#
Documentation Chatbot with Meta Synthetic Data Kit

Authored by: Alan Ponnachan

This notebook demonstrates a practical approach to building a domain-specific Question & Answering chatbot. We’ll focus on creating a chatbot that can answer questions about a specific piece of documentation – in this case, LangChain’s documentation on Chat Models.

Goal: To fine-tune a small, efficient Language Model (LLM) to understand and answer questions about the LangChain Chat Models documentation.

Approach:

1. Data Acquisition: Obtain the text content from the target LangChain documentation page.
2. Synthetic Data Generation: Use Meta’s `synthetic-data-kit` to automatically generate Question/Answer pairs from this documentation.
3. Efficient Fine-tuning: Employ Unsloth and `Hugging Face's TRL SFTTrainer` to efficiently fine-tune a Llama-3.2-3B model on the generated synthetic data.
4. Evaluation: Test the fine-tuned model with specific questions about the documentation.

This method allows us to adapt an LLM to a niche domain without requiring a large, manually curated dataset.

Hardware Used:

This notebook was run on Google Colab (Free Tier) with an NVIDIA T4 GPU

##
1. Setup and Installation

First, we need to install the necessary libraries. We’ll use `unsloth` for efficient model handling and training, and `synthetic-data-kit` for generating our training data.

    %%capture
    # In Colab, we skip dependency installation to avoid conflicts with preinstalled packages.
    # On local machines, we include dependencies for completeness.

    import os
    if "COLAB_" not in "".join(os.environ.keys()):
        !pip install unsloth vllm==0.8.2
    else:

        !pip install --no-deps unsloth vllm==0.8.2

    # Get https://github.com/meta-llama/synthetic-data-kit
    !pip install synthetic-data-kit

    %%capture
    import os
    if "COLAB_" in "".join(os.environ.keys()):

        import sys, re, requests; modules = list(sys.modules.keys())
        for x in modules: sys.modules.pop(x) if "PIL" in x or "google" in x else None
        !pip install --no-deps bitsandbytes accelerate xformers==0.0.29.post3 peft "trl==0.15.2" triton cut_cross_entropy unsloth_zoo
        !pip install sentencepiece protobuf datasets huggingface_hub[hf_xet] hf_transfer

        # vLLM requirements - vLLM breaks Colab due to reinstalling numpy
        f = requests.get("https://raw.githubusercontent.com/vllm-project/vllm/refs/heads/main/requirements/common.txt").content
        with open("vllm_requirements.txt", "wb") as file:
            file.write(re.sub(rb"(transformers|numpy|xformers|importlib_metadata)[^\n]{0,}\n", b"", f))
        !pip install -r vllm_requirements.txt

##
2. Synthetic Data Generation

We’ll use `SyntheticDataKit` from Unsloth (which wraps Meta’s `synthetic-data-kit`) to create Question/Answer pairs from our chosen documentation.

    >>> from unsloth.dataprep import SyntheticDataKit

    >>> generator = SyntheticDataKit.from_pretrained(

    ...     model_name = "unsloth/Llama-3.2-3B-Instruct",
    ...     max_seq_length = 2048,
    ... )

    🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning.
    🦥 Unsloth Zoo will now patch everything to make training faster!
    INFO 05-05 15:14:48 [__init__.py:239] Automatically detected platform cuda.

    generator.prepare_qa_generation(
        output_folder = "data", # Output location of synthetic data
        temperature = 0.7, # Higher temp makes more diverse data
        top_p = 0.95,
        overlap = 64, # Overlap portion during chunking
        max_generation_tokens = 512, # Can increase for longer QA pairs
    )

