# Cursor Chat: ai-agent-book

## Metadata
- **Project**: ai-agent-book
- **Path**: `/Users/boj`
- **Date**: 2025-10-03 10:48:20
- **Session ID**: `720952d6-c308-49fd-8846-6750df5a9c24`

## Conversation

### 👤 You

return parametrization()
  File "/root/miniconda3/lib/python3.13/site-packages/torch/nn/modules/module.py", line 1773, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
           ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
  File "/root/miniconda3/lib/python3.13/site-packages/torch/nn/modules/module.py", line 1784, in _call_impl
    return forward_call(*args, **kwargs)
  File "/root/miniconda3/lib/python3.13/site-packages/torch/nn/utils/parametrize.py", line 300, in forward
    x = self[0](self.original)
  File "/root/miniconda3/lib/python3.13/site-packages/torch/nn/modules/module.py", line 1773, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
           ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
  File "/root/miniconda3/lib/python3.13/site-packages/torch/nn/modules/module.py", line 1784, in _call_impl
    return forward_call(*args, **kwargs)
  File "/root/miniconda3/lib/python3.13/site-packages/peft/tuners/lora/layer.py", line 1776, in forward
    return W + self.delta_weight
           ~~^~~~~~~~~~~~~~~~~~~
torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 508.00 MiB. GPU 0 has a total capacity of 79.19 GiB of which 89.56 MiB is free. Process 1242509 has 79.08 GiB memory in use. Of the allocated memory 77.69 GiB is allocated by PyTorch, and 674.48 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)

@https://cookbook.openai.com/articles/gpt-oss/fine-tune-transfomers 

When I run trainer.train(), it shows OOM. Why? Before executing the commands, the GPU is free

---

### 🤖 Cursor Assistant

Looking at the error and the [OpenAI cookbook article](https://cookbook.openai.com/articles/gpt-oss/fine-tune-transfomers), the issue is that even though your GPU was free before loading the model, the model loading itself + training overhead is consuming all 79GB of VRAM.

The problem occurs during `trainer.train()` because:

1. **Model is already loaded** (~40-50GB with quantization)
2. **Training requires additional memory** for:
   - Optimizer states (Adam stores 2x parameters)
   - Gradients
   - Activations during forward/backward pass
   - Batch data

Here are solutions to try, in order of effectiveness:

## 1. **Reduce Batch Size and Sequence Length**

```python
training_args = SFTConfig(
    learning_rate=2e-4,
    gradient_checkpointing=True,
    num_train_epochs=1,
    logging_steps=1,
    per_device_train_batch_size=1,  # Reduce from 4 to 1
    gradient_accumulation_steps=16,  # Increase from 4 to 16 to maintain effective batch size
    max_length=1024,  # Reduce from 2048 to 1024
    warmup_ratio=0.03,
    lr_scheduler_type="cosine_with_min_lr",
    lr_scheduler_kwargs={"min_lr_rate": 0.1},
    output_dir="gpt-oss-20b-multilingual-reasoner",
    report_to="trackio",
    push_to_hub=True,
)
```

## 2. **Use More Aggressive Gradient Checkpointing**

```python
training_args = SFTConfig(
    # ... other args ...
    gradient_checkpointing=True,
    gradient_checkpointing_kwargs={"use_reentrant": False},  # More memory efficient
)
```

## 3. **Use 8-bit Optimizer (if available)**

```python
training_args = SFTConfig(
    # ... other args ...
    optim="adamw_8bit",  # Use 8-bit optimizer to reduce memory
)
```

## 4. **Try the PyTorch Memory Fragmentation Fix**

Before running your script, set the environment variable:

```bash
export PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True
```

## 5. **Verify Quantization is Working**

Make sure your model is properly quantized. Check after loading:

```python
model = AutoModelForCausalLM.from_pretrained("openai/gpt-oss-20b", **model_kwargs)
print(f"Model dtype: {model.dtype}")
print(f"Model device: {model.device}")
# Should show mixed precision/quantized weights
```

## 6. **Most Aggressive: Minimal Training Config**

---

### 🤖 Cursor Assistant

If none of the above work, try this minimal configuration:

```python
training_args = SFTConfig(
    learning_rate=2e-4,
    gradient_checkpointing=True,
    gradient_checkpointing_kwargs={"use_reentrant": False},
    num_train_epochs=1,
    logging_steps=1,
    per_device_train_batch_size=1,
    gradient_accumulation_steps=32,  # Very high to compensate for batch_size=1
    max_length=512,  # Very short sequences
    warmup_ratio=0.03,
    lr_scheduler_type="cosine_with_min_lr",
    lr_scheduler_kwargs={"min_lr_rate": 0.1},
    optim="adamw_8bit",
    output_dir="gpt-oss-20b-multilingual-reasoner",
    report_to="trackio",
    push_to_hub=True,
)
```

**Note**: The cookbook mentions this requires an **H100 with 80GB**. If you have a different GPU (A100, V100, etc.), you may need even more aggressive settings or consider using a smaller model variant if available.

