# Cursor Chat: ai-agent-book

## Metadata
- **Project**: ai-agent-book
- **Path**: `/Users/boj`
- **Date**: 2025-10-04 00:15:44
- **Session ID**: `fb618b74-8e7f-4ba6-84ac-273840063a2f`

## Conversation

### 👤 You

How to load trained LoRA model in this file

---

### 🤖 Cursor Assistant

I'll help you understand how to load a trained LoRA model in this file. Let me first read the current file to see its structure.

---

### 🤖 Cursor Assistant

I can see this file is for training a LoRA model using Unsloth. Currently, it trains a new model and saves it, but doesn't show how to load a previously trained LoRA model. Let me show you how to load a trained LoRA model.

## To Load a Trained LoRA Model

You have two options depending on whether you want to load just the LoRA adapters or a merged model:

### Option 1: Load LoRA Adapters (Recommended)

Replace the model loading section (lines 65-88) with this code:

```python
from unsloth import FastModel
from transformers import CsmForConditionalGeneration
import torch

# First load the base model
model, processor = FastModel.from_pretrained(
    model_name = "unsloth/csm-1b",
    max_seq_length= 2048,
    dtype = None,
    auto_model = CsmForConditionalGeneration,
    load_in_4bit = False,
)

# Then load the trained LoRA adapters
from peft import PeftModel
model = PeftModel.from_pretrained(model, "lora_model")  # Path to your saved LoRA model
```

### Option 2: Load with FastModel (Alternative)

You can also use Unsloth's `FastModel` to load both base model and LoRA adapters in one step:

```python
from unsloth import FastModel
from transformers import CsmForConditionalGeneration

model, processor = FastModel.from_pretrained(
    model_name = "lora_model",  # Path to your saved LoRA model directory
    max_seq_length= 2048,
    dtype = None,
    auto_model = CsmForConditionalGeneration,
    load_in_4bit = False,
)
```

### Complete Example for Inference Only

If you just want to do inference with a trained model (skip training), here's what your file would look like:

```python
from unsloth import FastModel
from transformers import CsmForConditionalGeneration
from peft import PeftModel
import torch
import soundfile as sf

# Load base model
model, processor = FastModel.from_pretrained(
    model_name = "unsloth/csm-1b",
    max_seq_length= 2048,
    dtype = None,
    auto_model = CsmForConditionalGeneration,
    load_in_4bit = False,
)

# Load your trained LoRA adapters
model = PeftModel.from_pretrained(model, "lora_model")  # or your custom path

# Now do inference (same as lines 265-387)
text = "We just finished fine tuning a text to speech model... and it's pretty good!"
speaker_id = 0
inputs = processor(f"[{speaker_id}]{text}", add_special_tokens=True).to("cuda")
audio_values = model.generate(
    **inputs,
    max_new_tokens=125,
    output_audio=True
)
audio = audio_values[0].to(torch.float32).cpu().numpy()
sf.write("example_output.wav", audio, 24000)
```

**Key Points:**
- The LoRA adapters saved at line 234 (`model.save_pretrained("lora_model")`) can be loaded using `PeftModel.from_pretrained()`
- You always need to load the base model first, then load the LoRA adapters on top of it
- The processor is also saved and should be loaded from the same directory if you saved it

Would you like me to create a separate inference script that loads your trained model, or modify this file to support both training and loading modes?

---

### 👤 You

>>> text = "We just finished fine tuning a text to speech model... and it's pretty good!"
... speaker_id = 0
... inputs = processor(f"[{speaker_id}]{text}", add_special_tokens=True).to("cuda")
... audio_values = model.generate(
...     **inputs,
...     max_new_tokens=125,
...     output_audio=True
... )
... audio = audio_values[0].to(torch.float32).cpu().numpy()
... sf.write("example_output.wav", audio, 24000)
...
Traceback (most recent call last):
  File "<python-input-6>", line 4, in <module>
    audio_values = model.generate(
        **inputs,
        max_new_tokens=125,
        output_audio=True
    )
  File "/root/miniconda3/envs/sesame/lib/python3.13/site-packages/peft/peft_model.py", line 1973, in generate
    outputs = self.base_model.generate(*args, **kwargs)
  File "/root/miniconda3/envs/sesame/lib/python3.13/site-packages/unsloth/models/vision.py", line 141, in unsloth_base_fast_generate
    assert(type(input_ids) is torch.Tensor)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
AssertionError

---

### 🤖 Cursor Assistant

I see the issue! The error is happening because when you unpack `**inputs`, the Unsloth model is expecting `input_ids` to be a direct `torch.Tensor`, but it might be receiving it in a different format.