    >>> !synthetic-data-kit system-check

    [?25l[32m VLLM server is running at [0m[4;94mhttp://localhost:8000/v1[0m
    [32m⠋[0m[32m Checking VLLM server at http://localhost:8000/v1...[0m
    [2KAvailable models: [1m&#123;[0m[32m'object'[0m: [32m'list'[0m, [32m'data'[0m: [1m[[0m[1m&#123;[0m[32m'id'[0m:
    [32m'unsloth/Llama-3.2-3B-Instruct'[0m, [32m'object'[0m: [32m'model'[0m, [32m'created'[0m: [1;36m1746459182[0m,
    [32m'owned_by'[0m: [32m'vllm'[0m, [32m'root'[0m: [32m'unsloth/Llama-3.2-3B-Instruct'[0m, [32m'parent'[0m: [3;35mNone[0m,
    [32m'max_model_len'[0m: [1;36m2048[0m, [32m'permission'[0m: [1m[[0m[1m&#123;[0m[32m'id'[0m:
    [32m'modelperm-5296f16bbd3c425a82af4d2f84f0cbfe'[0m, [32m'object'[0m: [32m'model_permission'[0m,
    [32m'created'[0m: [1;36m1746459182[0m, [32m'allow_create_engine'[0m: [3;91mFalse[0m, [32m'allow_sampling'[0m: [3;92mTrue[0m,
    [32m'allow_logprobs'[0m: [3;92mTrue[0m, [32m'allow_search_indices'[0m: [3;91mFalse[0m, [32m'allow_view'[0m: [3;92mTrue[0m,
    [32m'allow_fine_tuning'[0m: [3;91mFalse[0m, [32m'organization'[0m: [32m'*'[0m, [32m'group'[0m: [3;35mNone[0m, [32m'is_blocking'[0m:
    [3;91mFalse[0m[1m}[0m[1m][0m[1m}[0m[1m][0m[1m}[0m
    [32m⠋[0m Checking VLLM server at http://localhost:8000/v1...
    [2K[32m⠋[0m Checking VLLM server at http://localhost:8000/v1...
    [?25h
    [1A[2K

###
2.1. Acquire and Ingest Documentation

For this example, we’ll use the LangChain documentation page on Chat Models.

To get the text:

1. Go to the raw version of the MDX file (e.g., by clicking “Raw” on GitHub).
2. Copy the entire text content.
3. Save it locally as a `.txt` file. For this notebook, we assume you’ve saved it as `/content/langchain-ai-langchain.txt`. You can use a tool like `gitingest` or manual copy-paste.

Note: Ensure the text file is uploaded to your Colab environment at `/content/langchain-ai-langchain.txt` if you’re running this in Colab.

    >>> # Make sure synthetic_data_kit_config.yaml points to the 'data_docs' folder
    >>> !synthetic-data-kit -c synthetic_data_kit_config.yaml ingest /content/langchain-ai-langchain.txt

    [?25l[32m⠋[0m Processing /content/langchain-ai-langchain.txt...
    [?25h
    [1A[2K[32m Text successfully extracted to [0m[1;32mdata/output/langchain-ai-langchain.txt[0m

###
2.2. Chunk Data and Generate QA Pairs

The ingested document will be split into smaller chunks, and then QA pairs will be generated for each chunk.

    >>> filenames = generator.chunk_data("data/output/langchain-ai-langchain.txt")
    >>> print(f"Created {len(filenames)} chunks.")

    Created 3 chunks.

    import time
    # Process 2 chunks for now -> can increase but slower!
    for filename in filenames[:2]:
        !synthetic-data-kit \
            -c synthetic_data_kit_config.yaml \
            create {filename} \
            --num-pairs 25 \
            --type "qa"
        time.sleep(2) # Sleep some time to leave some room for processing

###
2.3. Format and Save QA Pairs

The generated QA pairs are then converted into a format suitable for fine-tuning.

    >>> qa_pairs_filenames = [
    ...     f"data/generated/langchain-ai-langchain_{i}_qa_pairs.json"
    ...     for i in range(len(filenames[:2]))
    ... ]
    >>> for filename in qa_pairs_filenames:
    ...     !synthetic-data-kit \
    ...         -c synthetic_data_kit_config.yaml \
    ...         save-as {filename} -f ft

    [?25l[32m⠋[0m Converting data/generated/langchain-ai-langchain_0_qa_pairs.json to ft format
    with json storage...
    [?25h
    [1A[2K[1A[2K[32m Converted to ft format and saved to [0m
    [1;32mdata/final/langchain-ai-langchain_0_qa_pairs_ft.json[0m
    [?25l[32m⠋[0m Converting data/generated/langchain-ai-langchain_1_qa_pairs.json to ft format
    with json storage...
    [1A[2K[1A[2K[32m Converted to ft format and saved to [0m
    [1;32mdata/final/langchain-ai-langchain_1_qa_pairs_ft.json[0m

    >>> generator.cleanup()

    Attempting to terminate the VLLM server gracefully...
    Server did not terminate gracefully after 10 seconds. Forcing kill...
    Server killed forcefully.