---

### 👤 You

Peak reserved memory % of max memory = 24.44 %.
Peak reserved memory for training % of max memory = 0.0 %.
<|start|>system<|message|>You are ChatGPT, a large language model trained by OpenAI.
Knowledge cutoff: 2024-06
Current date: 2025-10-03

Reasoning: medium

# Valid channels: analysis, commentary, final. Channel must be included for every message.
Calls to these tools must go to the commentary channel: 'functions'.<|end|><|start|>developer<|message|># Instructions

reasoning language: French

You are a helpful assistant that can solve mathematical problems.<|end|><|start|>user<|message|>Solve x^5 + 3x^4 - 10 = 3.<|end|><|start|>assistant<|channel|>analysis<|message|>The user says: Solve x^5 + 3x^4 - 10
= 3. Probably find real roots. Let's parse: x^5 + 3x^4 - 10 = 3 => x^5 + 3x^4 - 13 = 0.
<|start|>system<|message|>You are ChatGPT, a large language model trained by OpenAI.
Knowledge cutoff: 2024-06
Current date: 2025-10-03

Reasoning: high

# Valid channels: analysis, commentary, final. Channel must be included for every message.
Calls to these tools must go to the commentary channel: 'functions'.<|end|><|start|>developer<|message|># Instructions

reasoning language: French

You are a helpful assistant that can solve mathematical problems.<|end|><|start|>user<|message|>Solve x^5 + 3x^4 - 10 = 3.<|end|><|start|>assistant<|channel|>analysis<|message|>We need to solve x^5 + 3x^4 - 10 = 3.
 So we set x^5 + 3x^4 - 10 = 3 => x^5 + 3x^4 - 13 = 0. Solve for x? Might have

--
Does reasoning language work?

---

### 🤖 Cursor Assistant

Let me check your fine-tuning script to understand what's happening:

---

### 🤖 Cursor Assistant

Based on the output you're showing, I can see what's happening. The text you're seeing is **NOT the model generating** - it's just **the training dataset being printed** (line 177 in your script: `print(dataset[0]['text'])`).

---

### 🤖 Cursor Assistant

## Understanding What You're Seeing

The output you showed me has **two parts**:

### 1. **Memory Statistics** (Before Training)
```
Peak reserved memory % of max memory = 24.44 %.
Peak reserved memory for training % of max memory = 0.0 %.
```

This "0.0%" means **training hasn't started yet** when these stats were printed. This is printed at line 232 (the "Show current memory stats" section).

### 2. **Dataset Examples** (Input Data, NOT Model Output)
The text showing reasoning in English is just the **training data being printed** (line 177: `print(dataset[0]['text'])`). This is what the model will **learn from**, not what it's generating.

## To Test If Multilingual Reasoning Works

You need to:

1. **Complete the training** (line 236: `trainer.train()`)
2. **Then test inference** using the code at lines 257-269:

```python
# After training completes, run this:
messages = [
    {"role": "system", "content": "reasoning language: French\n\nYou are a helpful assistant that can solve mathematical problems."},
    {"role": "user", "content": "Solve x^5 + 3x^4 - 10 = 3."},
]
inputs = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt = True,
    return_tensors = "pt",
    return_dict = True,
    reasoning_effort = "medium",
).to("cuda")
from transformers import TextStreamer
_ = model.generate(**inputs, max_new_tokens = 256, streamer = TextStreamer(tokenizer))  # Increase max_new_tokens
```

**Only then** will you see if the model actually reasons in French!

## Expected Training Output

After `trainer.train()` completes, you should see:
- Training loss decreasing over ~30 steps
- Final memory statistics showing actual training memory usage
- Then you can test if French reasoning works

Did your training complete successfully? Can you share the output after the `trainer.train()` line?

---


*Exported from [Cursor View](https://github.com/saharmor/cursor-view)*