# Auto Train with alpaca model data set

**URL:** <https://discuss.huggingface.co/t/auto-train-with-alpaca-model-data-set/168711>\
**Category:** 🤗AutoTrain\
**Created:** [September 26, 2025, 10:09pm UTC](https://discuss.huggingface.co/t/auto-train-with-alpaca-model-data-set/168711 "2025-09-26T22:09:55Z")\
**Posts on this page:** 8\
**Page:** 1

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**Author:** ![ynsbyrm](https://sea2.discourse-cdn.com/hellohellohello/user_avatar/discuss.huggingface.co/ynsbyrm/32/54307_2.png) [@ynsbyrm](https://discuss.huggingface.co/u/ynsbyrm)\
**Post date:** [September 26, 2025, 10:09pm UTC](https://discuss.huggingface.co/t/auto-train-with-alpaca-model-data-set/168711/1 "2025-09-26T22:09:55Z")

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Hi there,

I’m new both on this forum and huggingface world. Please go easy on me 🙂  
I have a question to ask. I want to use auto train for fine tune a model like meta-llama/Llama-3.1-8B-Instruct. I have a data set which is in alpaca model with instruction, input and output columns.

Questions are;

I couldn’t find a good document or example in order to learn how to fine tune a model with using this type of model.

None of the information buttons are working on the Auto Train screen like the one above task or parameter combo-box.

How can I put more fields in column mapping section? There is only one right now. I think I should put instruction, input and output columns.

If there is any good documentation, please share it with me. So, I can started to learn some stuff.

 ![image](https://us1.discourse-cdn.com/hellohellohello/original/3X/7/3/73f1f6af42d758889638b241366e8aabe449e03c.png)

Best regards,  
Yunus Emre

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**Author:** ![John6666](https://sea2.discourse-cdn.com/hellohellohello/user_avatar/discuss.huggingface.co/john6666/32/27664_2.png) [@John6666](https://discuss.huggingface.co/u/John6666)\
**Post date:** [September 26, 2025, 11:14pm UTC](https://discuss.huggingface.co/t/auto-train-with-alpaca-model-data-set/168711/2 "2025-09-26T23:14:08Z")

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Hmm… [Try this](https://huggingface.co/docs/autotrain/en/tasks/llm_finetuning). And for [AutoTrain CSV data format](https://discuss.huggingface.co/t/autotrain-csv-data-format/63305).

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<div class="post-metadata">

**Author:** ![ynsbyrm](https://sea2.discourse-cdn.com/hellohellohello/user_avatar/discuss.huggingface.co/ynsbyrm/32/54307_2.png) [@ynsbyrm](https://discuss.huggingface.co/u/ynsbyrm)\
**Post date:** [October 1, 2025, 5:59pm UTC](https://discuss.huggingface.co/t/auto-train-with-alpaca-model-data-set/168711/3 "2025-10-01T17:59:16Z")

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Hi @John6666 ,

Thank you for your response. I’ve made some tries regarding the links which you’ve shared. I believe it is better now but I still have some questions. If you redirect me it would be really helpful.

For LLM SFT task I need to combine the columns from data set and put them in one column as text in the csv. The point which I don’t understand how LLM will understand which column means what? I saw there are few other data sets in here for example one of them has 3 columns but other one has 7. Is there anyway to differentiate which data set should use in which case or is this requires a knowledge of data scientists?

Best regards,  
Yunus

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<div class="post-metadata">

**Author:** ![John6666](https://sea2.discourse-cdn.com/hellohellohello/user_avatar/discuss.huggingface.co/john6666/32/27664_2.png) [@John6666](https://discuss.huggingface.co/u/John6666)\
**Post date:** [October 1, 2025, 9:59pm UTC](https://discuss.huggingface.co/t/auto-train-with-alpaca-model-data-set/168711/4 "2025-10-01T21:59:45Z")

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I don’t have any data science knowledge whatsoever, but I think we can manage if we just do some basic preprocessing in Python… Functions for data processing and shaping are usually available somewhere in the libraries.

