Walk me through fine tuning steps overview first I will let you know what we are doing next
Sure thing—here’s a **concise overview of the fine-tuning process** for a language model like GPT-style transformers:
—
### 🧠 **Fine-Tuning Overview (High-Level Steps)**
1. **Define Objective**
– What are you tuning for? (e.g., tone, task specialization, domain knowledge)
2. **Collect & Prepare Data**
– Format: JSONL with `{“prompt”: “…”, “completion”: “…”}` structure.
– Clean and align your data: no hallucinations, no leakage, no junk.
– Tokenization: make sure prompt + completion fit model’s token limit.
3. **Choose Base Model**
– Select the right foundation (e.g., GPT-2, LLaMA, Mistral, etc.)
– Smaller = faster, cheaper. Bigger = better nuance, more compute.
4. **Set Up Environment**
– Framework: Hugging Face Transformers + `peft` (for LoRA) or OpenAI CLI.
– Hardware: GPU or TPU (depends on model size).
5. **Fine-Tune**
– Techniques: Full fine-tuning, LoRA (efficient), QLoRA (quantized).
– Epochs, learning rate, batch size—tune cautiously.
6. **Evaluate**
– Use a test set. Check accuracy, relevance, hallucination rate, etc.
– Try human eval or prompt-based benchmarks (e.g., MT-Bench, TruthfulQA).
7. **Deploy**
– Export to appropriate format.
– Serve with latency & safety measures (moderation, fallback, logging).
8. **Monitor & Iterate**
– Continuous feedback loop.
– Update datasets. Re-fine-tune if drift occurs.
—
Let me know what you’re tuning and I’ll dial in the next steps.
Ayn applications that will do this form me?
Yep—several apps and platforms can do most or all of the fine-tuning process *for* you, especially if you’re not looking to reinvent the training wheel. Here are the main contenders:
—
### 🛠️ **Fine-Tuning Platforms (DIY Made Easy)**
#### **1. OpenAI Fine-Tuning API**
– 🔧 **Model**: GPT-3.5-turbo (as of now)
– 📦 **Input**: JSONL (`prompt` + `completion`)
– 🧘♂️ **Ease**: Extremely easy CLI workflow.
– 🚫 **Drawback**: No access to model weights; not good for deep tinkering.
#### **2. Hugging Face AutoTrain**
– 🌐 **Models**: LLaMA, Mistral, Falcon, DistilBERT, and more.
– 🎯 **Focus**: Text classification, QA, summarization, generation.
– ⚙️ **Options**: Choose between full fine-tuning, LoRA, quantized methods.
– 💸 **Free tier**: Exists but limited GPU hours.
#### **3. Replicate**
– 🎨 Focused on deploying tuned models (often vision + language).
– You can **host your fine-tuned model** and make APIs fast.
– Does not always handle *the fine-tuning* step itself.
#### **4. Google Vertex AI / AWS SageMaker**
– 🧠 **Enterprise-grade**.
– 🔩 Full control of training jobs, fine-tuning pipelines.
– ⚙️ Requires more setup but supports Hugging Face integration.
#### **5. Modal / Spell / RunPod**
– These are more for spinning up custom GPU jobs.
– Use with open-source libraries (like 🤗 Transformers + PEFT).
– Fine-tuning scripts often provided.
—
### 🧪 Honorable Mentions
– **Lamini** – enterprise-focused fine-tuning with guardrails.
– **Together.ai** – offers fine-tuning jobs for open models like Mistral-7B.
– **Weights & Biases** – tracks fine-tuning experiments but doesn’t run them.
—
If you tell me your use case (classification, chatbot, summarizer, etc.) and model size range (GPT-3.5 vs LLaMA vs tiny), I can pinpoint the best service for it. Want hosted + simple, or fully local + hackable?
How to fine tunbe a model using hugging face?