###
2.4. Load the Formatted Dataset

Now, let’s load the generated and formatted data.

    from datasets import Dataset
    import pandas as pd
    final_filenames = [
        f"data/final/langchain-ai-langchain_{i}_qa_pairs_ft.json"
        for i in range(len(filenames[:2]))
    ]
    conversations = pd.concat([
        pd.read_json(name) for name in final_filenames
    ]).reset_index(drop = True)

    dataset = Dataset.from_pandas(conversations)

    dataset[0]

    dataset[-1]

###
Memory Management Note (Critical for Resource-Constrained Environments)

If you encounter CUDA Out-of-Memory (OOM) errors when trying to load the Llama model for fine-tuning in the next steps (even after `generator.cleanup()`), it means the GPU memory wasn’t fully released. This is common in environments like Google Colab’s free tier.

Workaround Strategy:

1. Archive the generated data: After the `generator.cleanup()` cell, zip the entire `/content/data` folder and download to local.
2. Restart the Colab Runtime: Go to “Runtime” -> “Restart runtime…“. This completely clears GPU memory.
3. Re-run Installations & Imports: Execute the initial installation cells and necessary import cells again.
4. Restore Data: Upload the zip data folder and Unzip your data.
5. Load Dataset from Restored Files: Use a script to load from the unzipped `/content/data/final/` directory.
6. Proceed to model loading and fine-tuning.

The cells below include commands for zipping. If you restart, you’d manually run the unzip and data loading code from the “Optional: Restart and Reload Data” section.

    # !zip -r data.zip /content/
    # !unzip data.zip

    # import os
    # import pandas as pd
    # from datasets import Dataset

    # # Path to your folder containing JSON files
    # folder_path = 'content/data/final/'

    # # List all .json files in the folder
    # final_filenames = [os.path.join(folder_path, f) for f in os.listdir(folder_path) if f.endswith('.json')]

    # # Read and combine the JSON files
    # conversations = pd.concat([
    #     pd.read_json(name) for name in final_filenames
    # ]).reset_index(drop=True)

    # # Convert to Hugging Face Dataset
    # dataset = Dataset.from_pandas(conversations)

##
3. Fine-tuning the LLM with Unsloth

Now, we’ll load our base model using Unsloth for 4-bit quantization and then fine-tune it on our synthetically generated dataset.

###
3.1. Load Base Model and Tokenizer

We’ll use `Llama-3.2-3B-Instruct` in 4-bit precision. Unsloth makes this very memory-efficient.

    >>> from unsloth import FastLanguageModel
    >>> import torch

    >>> model, tokenizer = FastLanguageModel.from_pretrained(
    ...     model_name = "unsloth/Llama-3.2-3B-Instruct",
    ...     max_seq_length = 1024, # Choose any for long context!
    ...     load_in_4bit = True,  # 4 bit quantization to reduce memory
    ...     load_in_8bit = False, # [NEW!] A bit more accurate, uses 2x memory
    ...     full_finetuning = False, # [NEW!] We have full finetuning now!

    ... )

    🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning.
    🦥 Unsloth Zoo will now patch everything to make training faster!
    INFO 05-05 15:54:31 [__init__.py:239] Automatically detected platform cuda.
    ==((====))==  Unsloth 2025.4.7: Fast Llama patching. Transformers: 4.51.3. vLLM: 0.8.2.
       \\   /|    Tesla T4. Num GPUs = 1. Max memory: 14.741 GB. Platform: Linux.
    O^O/ \_/ \    Torch: 2.6.0+cu124. CUDA: 7.5. CUDA Toolkit: 12.4. Triton: 3.2.0
    \        /    Bfloat16 = FALSE. FA [Xformers = 0.0.29.post3. FA2 = False]
     "-____-"     Free license: http://github.com/unslothai/unsloth
    Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!

