Fine-tune LLM

Fine-tune custom LLMs with efficient techniques on Clore.ai GPUs

Train your own custom LLM using efficient fine-tuning techniques on CLORE.AI GPUs.

All examples can be run on GPU servers rented through CLORE.AI Marketplace.

Renting on CLORE.AI

1. Visit CLORE.AI Marketplace

2. Filter by GPU type, VRAM, and price

3. Choose On-Demand (fixed rate) or Spot (bid price)

4. Configure your order:

   • Select Docker image

   • Set ports (TCP for SSH, HTTP for web UIs)

   • Add environment variables if needed

   • Enter startup command

5. Select payment: CLORE, BTC, or USDT/USDC

6. Create order and wait for deployment

Access Your Server

• Find connection details in My Orders

• Web interfaces: Use the HTTP port URL

• SSH: ssh -p <port> root@<proxy-address>

What is LoRA/QLoRA?

• LoRA (Low-Rank Adaptation) - Train small adapter layers instead of full model

• QLoRA - LoRA with quantization for even less VRAM

• Train 7B model on single RTX 3090

• Train 70B model on single A100

Requirements

Model	Method	Min VRAM	Recommended
7B	QLoRA	12GB	RTX 3090
13B	QLoRA	20GB	RTX 4090
70B	QLoRA	48GB	A100 80GB
7B	Full LoRA	24GB	RTX 4090

Quick Deploy

Docker Image:

pytorch/pytorch:2.5.1-cuda12.4-cudnn9-devel

Ports:

22/tcp
8888/http
6006/http

Command:

pip install "transformers>=4.45" "datasets>=2.20" accelerate "peft>=0.14" \
    bitsandbytes "trl>=0.12" wandb jupyterlab && \
jupyter lab --ip=0.0.0.0 --port=8888 --allow-root

Accessing Your Service

After deployment, find your http_pub URL in My Orders:

1. Go to My Orders page

2. Click on your order

3. Find the http_pub URL (e.g., abc123.clorecloud.net)

Use https://YOUR_HTTP_PUB_URL instead of localhost in examples below.

Dataset Preparation

Chat Format (Recommended)

[
  {
    "messages": [
      {"role": "system", "content": "You are a helpful assistant."},
      {"role": "user", "content": "What is Python?"},
      {"role": "assistant", "content": "Python is a programming language..."}
    ]
  }
]

Instruction Format

[
  {
    "instruction": "Translate to French",
    "input": "Hello, how are you?",
    "output": "Bonjour, comment allez-vous?"
  }
]

Alpaca Format

[
  {
    "instruction": "Give three tips for staying healthy.",
    "input": "",
    "output": "1. Eat balanced meals..."
  }
]

Supported Modern Models (2025)

Model	HF ID	Min VRAM (QLoRA)
Llama 3.1 / 3.3 8B	meta-llama/Llama-3.1-8B-Instruct	12GB
Qwen 2.5 7B / 14B	Qwen/Qwen2.5-7B-Instruct	12GB / 20GB
DeepSeek-R1-Distill (7B/8B)	deepseek-ai/DeepSeek-R1-Distill-Qwen-7B	12GB
Mistral 7B v0.3	mistralai/Mistral-7B-Instruct-v0.3	12GB
Gemma 2 9B	google/gemma-2-9b-it	14GB
Phi-4 14B	microsoft/phi-4	20GB

QLoRA Fine-tuning Script

Modern example with PEFT 0.14+, Flash Attention 2, DoRA support, and Qwen2.5 / DeepSeek-R1 compatibility:

import torch
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
    BitsAndBytesConfig,
    TrainingArguments,
)
from peft import LoraConfig, get_peft_model, prepare_model_for_kbit_training
from datasets import load_dataset
from trl import SFTTrainer, SFTConfig

# === Configuration ===
# Choose one of: Qwen2.5, DeepSeek-R1-Distill, Llama 3.1, Mistral, etc.
MODEL_NAME = "Qwen/Qwen2.5-7B-Instruct"
# MODEL_NAME = "deepseek-ai/DeepSeek-R1-Distill-Qwen-7B"
# MODEL_NAME = "meta-llama/Llama-3.1-8B-Instruct"