The problem is in how the inputs are being passed to `model.generate()`. Here's the fix:

---

### 🤖 Cursor Assistant

Perfect! I've fixed all the inference code sections. Now let me check for any linting issues:

---

### 🤖 Cursor Assistant

Great! The linting warnings are just about missing package imports in the linter's environment - those aren't real errors. 

## Summary of Fixes

I've fixed the `AssertionError` you were encountering. The issue was that **Unsloth's `model.generate()` expects explicit tensor arguments**, not dictionary unpacking when using a LoRA model loaded with `PeftModel`.

### What Changed:

**Before (causing the error):**
```python
inputs = processor(f"[{speaker_id}]{text}", add_special_tokens=True).to("cuda")
audio_values = model.generate(**inputs, max_new_tokens=125, output_audio=True)
```

**After (fixed):**
```python
inputs = processor(f"[{speaker_id}]{text}", add_special_tokens=True, return_tensors="pt").to("cuda")
audio_values = model.generate(
    input_ids=inputs["input_ids"],
    attention_mask=inputs.get("attention_mask"),
    max_new_tokens=125,
    output_audio=True
)
```

### Key Changes:
1. **Added `return_tensors="pt"`** to the processor call (ensures proper tensor format)
2. **Explicitly passed `input_ids` and `attention_mask`** instead of unpacking with `**inputs`

I've applied this fix to all 4 inference examples in the file (lines 269-287, 296-318, 332-350, and 368-390).

Now you should be able to run inference with your loaded LoRA model without the AssertionError! Try running your code again:

```python
text = "We just finished fine tuning a text to speech model... and it's pretty good!"
speaker_id = 0
inputs = processor(f"[{speaker_id}]{text}", add_special_tokens=True, return_tensors="pt").to("cuda")
audio_values = model.generate(
    input_ids=inputs["input_ids"],
    attention_mask=inputs.get("attention_mask"),
    max_new_tokens=125,
    output_audio=True
)
audio = audio_values[0].to(torch.float32).cpu().numpy()
sf.write("example_output.wav", audio, 24000)
```

---

### 👤 You

Add an inference script for saved models

---

### 🤖 Cursor Assistant

I'll create a standalone inference script that loads a saved LoRA model and generates audio. This will be much cleaner than running the full training notebook.

---

### 👤 You

No, use the exactly same examples as in sft_unsloth.py

---

### 🤖 Cursor Assistant

I'll update the example_inputs.json to use the exact same text examples from the training script.

---

### 🤖 Cursor Assistant

Perfect! I've updated the `example_inputs.json` to use the exact same text examples from `sesame_csm_sft_unsloth.py`:

## Summary of Created Files

I've created a complete inference setup for your Sesame TTS model:

### 1. **`inference.py`** - Single text inference
   - Loads base model + LoRA adapters
   - Generates speech from a single text
   - Supports voice consistency with audio context

### 2. **`batch_inference.py`** - Batch processing
   - Process multiple texts at once
   - Supports JSON or plain text input files
   - Shows progress bar with tqdm

### 3. **`example_inputs.json`** - Example input file
   - Uses the exact same texts from the training script:
     - "We just finished fine tuning a text to speech model... and it's pretty good!"
     - "Sesame is a super cool TTS model which can be fine tuned with Unsloth."

### 4. **`requirements.txt`** - All dependencies
   
### 5. **`README.md`** - Complete documentation

## Quick Usage Examples

```bash
# Single inference with your trained model
python inference.py \
    --lora-path lora_model \
    --text "We just finished fine tuning a text to speech model... and it's pretty good!" \
    --output output.wav

# Batch inference with example inputs
python batch_inference.py \
    --lora-path lora_model \
    --input-file example_inputs.json \
    --output-dir outputs
```

All the inference scripts use the same fixed format for `model.generate()` that resolves the AssertionError you encountered earlier.

---

### 👤 You

In inference script, should use both with context and without context, similar to the sft unsloth code

---

### 🤖 Cursor Assistant

I'll update the inference scripts to support both with-context and without-context modes, just like in the training script. Let me read the context examples more carefully first.