* * *

Use one rendered `text` column for SFT. Do not map `instruction/input/output` separately. Convert your rows to the model’s chat format, save as a single-column dataset, and map `text → text` in AutoTrain. ([Hugging Face](https://huggingface.co/docs/autotrain/en/tasks/llm_finetuning "LLM Finetuning with AutoTrain Advanced"))

# Beginner guide: LLM SFT with AutoTrain

## 1) Choose trainer and model

- Trainer: **SFT** in AutoTrain Advanced. ([Hugging Face](https://huggingface.co/docs/autotrain/en/tasks/llm_finetuning "LLM Finetuning with AutoTrain Advanced"))
- Model: pick your chat model and its tokenizer, e.g. `meta-llama/Llama-3.1-8B-Instruct`. ([Hugging Face](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct "meta-llama/Llama-3.1-8B-Instruct"))

## 2) Know the accepted dataset shapes

SFTTrainer accepts either:

- single-column: `{"text": "...final rendered conversation..."}`, or
- two-column: `{"prompt": "...", "completion": "..."}`.  
AutoTrain commonly uses the single `text` column for chat SFT. ([Hugging Face](https://huggingface.co/docs/trl/en/sft_trainer "SFT Trainer"))

## 3) Render your triples into one training string

- Build messages: user = `instruction + ("\n\n" + input if present)`; assistant = `output`.
- Render with the tokenizer’s **chat template** : `apply_chat_template(messages, tokenize=False, add_generation_prompt=False)`.
- Save one column named **text**. ([Hugging Face](https://huggingface.co/docs/transformers/en/chat_templating "Chat templates"))

## 4) Minimal preprocessing code

```python
from datasets import load_dataset
from transformers import AutoTokenizer
import pandas as pd

tok = AutoTokenizer.from_pretrained("meta-llama/Llama-3.1-8B-Instruct")

def render_row(r):
    user = r["instruction"] + (("\n\n" + r["input"]) if r.get("input") else "")
    messages = [{"role":"user","content":user},
                {"role":"assistant","content":r["output"]}]
    return tok.apply_chat_template(messages, tokenize=False, add_generation_prompt=False)

ds = load_dataset("tatsu-lab/alpaca", split="train") # replace with your data
df = pd.DataFrame({"text": [render_row(x) for x in ds]})
df.to_csv("autotrain_llm_sft.csv", index=False)

```

`apply_chat_template` ensures the exact prompt tokens and headers the model expects. ([Hugging Face](https://huggingface.co/docs/transformers/v4.51.1/chat_templating "Templates"))

## 5) Create the AutoTrain job

UI: upload CSV/JSONL, set **Column Mapping → text → text** , choose **LLM finetuning → SFT**. ([Hugging Face](https://huggingface.co/docs/autotrain/en/col_map "Understanding Column Mapping"))  
CLI (reliable, explicit):

```bash
pip install autotrain-advanced

autotrain llm \
  --train \
  --project-name llama31-alpaca-sft \
  --model meta-llama/Llama-3.1-8B-Instruct \
  --data-path ./ \
  --train-split train \
  --text-column text \
  --trainer sft \
  --use-peft \
  --lora-r 16 --lora-alpha 32 --lora-dropout 0.05 \
  --batch-size 4 --gradient-accumulation 8 \
  --lr 2e-4 --epochs 3 --bf16 \
  --max-seq-length 4096

```

Flags mirror documented AutoTrain usage. Adjust batch and GA for VRAM. ([Hugging Face](https://huggingface.co/docs/autotrain/en/tasks/llm_finetuning "LLM Finetuning with AutoTrain Advanced"))

## 6) Inference must match training

At generation, build messages and call the **same tokenizer’s** chat template to format the prompt before `generate`. Template mismatches degrade outputs. Llama 3.1 has known header nuances; verify your output. ([Hugging Face](https://huggingface.co/docs/transformers/en/chat_templating "Chat templates"))

## 7) When you’d use more columns

Only if you pick a different trainer or format:

- **Prompt+completion SFT** : map `prompt` and `completion`. ([Hugging Face](https://huggingface.co/docs/trl/en/sft_trainer "SFT Trainer"))
- **DPO/ORPO** : needs `prompt`, `chosen`, `rejected`. AutoTrain exposes those roles in column mapping. ([Hugging Face](https://huggingface.co/docs/autotrain/en/tasks/llm_finetuning "LLM Finetuning with AutoTrain Advanced"))