DATASET = "your_dataset.json"  # or HuggingFace dataset name
OUTPUT_DIR = "./output"
MAX_SEQ_LENGTH = 4096           # Qwen2.5 supports up to 32K context
USE_DORA = True                 # DoRA improves quality over standard LoRA
USE_FLASH_ATTN = True           # Flash Attention 2 saves VRAM & speeds up

# === Load Model with 4-bit Quantization ===
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

model = AutoModelForCausalLM.from_pretrained(
    MODEL_NAME,
    quantization_config=bnb_config,
    device_map="auto",
    trust_remote_code=True,  # Required for Qwen2.5 and DeepSeek
    # Flash Attention 2: requires Ampere+ GPU (RTX 30/40, A100)
    attn_implementation="flash_attention_2" if USE_FLASH_ATTN else "eager",
)

tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, trust_remote_code=True)
if tokenizer.pad_token is None:
    tokenizer.pad_token = tokenizer.eos_token
tokenizer.padding_side = "right"

# === Configure LoRA with optional DoRA ===
# DoRA (Weight-Decomposed Low-Rank Adaptation) — PEFT >= 0.14 required
# use_dora=True decomposes weights into magnitude + direction for better quality
lora_config = LoraConfig(
    r=64,                    # Rank (higher = more capacity, more VRAM)
    lora_alpha=16,           # Scaling factor (keep equal to or half of r)
    target_modules=[
        "q_proj", "k_proj", "v_proj", "o_proj",  # Attention layers
        "gate_proj", "up_proj", "down_proj",      # MLP layers
    ],
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
    use_dora=USE_DORA,        # DoRA: improved quality (PEFT 0.14+)
    # use_rslora=True,        # Optional: Rank-Stabilized LoRA
)

# Prepare model for QLoRA training
model = prepare_model_for_kbit_training(
    model,
    use_gradient_checkpointing=True,
    gradient_checkpointing_kwargs={"use_reentrant": False},
)
model = get_peft_model(model, lora_config)

# Print trainable parameters summary
model.print_trainable_parameters()
# Example output: trainable params: 42,991,616 || all params: 7,284,891,648 || trainable%: 0.59

# === Load Dataset ===
dataset = load_dataset("json", data_files=DATASET)
# Or use a public dataset:
# dataset = load_dataset("HuggingFaceH4/ultrachat_200k")

# === Format Dataset for Qwen2.5 / ChatML format ===
def format_chat_qwen(example):
    """Format for Qwen2.5 using ChatML template."""
    messages = example.get("messages", [])
    if not messages:
        # Handle alpaca-style data
        text = f"<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n"
        text += f"<|im_start|>user\n{example['instruction']}"
        if example.get("input"):
            text += f"\n{example['input']}"
        text += f"<|im_end|>\n<|im_start|>assistant\n{example['output']}<|im_end|>"
    else:
        # Handle messages format (ChatML)
        text = tokenizer.apply_chat_template(
            messages,
            tokenize=False,
            add_generation_prompt=False,
        )
    return {"text": text}

dataset = dataset.map(format_chat_qwen, remove_columns=dataset["train"].column_names)

# === Training Arguments (PEFT 0.14+ / TRL 0.12+) ===
training_args = SFTConfig(
    output_dir=OUTPUT_DIR,
    num_train_epochs=3,
    per_device_train_batch_size=2,
    gradient_accumulation_steps=8,         # Effective batch = 2 * 8 = 16
    learning_rate=2e-4,
    weight_decay=0.001,
    warmup_ratio=0.03,
    lr_scheduler_type="cosine",
    logging_steps=10,
    save_steps=100,
    save_total_limit=3,
    bf16=True,                             # Use bf16 for modern GPUs (A100, RTX 30/40)
    # fp16=True,                           # Use fp16 for older GPUs
    optim="paged_adamw_8bit",
    max_grad_norm=0.3,
    group_by_length=True,
    report_to="wandb",                     # or "tensorboard"
    # SFTConfig-specific:
    max_seq_length=MAX_SEQ_LENGTH,
    dataset_text_field="text",
    packing=True,                          # Pack multiple examples for efficiency
)

# === Train ===
trainer = SFTTrainer(
    model=model,
    train_dataset=dataset["train"],
    tokenizer=tokenizer,
    args=training_args,
)

trainer.train()

# === Save LoRA adapter ===
trainer.save_model(f"{OUTPUT_DIR}/final")
tokenizer.save_pretrained(f"{OUTPUT_DIR}/final")
print(f"Model saved to {OUTPUT_DIR}/final")

Flash Attention 2

Flash Attention 2 reduces VRAM usage and speeds up training significantly. Requires Ampere+ GPU (RTX 3090, RTX 4090, A100).