###
3.2. Add LoRA Adapters

We use LoRA (Low-Rank Adaptation) for parameter-efficient fine-tuning.

    model = FastLanguageModel.get_peft_model(
        model,
        r = 16, # Choose any number > 0 ! Suggested 8, 16, 32, 64, 128
        target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
                          "gate_proj", "up_proj", "down_proj",],
        lora_alpha = 16,
        lora_dropout = 0, # Supports any, but = 0 is optimized
        bias = "none",    # Supports any, but = "none" is optimized
        # [NEW] "unsloth" uses 30% less VRAM, fits 2x larger batch sizes!
        use_gradient_checkpointing = "unsloth", # True or "unsloth" for very long context
        random_state = 3407,
        use_rslora = False,  # We support rank stabilized LoRA
        loftq_config = None, # And LoftQ
    )

###
3.3. Data Preparation for Chat Format

We need to format our dataset into the chat template expected by the Llama-3.2 model.

    <|begin_of_text|><|start_header_id|>system<|end_header_id|>

    Cutting Knowledge Date: December 2023
    Today Date: 01 May 2025

    You are a helpful assistant.<|eot_id|><|start_header_id|>user<|end_header_id|>

    What is 1+1?<|eot_id|><|start_header_id|>assistant<|end_header_id|>

    2<|eot_id|>

    def formatting_prompts_func(examples):
        convos = examples["messages"]
        texts = [tokenizer.apply_chat_template(convo, tokenize = False, add_generation_prompt = False) for convo in convos]
        return { "text" : texts, }
    pass

    # Get our previous dataset and format it:
    dataset = dataset.map(formatting_prompts_func, batched = True,)

    dataset[0]

###
3.4. Train the Model

We’ll use Hugging Face TRL’s `SFTTrainer` to fine-tune the model with the SFTTrainer class—designed specifically for supervised fine-tuning (SFT). We configure the training parameters using SFTConfig, specifying the dataset, model, training steps, and optimization settings. This setup allows us to efficiently fine-tune models with gradient accumulation and mixed-precision optimizers like adamw_8bit, onlimited hardware environment.

    from trl import SFTTrainer, SFTConfig

    trainer = SFTTrainer(
        model = model,
        processing_class = tokenizer,
        train_dataset = dataset,
        eval_dataset = None, # Can set up evaluation!
        args = SFTConfig(
            dataset_text_field = "text",
            per_device_train_batch_size = 2,
            gradient_accumulation_steps = 4, # Use GA to mimic batch size!
            warmup_steps = 5,
            max_steps = 60,
            learning_rate = 2e-4,
            logging_steps = 1,
            optim = "adamw_8bit",
            weight_decay = 0.01,
            lr_scheduler_type = "linear",
            seed = 3407,
            report_to = "none", # Use this for WandB etc
        ),
    )

    trainer_stats = trainer.train()

##
4. Inference and Testing

Let’s test our fine-tuned model with some questions related to the LangChain Chat Models documentation.

    >>> messages = [
    ...     {"role": "user", "content": "What is the standard interface for binding tools to models?"},
    ... ]
    >>> inputs = tokenizer.apply_chat_template(
    ...     messages,
    ...     tokenize = True,
    ...     add_generation_prompt = True, # Must add for generation
    ...     return_tensors = "pt",
    ... ).to("cuda")

    >>> from transformers import TextStreamer
    >>> text_streamer = TextStreamer(tokenizer, skip_prompt = True)
    >>> _ = model.generate(input_ids = inputs, streamer = text_streamer,
    ...                    max_new_tokens = 256, temperature = 0.1)

    Standard [tool calling API](/docs/concepts/tool_calling): standard interface for binding tools to models.<|eot_id|>

##
5. Conclusion

We have successfully:

1. Acquired documentation text for LangChain Chat Models..
2. Generated synthetic Question/Answer pairs using `synthetic-data-kit`.
3. Fine-tuned a Llama-3.2-3B model efficiently using Unsloth and Hugging Face’s TRL SFTTrainer.
4. Tested the model’s ability to answer questions specific to the documentation.

This notebook provides a template for creating specialized chatbots for various documentation or domain-specific texts. The use of synthetic data generation and efficient fine-tuning techniques makes this approach accessible even with limited resources.

Further improvements could include:

- Using a larger portion of the documentation or multiple related pages.
- More sophisticated curation of synthetic QA pairs.
- Experimenting with different base models or hyperparameter tuning.
- Implementing a more robust evaluation framework (e.g., comparing against a held-out set of questions or using metrics like ROUGE, BLEU if applicable, or LLM-as-a-judge).

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