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

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.