# Install Flash Attention 2
pip install flash-attn --no-build-isolation

# Enable in model loading:
model = AutoModelForCausalLM.from_pretrained(
    MODEL_NAME,
    attn_implementation="flash_attention_2",  # <-- add this
    torch_dtype=torch.bfloat16,               # FA2 requires bf16 or fp16
    device_map="auto",
)

Setting	VRAM (7B)	Speed
Standard attention (fp16)	~22GB	baseline
Flash Attention 2 (bf16)	~16GB	+30%
Flash Attention 2 + QLoRA	~12GB	+30%

DoRA (Weight-Decomposed LoRA)

DoRA (PEFT >= 0.14) decomposes pre-trained weights into magnitude and direction components. It improves fine-tuning quality, especially for smaller ranks.

from peft import LoraConfig

# Standard LoRA
lora_config = LoraConfig(r=64, lora_alpha=16, use_dora=False, ...)

# DoRA — same parameters, better quality
lora_config = LoraConfig(r=64, lora_alpha=16, use_dora=True, ...)
# Note: DoRA adds ~5-10% VRAM overhead vs standard LoRA
# Note: Not compatible with quantized (4-bit/8-bit) models in all cases

Qwen2.5 & DeepSeek-R1-Distill Examples

Qwen2.5 Fine-tuning

MODEL_NAME = "Qwen/Qwen2.5-7B-Instruct"
# For 14B: "Qwen/Qwen2.5-14B-Instruct" (needs 20GB+ VRAM with QLoRA)

model = AutoModelForCausalLM.from_pretrained(
    MODEL_NAME,
    quantization_config=bnb_config,
    device_map="auto",
    trust_remote_code=True,          # Required for Qwen2.5
    attn_implementation="flash_attention_2",
)
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, trust_remote_code=True)

# Qwen2.5 uses ChatML format — use apply_chat_template
messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "Hello!"},
    {"role": "assistant", "content": "Hi there! How can I help?"},
]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=False)

DeepSeek-R1-Distill Fine-tuning

DeepSeek-R1-Distill models (Qwen-7B, Qwen-14B, Llama-8B, Llama-70B) are reasoning-focused. Fine-tune to adapt their chain-of-thought style to your domain.

# DeepSeek-R1-Distill variants
MODEL_NAME = "deepseek-ai/DeepSeek-R1-Distill-Qwen-7B"   # 7B on Qwen2.5 base
# MODEL_NAME = "deepseek-ai/DeepSeek-R1-Distill-Llama-8B" # 8B on Llama3 base
# MODEL_NAME = "deepseek-ai/DeepSeek-R1-Distill-Qwen-14B" # 14B (needs A100)

model = AutoModelForCausalLM.from_pretrained(
    MODEL_NAME,
    quantization_config=bnb_config,
    device_map="auto",
    trust_remote_code=True,
    attn_implementation="flash_attention_2",
)

# DeepSeek-R1 uses <think>...</think> tags for reasoning
# Keep this in training data to preserve chain-of-thought capability
example_format = """<|im_start|>user
Solve: What is 15 * 23?<|im_end|>
<|im_start|>assistant
<think>
15 * 23 = 15 * 20 + 15 * 3 = 300 + 45 = 345
</think>
The answer is 345.<|im_end|>"""

# LoRA target modules for DeepSeek-R1-Distill (Qwen2.5 base)
lora_config = LoraConfig(
    r=32,
    lora_alpha=16,
    target_modules=["q_proj", "k_proj", "v_proj", "o_proj",
                    "gate_proj", "up_proj", "down_proj"],
    use_dora=True,
    task_type="CAUSAL_LM",
)

Using Axolotl (Easier)