## 8) Quick checks

- Open one CSV row. Confirm it contains the full rendered conversation string. ([Hugging Face](https://huggingface.co/docs/trl/en/sft_trainer "SFT Trainer"))
- If UI mapping is unclear, switch to CLI and set `--text-column text`. ([Hugging Face](https://huggingface.co/docs/autotrain/en/tasks/llm_finetuning "LLM Finetuning with AutoTrain Advanced"))
- If outputs look odd, print a rendered example, confirm chat headers match the model card’s template. ([Llama](https://www.llama.com/docs/model-cards-and-prompt-formats/llama3_1/ "Llama 3.1 | Model Cards and Prompt formats"))

## References

AutoTrain LLM finetuning and column mapping, TRL SFT dataset formats, and chat templating docs. ([Hugging Face](https://huggingface.co/docs/autotrain/en/tasks/llm_finetuning "LLM Finetuning with AutoTrain Advanced"))

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<div class="post-metadata">

**Author:** ![John6666](https://sea2.discourse-cdn.com/hellohellohello/user_avatar/discuss.huggingface.co/john6666/32/27664_2.png) [@John6666](https://discuss.huggingface.co/u/John6666)\
**Post date:** [October 1, 2025, 11:07pm UTC](https://discuss.huggingface.co/t/auto-train-with-alpaca-model-data-set/168711/5 "2025-10-01T23:07:44Z")

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For SFT and its practical implementation, the [Smol course](https://huggingface.co/learn/smol-course/unit0/1) provides a concise overview of the entire process, so I recommend giving it a quick read.

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**Author:** ![ynsbyrm](https://sea2.discourse-cdn.com/hellohellohello/user_avatar/discuss.huggingface.co/ynsbyrm/32/54307_2.png) [@ynsbyrm](https://discuss.huggingface.co/u/ynsbyrm)\
**Post date:** [October 3, 2025, 8:31am UTC](https://discuss.huggingface.co/t/auto-train-with-alpaca-model-data-set/168711/6 "2025-10-03T08:31:23Z")

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Hi @John6666 ,

Great explanation and these are wonderful links. I’m feel like enlightened. Even I started to following that smol course.

Thank you,  
Yunus 🤗

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**Author:** ![JamesDavids](https://sea2.discourse-cdn.com/hellohellohello/user_avatar/discuss.huggingface.co/jamesdavids/32/54347_2.png) [@JamesDavids](https://discuss.huggingface.co/u/JamesDavids)\
**Post date:** [October 4, 2025, 7:03am UTC](https://discuss.huggingface.co/t/auto-train-with-alpaca-model-data-set/168711/7 "2025-10-04T07:03:00Z")

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Welcome! 😊 You’re on the right track. Hugging Face **AutoTrain** does support fine-tuning instruction-style datasets like Alpaca, but it’s a bit limited compared to manual training.

- For datasets with **instruction / input / output** , the standard approach is to **merge instruction + input into a single prompt column** , and keep output as the label. AutoTrain usually expects just one “text” and one “label/output” field.

- If the UI only shows one mapping field, you’ll need to preprocess your dataset before uploading (e.g., combine `instruction` + `input` into a new `prompt` column).

- For full control, many people skip AutoTrain and instead use the Hugging Face **`trl` library** (`SFTTrainer`) with LoRA. This gives you more flexibility for instruction-tuning LLaMA models.

Docs to check:

- Fine-tuning with TRL

- AutoTrain docs

So TL;DR: preprocess into 2 columns (`prompt`, `output`), then upload to AutoTrain, or use `trl` for more advanced setups.

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**Author:** ![system](https://us1.discourse-cdn.com/hellohellohello/original/2X/d/de4155eb4aa4108ecb32a1389d7cc37ae69f88b7.png) [@system](https://discuss.huggingface.co/u/system)\
**Post date:** [October 7, 2025, 3:04pm UTC](https://discuss.huggingface.co/t/auto-train-with-alpaca-model-data-set/168711/8 "2025-10-07T15:04:17Z")

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