), factors considered (e.g., codebase size, problem complexity), and evaluation metrics (accuracy, Pass@10).
 
 ### Results
 
-[More Information Needed]
-
-#### Summary
-
+This section would detail the results obtained on the Loc-Bench benchmark, comparing LocAgent's performance with other state-of-the-art models (as described in the paper).
 
 
-## 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. -->
+## Citation
 
 **BibTeX:**
 
-[More Information Needed]
+```bibtex
+@article{chen2025locagent,
+  title={LocAgent: Graph-Guided LLM Agents for Code Localization},
+  author={Chen, Zhaoling and Tang, Xiangru and Deng, Gangda and Wu, Fang and Wu, Jialong and Jiang, Zhiwei and Prasanna, Viktor and Cohan, Arman and Wang, Xingyao},
+  journal={arXiv preprint arXiv:2503.09089},
+  year={2025}
+}
+```
 
 **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 Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]
+Chen, Z., Tang, X., Deng, G., Wu, F., Wu, J., Jiang, Z., Prasanna, V., Cohan, A., & Wang, X. (2025). *LocAgent: Graph-Guided LLM Agents for Code Localization*. arXiv preprint arXiv:2503.09089.