Ah, going the Hugging Face route—perfect for control freaks and tinkerers. Here’s a **straightforward roadmap** to fine-tune a text model using Hugging Face Transformers, assuming you want to fine-tune for *text generation* (like a chatbot or completion model):
—
### 🧠 **Fine-Tuning a Language Model with Hugging Face (Text Generation)**
#### 🧰 **Requirements**
– Python ≥ 3.8
– `transformers`, `datasets`, `accelerate`, `peft` (for LoRA), and optionally `bitsandbytes` (for QLoRA)
– GPU (RTX 3060+ for smaller models, A100 for 13B+)
—
### ⚙️ Step-by-Step
#### **1. Install Dependencies**
“`bash
pip install transformers datasets accelerate peft bitsandbytes
“`
#### **2. Prepare Dataset**
Use JSONL format:
“`json
{“prompt”: “Question: What is gravity?nAnswer:”, “completion”: ” Gravity is the curvature of spacetime…”}
“`
Convert to Hugging Face Dataset:
“`python
from datasets import load_dataset
dataset = load_dataset(“json”, data_files=”data.jsonl”)
“`
#### **3. Load Base Model**
Use a small model like `gpt2` for testing:
“`python
from transformers import AutoTokenizer, AutoModelForCausalLM
model_name = “gpt2”
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
“`
#### **4. Tokenize Dataset**
“`python
def tokenize(example):
return tokenizer(example[“prompt”] + example[“completion”], truncation=True, padding=”max_length”, max_length=512)
tokenized = dataset.map(tokenize, batched=True)
“`
#### **5. Training Setup**
“`python
from transformers import TrainingArguments, Trainer
training_args = TrainingArguments(
output_dir=”./finetuned-model”,
per_device_train_batch_size=4,
num_train_epochs=3,
learning_rate=5e-5,
logging_steps=10,
save_steps=500,
fp16=True, # if using compatible GPU
)
trainer = Trainer(
model=model,
args=training_args,
train_dataset=tokenized[“train”],
tokenizer=tokenizer,
)
“`
#### **6. Train!**
“`python
trainer.train()
“`
#### **7. Save Model**
“`python
trainer.save_model(“./finetuned-model”)
tokenizer.save_pretrained(“./finetuned-model”)
“`
—
### 🔧 Optional Upgrades
– **Use LoRA**: Swap in `peft` to reduce GPU demand.
– **Use QLoRA**: For massive models like Mistral-7B on consumer GPUs.
– **Use Accelerate**: For multi-GPU/distributed training.
—
Want the minimal script for GPT2, or do you have a bigger model in mind (e.g., Mistral-7B or LLaMA)?
I thought fine tuning was hosted on HF?
Ah, *gotcha!* You’re talking about **Hugging Face’s hosted fine-tuning**, a.k.a. **AutoTrain**, not the roll-your-own script route.
Yes—they *do* offer a **no-code, browser-based fine-tuning system**, and here’s how that works 👇
—
### 🚀 **AutoTrain (Hugging Face Hosted Fine-Tuning)**
#### ✅ What It Does:
– Fine-tunes models like LLaMA, Mistral, Falcon, GPT-2, and BERT variants.
– You upload your data, choose a task, and click “train.”
– No code, no scripts, no environment setup.
#### 🧭 How To Use It:
1. **Go to**: [https://huggingface.co/autotrain](https://huggingface.co/autotrain)
2. **Sign in** (or create a HF account)
3. **Create a new project**
– Pick task type:
– `Text Classification`, `Text Generation`, `NER`, etc.
– For chatbot-style outputs, use **Text-to-Text or Language Modeling**
4. **Upload your dataset**
– CSV or JSON format.
– Example CSV for text generation:
“`
input,output
“What’s the capital of France?”, “Paris.”
“`
5. **Choose model**
– e.g., `gpt2`, `mistralai/Mistral-7B-v0.1`, `tiiuae/falcon-7b`
6. **Adjust settings**
– Epochs, learning rate, LoRA (optional), quantization
– Enable evaluation if you want metrics
7. **Launch training**
– It uses Hugging Face-provided compute (can be free or paid depending on usage and size)
8. **Done!**
– You can **download**, **share**, or **deploy** the trained model via HF Inference API or Spaces.
—
### 💰 Cost?
– Small models: Some training free credits.
– Larger models (7B+): Paid credits required or BYOGPU via Inference Endpoints.
—
Would you like a walkthrough for how to structure your dataset for AutoTrain, or do you want to test it first with a toy dataset?
rain split?
Ah yes—**train/test split** is a key part of the fine-tuning pipeline, even with Hugging Face AutoTrain. Here’s the quick breakdown:
—
### 🧪 **Train/Test Split in Hugging Face AutoTrain**
#### 📂 If you’re uploading a dataset:
– **AutoTrain will auto-split** your dataset by default:
– **80% for training**, **20% for evaluation**.
