Upload Falcon3-3B-Instruct-RL-CODE-FIX

README.md

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+
+# Falcon3-3B-Instruct-RL-CODE-FIX
+
+This repository hosts the **Falcon3-3B-Instruct-RL-CODE-FIX** model — a fine-tuned version of [Falcon-3B-Instruct](https://huggingface.co/tiiuae/falcon3-3b-instruct), trained using **GRPO (Guided Reinforcement Program Optimization)** to solve programming tasks in the context of automatic program repair.
+
+## 🛠️ Model Purpose
+
+This model is designed to:
+- **Understand buggy code snippets**
+- **Propose test cases that expose the bugs**
+- **Generate fixed versions of the code**
+
+It is particularly useful for:
+- **Code contests**
+- **Automated debugging**
+- **Education and code quality assurance**
+
+## 🧠 Training Details
+
+- **Base model**: Falcon3-3B-Instruct
+- **Method**: GRPO (Guided Reinforcement Program Optimization)
+- **Dataset**: Custom dataset of buggy code + test cases + fixes
+- **Objective**: Improve model reasoning over structured code repair tasks
+
+## 🚀 Usage Example
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+faulty ="""
+def add (x, y):
+    \"\"\"return sum of x and y\"\"\"
+    return x * y 
+"""
+
+
+PROGRAM_REPAIR_TEMPLATE = f"""
+You are an expert in the field of software testing. 
+You are given a buggy Python program, you are supposed to first generate testcases that can expose the bug, 
+and then generate the corresponding fixed code. The two tasks are detailed as follows.
+
+1. **Generate a comprehensive set of test cases to expose the bug**:
+   - Each test case should include an input and the expected output.
+   - Output the test cases as a JSON list, where each entry is a dictionary with keys `"test_input"` and `"test_output"`.
+   - Write in ```json ``` block.
+
+2. **Provide a fixed version**:
+   - Write a correct Python program to fix the bug.
+   - Write in ```python ``` block.
+   - The code should read from standard input and write to standard output, matching the input/output format specified in the problem.
+   
+Here is an example. 
+The faulty Python program is:
+\`\`\`python
+\"\"\"Please write a Python program to sum two integer inputs\"\"\"
+def add (x, y):
+    return x - y 
+x = int(input())
+y = int(input())
+print(add(x,y))
+\`\`\`
+
+Testcases that can expose the bug:
+\`\`\`json
+[
+    {{
+        \"test_input\":\"1\n2\",
+        \"test_output\":\"3\"
+    }},
+    {{
+        \"test_input\":\"-1\n1\",
+        \"test_output\":\"0\"
+    }},
+    {{
+        \"test_input\":\"-1\n2\",
+        \"test_output\":\"1\"
+    }}
+]
+\`\`\`
+
+Fixed code:
+\`\`\`python
+def add (x, y):
+    return x + y 
+x = int(input())
+y = int(input())
+print(add(x,y))
+\`\`\`
+
+Now, you are given a faulty Python function, please return:
+1. **Testcases** that helps expose the bug.
+2. **Fixed code** that can pass all testcases.
+
+The faulty function is:
+\`\`\`python
+{faulty}
+\`\`\`
+<|assistant|>
+"""
+
+model = AutoModelForCausalLM.from_pretrained(
+    "Neo111x/Falcon3-3B-Instruct-RL-CODE-FIX",
+    trust_remote_code=True
+)
+tokenizer = AutoTokenizer.from_pretrained(
+    "Neo111x/Falcon3-3B-Instruct-RL-CODE-FIX",
+    trust_remote_code=True
+)
+
+messages = [
+    {"role": "user", "content": PROGRAM_REPAIR_TEMPLATE}
+]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+)
+model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+generated_ids = model.generate(
+    **model_inputs,
+    max_new_tokens=512
+)
+generated_ids = [
+    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+]
+
+response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+print(response)
+```

all_results.json

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+{
+    "eval_train_loss": 0.1119963601231575,
+    "eval_train_model_preparation_time": 0.0021,
+    "eval_train_runtime": 3743.1417,
+    "eval_train_samples_per_second": 0.099,
+    "eval_train_steps_per_second": 0.013,
+    "total_flos": 0.0,
+    "train_loss": -4.9164727360556425e-09,
+    "train_runtime": 11457.4856,
+    "train_samples": 1473,
+    "train_samples_per_second": 0.129,
+    "train_steps_per_second": 0.129
+}