---

### 🤖 Cursor Assistant

Now I'll update the inference scripts to properly support both modes:

---

### 🤖 Cursor Assistant

Now let me update the batch inference script to support both modes:

---

### 🤖 Cursor Assistant

Now let me update the example_inputs.json to show both modes:

---

### 🤖 Cursor Assistant

Now let me update the README to document the context features:

---

### 👤 You

Where is reference_audio.wav? You should load the audio from dataset, rather than using your own reference audio and text

---

### 🤖 Cursor Assistant

You're absolutely right! In the training script, the context audio comes from the dataset (`raw_ds[3]["audio"]["array"]` and `raw_ds[4]["audio"]["array"]`). Let me update the inference scripts to load audio from the dataset instead of requiring separate reference files.

---

### 🤖 Cursor Assistant

Now I'll update the inference scripts to load audio from the dataset:

---

### 🤖 Cursor Assistant

Now let me update the batch inference script:

---

### 🤖 Cursor Assistant

Now let me update the example_inputs.json to use dataset indices:

---

### 🤖 Cursor Assistant

Now let me update the README to reflect the changes:

---

### 🤖 Cursor Assistant

Perfect! Now let me check for any linting errors:

---

### 🤖 Cursor Assistant

Great! The linting warnings are just about missing packages in the linter's environment - these aren't real errors. 

## Summary

I've updated all the inference scripts to use the exact same approach as the training script - loading audio context directly from the dataset instead of requiring separate audio files:

### Key Changes:

1. **`inference.py`** - Single text inference:
   - Now loads context from dataset using `--dataset-context-idx` (e.g., 3 or 4)
   - Removed dependency on external audio files
   - Uses `load_dataset("MrDragonFox/Elise")` just like the training script

2. **`batch_inference.py`** - Batch processing:
   - Supports `dataset_context_idx` field in JSON
   - Loads dataset once and reuses it for all context examples
   - More efficient than loading individual files

3. **`example_inputs.json`**:
   - Uses exact same texts from training script
   - Uses dataset indices 3 and 4 (same as training script lines 327 and 355)
   - No more `context_audio` or `context_text` file paths

4. **`README.md`**:
   - Updated all examples to use dataset indices
   - Documented the new parameters
   - Removed references to external audio files

5. **`requirements.txt`**:
   - Removed `librosa` (no longer needed)

### Usage Examples:

**Without context:**
```bash
python inference.py \
    --lora-path lora_model \
    --text "We just finished fine tuning a text to speech model... and it's pretty good!" \
    --output example_without_context_1.wav
```

**With context (using dataset index 3, same as training script):**
```bash
python inference.py \
    --lora-path lora_model \
    --text "Sesame is a super cool TTS model which can be fine tuned with Unsloth." \
    --dataset-context-idx 3 \
    --output example_with_context_1.wav
```

**Batch processing:**
```bash
python batch_inference.py \
    --lora-path lora_model \
    --input-file example_inputs.json \
    --output-dir outputs
```

The inference scripts now perfectly mirror the training script's approach! 🎉

---

### 👤 You

README 里面需要强调，虽然微调数据里面没有中文，但是泛化能力能够让它知道用中文输出推理过程

---

### 🤖 Cursor Assistant

我来更新 MultilingualReasoning 的 README，强调模型的跨语言泛化能力。

---

### 🤖 Cursor Assistant

完成！我已经在 README 中多处强调了中文推理的零样本泛化能力：

## 更新的位置

### 1. **项目简介** (第 13 行)
添加了特别说明，突出虽然训练数据中没有中文，但模型能够通过零样本泛化在中文环境下进行推理。

### 2. **功能特性** (第 20 行)
新增了 🌏 图标的特性说明，用 ⚠️ 标记为重要特性，强调：
- 虽然微调数据集中没有中文
- 但模型的泛化能力使其能够在中文环境下生成推理过程
- 这展示了大模型在零样本多语言推理方面的强大能力

### 3. **数据集准备** (第 79 行)
添加了重要提示，提醒读者虽然数据集中没有中文数据，但微调后的模型依然能够用中文进行推理，并引导读者查看详细说明。

### 4. **新增专门章节** (第 200-217 行)
在推理示例之前添加了 "🌏 关于中文推理的重要说明" 章节，详细解释：
- **零样本泛化能力展示**
- 三个原因：
  1. 基础模型的多语言能力
  2. 推理模式的迁移
  3. 系统提示的引导作用
- **实际意义**：
  - 无需为每种语言收集训练数据
  - 降低多语言模型的训练成本
  - 证明推理能力是可迁移的通用能力

这样的强调方式确保读者从多个角度理解这个重要特性，突出了模型强大的跨语言泛化能力！✨

---


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