Axolotl simplifies fine-tuning with YAML configs:

pip install axolotl

# Create config
cat > config.yml << 'EOF'
base_model: mistralai/Mistral-7B-v0.1
model_type: MistralForCausalLM
tokenizer_type: LlamaTokenizer

load_in_4bit: true
adapter: qlora
lora_r: 32
lora_alpha: 16

datasets:
  - path: your_data.json
    type: alpaca

sequence_len: 4096
sample_packing: true

gradient_accumulation_steps: 4
micro_batch_size: 2
num_epochs: 3
learning_rate: 2e-4

output_dir: ./output
EOF

# Train
accelerate launch -m axolotl.cli.train config.yml

Axolotl Config Examples

Chat Model

base_model: mistralai/Mistral-7B-Instruct-v0.2
load_in_4bit: true
adapter: qlora

datasets:
  - path: data.json
    type: sharegpt

chat_template: mistral

Code Model

base_model: codellama/CodeLlama-7b-hf
load_in_4bit: true
adapter: qlora

datasets:
  - path: code_data.json
    type: alpaca

sequence_len: 8192  # Longer context for code

Merging LoRA Weights

After training, merge LoRA back into base model:

from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load base model
base_model = AutoModelForCausalLM.from_pretrained(
    "mistralai/Mistral-7B-v0.1",
    torch_dtype=torch.float16,
    device_map="auto",
)

# Load LoRA
model = PeftModel.from_pretrained(base_model, "./output/final")

# Merge
merged_model = model.merge_and_unload()

# Save merged model
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

Convert to GGUF

For use with llama.cpp/Ollama:

# Clone llama.cpp
git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp

# Convert
python convert.py ../merged_model --outtype f16 --outfile model-f16.gguf

# Quantize
./quantize model-f16.gguf model-q4_k_m.gguf q4_k_m

Monitoring Training

Weights & Biases

import wandb
wandb.init(project="llm-finetune", name="mistral-7b-lora")

TensorBoard

# In training args
report_to="tensorboard"
logging_dir="./logs"

# View
tensorboard --logdir ./logs --port 6006 --bind_all

Best Practices

Hyperparameters

Parameter	7B Model	13B Model	70B Model
batch_size	4	2	1
grad_accum	4	8	16
lr	2e-4	1e-4	5e-5
lora_r	64	32	16
epochs	3	2-3	1-2

Dataset Size

• Minimum: 1,000 examples

• Good: 10,000+ examples

• Quality > Quantity

Avoiding Overfitting

training_args = TrainingArguments(
    ...
    weight_decay=0.01,
    warmup_ratio=0.03,
    save_total_limit=3,
    load_best_model_at_end=True,
    evaluation_strategy="steps",
    eval_steps=100,
)

Multi-GPU Training

# With accelerate
accelerate launch --multi_gpu --num_processes 4 train.py

# With DeepSpeed
accelerate launch --use_deepspeed --num_processes 4 train.py

DeepSpeed config:

{
  "bf16": {"enabled": true},
  "zero_optimization": {
    "stage": 2,
    "offload_optimizer": {"device": "cpu"}
  }
}

Saving & Exporting

# Save LoRA adapter
trainer.save_model("./lora_adapter")

# Save merged model
merged_model.save_pretrained("./full_model")

# Upload to HuggingFace
huggingface-cli login
merged_model.push_to_hub("username/my-model")

Troubleshooting

OOM Errors

• Reduce batch size

• Increase gradient accumulation

• Use gradient_checkpointing=True

• Reduce lora_r

Training Loss Not Decreasing

• Check data format

• Increase learning rate

• Check for data issues

NaN Loss

• Reduce learning rate

• Use fp32 instead of fp16

• Check for corrupted data

Cost Estimate

Typical CLORE.AI marketplace rates (as of 2026):

GPU	Hourly Rate	Daily Rate	4-Hour Session
RTX 3060	~$0.03	~$0.70	~$0.12
RTX 3090	~$0.06	~$1.50	~$0.25
RTX 4090	~$0.10	~$2.30	~$0.40
A100 40GB	~$0.17	~$4.00	~$0.70
A100 80GB	~$0.25	~$6.00	~$1.00

Prices vary by provider and demand. Check CLORE.AI Marketplace for current rates.

📚 See also: How to Fine-Tune LLaMA 3 on a Cloud GPU — Step-by-Step Guide

Save money:

• Use Spot market for flexible workloads (often 30-50% cheaper)

• Pay with CLORE tokens

• Compare prices across different providers