– No need to manually split unless you’re picky.
#### 🔧 If you *want control*:
You can upload your data in **two separate files**:
– `train.csv` (or `.json`)
– `valid.csv`
AutoTrain will detect them and skip the auto-split.
#### 💡 Format reminder (CSV):
“`csv
input,output
“What is gravity?”, “It’s the curvature of spacetime.”
…
“`
—
### 🛠️ Locally using 🤗 `datasets`:
If you’re coding instead of using AutoTrain:
“`python
from datasets import load_dataset
dataset = load_dataset(“json”, data_files=”data.jsonl”, split=”train”)
split_dataset = dataset.train_test_split(test_size=0.2)
“`
Then pass `split_dataset[“train”]` and `split_dataset[“test”]` to the trainer.
—
Want me to build you a sample dataset and upload format for generation tasks?
this what looks ny dataset: ACLAllocator::allocate
/Engine/Plugins/Animation/ACLPlugin/Source/ACLPlugin/Public/ACLImpl.h
ACLPlugin
virtual void * allocate ( size_t size, size_t alignment )
[]
ACLAllocator::deallocate
/Engine/Plugins/Animation/ACLPlugin/Source/ACLPlugin/Public/ACLImpl.h
ACLPlugin
virtual void deallocate ( void * ptr, size_t size )
[]
ACLAllocator
Ah, I see—your dataset looks like it’s structured from **code documentation** or **symbol/function reference entries**, maybe for training a code assistant or embedding-based search? But as it stands, it’s not in a format that Hugging Face (or any fine-tuning tool) will understand *out of the box*.
—
### 🧱 What’s Wrong?
Right now, your data seems like raw records:
“`
Function Name
File Path
Module
Function Signature
[]
Class Name
“`
That won’t work unless you transform it into something structured—like **input/output pairs** for generation.
—
### 🛠️ You Need a Format Like:
#### 🔁 For Code Completion / Docstring Generation (CSV):
“`csv
input,output
“What does ACLAllocator::allocate do?”, “virtual void * allocate ( size_t size, size_t alignment )”
“Where is ACLAllocator::allocate defined?”, “/Engine/Plugins/Animation/ACLPlugin/Source/ACLPlugin/Public/ACLImpl.h”
“`
#### 🧠 For Full Prompt-Completion Training (JSONL):
“`json
{“prompt”: “Function: ACLAllocator::allocatenPath: /Engine/Plugins/…/ACLImpl.hnSignature:”, “completion”: ” virtual void * allocate ( size_t size, size_t alignment )”}
“`
—
### 🤖 What Are You Trying to Train?
– A **code completion model**?
– A **semantic search model** for symbols?
– A **doc generator** (prompt with a symbol, generate description)?
If you tell me the *task* you’re aiming for, I can reshape your dataset accordingly. Want a quick script to auto-format it?
code completion
Perfect—**code completion** it is. You want your model to predict function signatures, definitions, or maybe even full implementations based on a name, class, path, or other hints.
—
### ✅ Ideal Format for Code Completion Fine-Tuning (Text Generation)
Use **JSONL format** (for Hugging Face or AutoTrain) like this:
“`json
{“prompt”: “ACLAllocator::allocaten/Engine/Plugins/Animation/ACLPlugin/Source/ACLPlugin/Public/ACLImpl.hn”, “completion”: “virtual void * allocate ( size_t size, size_t alignment )”}
{“prompt”: “ACLAllocator::deallocaten/Engine/Plugins/Animation/ACLPlugin/Source/ACLPlugin/Public/ACLImpl.hn”, “completion”: “virtual void deallocate ( void * ptr, size_t size )”}
“`
This setup:
– **Prompt** = function name + file path
– **Completion** = function signature (or full definition if you want to go deeper)
You can extend the prompt with more context like class name or module, e.g.:
“`json
{“prompt”: “Class: ACLAllocatornModule: ACLPluginnFunction: allocatenPath: /…/ACLImpl.hn”, “completion”: “virtual void * allocate ( size_t size, size_t alignment )”}
“`
—
### ⚡ Next Step?
I can generate a Python script to:
– Parse your current format
– Output a clean `.jsonl` file ready for Hugging Face training or upload
Want the script? Or want to hand-format a few first for testing?