Improve model card: Add library_name, update paper link, and expand usage info

This PR enhances the model card for `Soar-qwen-7b` by:
- Adding `library_name: transformers` to the metadata, enabling the "Use in Transformers" widget and improving discoverability.
- Updating the paper link in both the header and the citation block to the official Hugging Face Papers page (`https://huggingface.co/papers/2507.14172`), providing a consistent and preferred link within the Hugging Face ecosystem.
- Including a direct Python code example for model inference using the `transformers` library in the "How to Use the Model" section, offering a quick start for users.
- Integrating detailed `conda` installation instructions and guidance on running SOAR directly from the project's GitHub README, making the model card a more comprehensive resource for setup and operation.

These updates improve the model's documentation, making it easier for researchers and developers to utilize and understand the SOAR framework and its models.

README.md

@@ -1,9 +1,9 @@
 ---
-license: apache-2.0
-datasets:
-- julien31/soar_arc_train_5M
 base_model:
 - Qwen/Qwen2.5-Coder-7B-Instruct
+datasets:
+- julien31/soar_arc_train_5M
+license: apache-2.0
 pipeline_tag: text-generation
 tags:
 - text-generation
@@ -13,16 +13,18 @@ tags:
 - arc
 - arc-agi
 - soar
+library_name: transformers
 ---
+
 # SOAR-ARC Models: Self-Improving Language Models for Program Synthesis
 
 <p align="center">
-          🤗 <a href="https://huggingface.co/collections/julien31/soar-arc-6856d27681fce01d9af4c4a3">Hugging Face (data and model)</a>&nbsp&nbsp | &nbsp&nbsp 📑 <a href="https://icml.cc/virtual/2025/poster/43499">Paper</a> &nbsp&nbsp | &nbsp&nbsp 📑 <a href="https://julienp.netlify.app/posts/soar/">Blog</a>
+          🤗 <a href="https://huggingface.co/collections/julien31/soar-arc-6856d27681fce01d9af4c4a3">Hugging Face (data and model)</a>&nbsp&nbsp | &nbsp&nbsp 📑 <a href="https://huggingface.co/papers/2507.14172">Paper</a> &nbsp&nbsp | &nbsp&nbsp 📑 <a href="https://julienp.netlify.app/posts/soar/">Blog</a>
 </p>
 
 This repository contains one of the models fine-tuned using the **SOAR** (**S**elf-improving **O**perators for **A**utomated program **R**efinements) framework, as presented in the paper:
 
-> [**Self-Improving Language Models for Evolutionary Program Synthesis: A Case Study on ARC-AGI**](https://icml.cc/virtual/2025/poster/43499)
+> [**Self-Improving Language Models for Evolutionary Program Synthesis: A Case Study on ARC-AGI**](https://huggingface.co/papers/2507.14172)
 >
 > Julien Pourcel, Cédric Colas, Pierre-Yves Oudeyer.
 > *Proceedings of the 42nd International Conference on Machine Learning (ICML), 2025.*
@@ -64,9 +66,95 @@ This process creates a powerful feedback loop: the fine-tuned model becomes bett
 
 The primary use of this model is to generate a Python function that solves an ARC task. The input to the model should be a formatted prompt containing the training and test examples of the ARC task.
 
+Here's a quick example to get started:
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+model_id = "julien31/Soar-qwen-7b" # or any other Soar-qwen model
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16, # Use torch.float16 for GPUs that don't support bfloat16
+    device_map="auto",
+)
+
+prompt = "def solve_arc_task(input_grid, output_grid):\
+    \\\"\\\"\\\"Given an ARC-AGI task, transform the input grid to the output grid by applying a series of operations.\
+    \\\"\\\"\\\""
+
+messages = [
+    {"role": "user", "content": prompt}
+]
+
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+
+model_inputs = tokenizer(text, return_tensors="pt").to(model.device)
+
+generated_ids = model.generate(
+    model_inputs.input_ids,
+    max_new_tokens=256,
+    do_sample=True,
+    temperature=0.7,
+    top_p=0.8,
+    repetition_penalty=1.1,
+    eos_token_id=tokenizer.eos_token_id,
+    pad_token_id=tokenizer.pad_token_id, # This is often the same as eos_token_id for Qwen models
+)
+
+# Decode only the newly generated text
+decoded_output = tokenizer.decode(generated_ids[0, model_inputs.input_ids.shape[1]:], skip_special_tokens=True)
+print(decoded_output)
+```
+
 For a complete, end-to-end example of how to format the prompt, run inference, execute the generated code, and visualize the results, please refer to the official repository and notebook:
 
 *   **Official SOAR GitHub Repository**: [https://github.com/flowersteam/SOAR](https://github.com/flowersteam/SOAR)
 *   **Inference & Visualization Notebook**: [https://github.com/flowersteam/SOAR/blob/main/notebook/inference_visualisation.ipynb](https://github.com/flowersteam/SOAR/blob/main/notebook/inference_visualisation.ipynb)
 
-<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/made with unsloth.png" width="20%" />
+<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/made with unsloth.png" width="20%" />
+
+## Installation
+
+### Conda inference environment
+```
+pip install --upgrade pip
+
+git clone https://github.com/flowersteam/SOAR
+cd SOAR
+conda create --name sglang47 \
+    python=3.11 \
+    -y
+conda activate sglang47
+
+pip install "sglang[all]>=0.4.7"
+
+pip install -e .
+pip install -r requirements
+
+```
+
+### Conda training environment
+```
+conda create --name unsloth_env \
+    python=3.11 \
+    pytorch-cuda=12.1 \
+    pytorch cudatoolkit xformers -c pytorch -c nvidia -c xformers \
+    -y
+conda activate unsloth_env
+
+pip install unsloth
+cd SOAR
+pip install -e .
+pip install -r requirements.txt
+```
+
+## Run SOAR
+To run SOAR, please refer to execution instructions located in the experience folder. 
+
+For simple instructions on running sampling and refinement with SOAR, as well as exploring the dataset, please see the Jupyter notebooks provided in the `notebook` folder. These notebooks walk through the basic SOAR step, including how to generate candidate solutions, perform refinement, and analyze results. This hands-on guide will help you get started quickly and understand each step of the SOAR process.