chat_template.jinja

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+{%- if tools %}
+{{- '<|system|>\n' }}
+{%- if messages[0]['role'] == 'system' %}
+{{- messages[0]['content'] }}
+{%- set remaining_messages = messages[1:] %}
+{%- else %}
+{%- set remaining_messages = messages %}
+{%- endif %}
+{{- 'You are a Falcon assistant skilled in function calling. You are helpful, respectful, and concise.\n\n# Tools\n\nYou have access to the following functions. You MUST use them to answer questions when needed. For each function call, you MUST return a JSON object inside <tool_call></tool_call> tags.\n\n<tools>' + tools|tojson(indent=2) + '</tools>\n\n# Output Format\n\nYour response MUST follow this format when making function calls:\n<tool_call>\n[\n  {"name": "function_name", "arguments": {"arg1": "value1", "arg2": "value2"}},\n  {"name": "another_function", "arguments": {"arg": "value"}}\n]\n</tool_call>\nIf no function calls are needed, respond normally without the tool_call tags.\n' }}
+{%- for message in remaining_messages %}
+{%- if message['role'] == 'user' %}
+{{- '<|user|>\n' + message['content'] + '\n' }}
+{%- elif message['role'] == 'assistant' %}
+{%- if message.content %}
+{{- '<|assistant|>\n' + message['content'] }}
+{%- endif %}
+{%- if message.tool_calls %}
+{{- '\n<tool_call>\n' }}
+{{- message.tool_calls|tojson(indent=2) }}
+{{- '\n</tool_call>' }}
+{%- endif %}
+{{- eos_token + '\n' }}
+{%- elif message['role'] == 'tool' %}
+{{- '<|assistant|>\n<tool_response>\n' + message['content'] + '\n</tool_response>\n' }}
+{%- endif %}
+{%- endfor %}
+{{- '<|assistant|>\n' if add_generation_prompt }}
+{%- else %}
+{%- for message in messages %}
+{%- if message['role'] == 'system' %}
+{{- '<|system|>\n' + message['content'] + '\n' }}
+{%- elif message['role'] == 'user' %}
+{{- '<|user|>\n' + message['content'] + '\n' }}
+{%- elif message['role'] == 'assistant' %}
+{%- if not loop.last %}
+{{- '<|assistant|>\n' + message['content'] + eos_token + '\n' }}
+{%- else %}
+{{- '<|assistant|>\n' + message['content'] + eos_token }}
+{%- endif %}
+{%- endif %}
+{%- if loop.last and add_generation_prompt %}
+{{- '<|assistant|>\n' }}
+{%- endif %}
+{%- endfor %}
+{%- endif %}

checkpoint-1473/chat_template.jinja

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+{%- if tools %}
+{{- '<|system|>\n' }}
+{%- if messages[0]['role'] == 'system' %}
+{{- messages[0]['content'] }}
+{%- set remaining_messages = messages[1:] %}
+{%- else %}
+{%- set remaining_messages = messages %}
+{%- endif %}
+{{- 'You are a Falcon assistant skilled in function calling. You are helpful, respectful, and concise.\n\n# Tools\n\nYou have access to the following functions. You MUST use them to answer questions when needed. For each function call, you MUST return a JSON object inside <tool_call></tool_call> tags.\n\n<tools>' + tools|tojson(indent=2) + '</tools>\n\n# Output Format\n\nYour response MUST follow this format when making function calls:\n<tool_call>\n[\n  {"name": "function_name", "arguments": {"arg1": "value1", "arg2": "value2"}},\n  {"name": "another_function", "arguments": {"arg": "value"}}\n]\n</tool_call>\nIf no function calls are needed, respond normally without the tool_call tags.\n' }}
+{%- for message in remaining_messages %}
+{%- if message['role'] == 'user' %}
+{{- '<|user|>\n' + message['content'] + '\n' }}
+{%- elif message['role'] == 'assistant' %}
+{%- if message.content %}
+{{- '<|assistant|>\n' + message['content'] }}
+{%- endif %}
+{%- if message.tool_calls %}
+{{- '\n<tool_call>\n' }}
+{{- message.tool_calls|tojson(indent=2) }}
+{{- '\n</tool_call>' }}
+{%- endif %}
+{{- eos_token + '\n' }}
+{%- elif message['role'] == 'tool' %}
+{{- '<|assistant|>\n<tool_response>\n' + message['content'] + '\n</tool_response>\n' }}
+{%- endif %}
+{%- endfor %}
+{{- '<|assistant|>\n' if add_generation_prompt }}
+{%- else %}
+{%- for message in messages %}
+{%- if message['role'] == 'system' %}
+{{- '<|system|>\n' + message['content'] + '\n' }}
+{%- elif message['role'] == 'user' %}
+{{- '<|user|>\n' + message['content'] + '\n' }}
+{%- elif message['role'] == 'assistant' %}
+{%- if not loop.last %}
+{{- '<|assistant|>\n' + message['content'] + eos_token + '\n' }}
+{%- else %}
+{{- '<|assistant|>\n' + message['content'] + eos_token }}
+{%- endif %}
+{%- endif %}
+{%- if loop.last and add_generation_prompt %}
+{{- '<|assistant|>\n' }}
+{%- endif %}
+{%- endfor %}
+{%- endif %}

checkpoint-1473/config.json

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+{
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "eos_token_id": 11,
+  "head_dim": 256,
+  "hidden_act": "silu",
+  "hidden_size": 3072,
+  "initializer_range": 0.02,
+  "intermediate_size": 9216,
+  "max_position_embeddings": 32768,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 22,
+  "num_key_value_heads": 4,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 1000042,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.54.1",
+  "use_cache": false,
+  "vocab_size": 131072
+}

checkpoint-1473/generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 11,
+  "eos_token_id": 11,
+  "transformers_version": "4.54.1"
+}

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