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Use when fine-tuning large models (7B-70B) with limited GPU memory, when you need to train \u003C1% of parameters with minimal accuracy loss, or for multi-adapter serving. HuggingFace's official library integrated with transformers ecosystem.","endman100\u002Fskill-router-nexus","endman100","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F31997019?v=4","01M6KPDX17BYDWW4RRB0TNGGMX","01M6KPDX64Y2QBEWVFG5W5PWPF",0,"MIT","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FAI-Training\u002Fpeft\u002FSKILL.md",[],[18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38],"tuning","fine tuning","peft fine tuning","PEFT","methods","when","accuracy","for","orchestra","you","ecosystem","Bitsandbytes","Memory Optimization","adapter","llms","minimal","Transformers","Optimization","library","official","memory","2026-11-10 10:12:30",false,"---\nname: peft-fine-tuning\ndescription: Parameter-efficient fine-tuning for LLMs using LoRA, QLoRA, and 25+ methods. Use when fine-tuning large models (7B-70B) with limited GPU memory, when you need to train \u003C1% of parameters with minimal accuracy loss, or for multi-adapter serving. HuggingFace's official library integrated with transformers ecosystem.\nversion: 1.0.0\nauthor: Orchestra Research\nlicense: MIT\ntags: [Fine-Tuning, PEFT, LoRA, QLoRA, Parameter-Efficient, Adapters, Low-Rank, Memory Optimization, Multi-Adapter]\ndependencies: [peft>=0.13.0, transformers>=4.45.0, torch>=2.0.0, bitsandbytes>=0.43.0]\n---\n\n# PEFT (Parameter-Efficient Fine-Tuning)\n\nFine-tune LLMs by training \u003C1% of parameters using LoRA, QLoRA, and 25+ adapter methods.\n\n## When to use PEFT\n\n**Use PEFT\u002FLoRA when:**\n- Fine-tuning 7B-70B models on consumer GPUs (RTX 4090, A100)\n- Need to train \u003C1% parameters (6MB adapters vs 14GB full model)\n- Want fast iteration with multiple task-specific adapters\n- Deploying multiple fine-tuned variants from one base model\n\n**Use QLoRA (PEFT + quantization) when:**\n- Fine-tuning 70B models on single 24GB GPU\n- Memory is the primary constraint\n- Can accept ~5% quality trade-off vs full fine-tuning\n\n**Use full fine-tuning instead when:**\n- Training small models (\u003C1B parameters)\n- Need maximum quality and have compute budget\n- Significant domain shift requires updating all weights\n\n## Quick start\n\n### Installation\n\n```bash\n# Basic installation\npip install peft\n\n# With quantization support (recommended)\npip install peft bitsandbytes\n\n# Full stack\npip install peft transformers accelerate bitsandbytes datasets\n```\n\n### LoRA fine-tuning (standard)\n\n```python\nfrom transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments, Trainer\nfrom peft import get_peft_model, LoraConfig, TaskType\nfrom datasets import load_dataset\n\n# Load base model\nmodel_name = \"meta-llama\u002FLlama-3.1-8B\"\nmodel = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=\"auto\", device_map=\"auto\")\ntokenizer = AutoTokenizer.from_pretrained(model_name)\ntokenizer.pad_token = tokenizer.eos_token\n\n# LoRA configuration\nlora_config = LoraConfig(\n    task_type=TaskType.CAUSAL_LM,\n    r=16,                          # Rank (8-64, higher = more capacity)\n    lora_alpha=32,                 # Scaling factor (typically 2*r)\n    lora_dropout=0.05,             # Dropout for regularization\n    target_modules=[\"q_proj\", \"v_proj\", \"k_proj\", \"o_proj\"],  # Attention layers\n    bias=\"none\"                    # Don't train biases\n)\n\n# Apply LoRA\nmodel = get_peft_model(model, lora_config)\nmodel.print_trainable_parameters()\n# Output: trainable params: 13,631,488 || all params: 8,043,307,008 || trainable%: 0.17%\n\n# Prepare dataset\ndataset = load_dataset(\"databricks\u002Fdatabricks-dolly-15k\", split=\"train\")\n\ndef tokenize(example):\n    text = f\"### Instruction:\\n{example['instruction']}\\n\\n### Response:\\n{example['response']}\"\n    return tokenizer(text, truncation=True, max_length=512, padding=\"max_length\")\n\ntokenized = dataset.map(tokenize, remove_columns=dataset.column_names)\n\n# Training\ntraining_args = TrainingArguments(\n    output_dir=\".\u002Flora-llama\",\n    num_train_epochs=3,\n    per_device_train_batch_size=4,\n    gradient_accumulation_steps=4,\n    learning_rate=2e-4,\n    fp16=True,\n    logging_steps=10,\n    save_strategy=\"epoch\"\n)\n\ntrainer = Trainer(\n    model=model,\n    args=training_args,\n    train_dataset=tokenized,\n    data_collator=lambda data: {\"input_ids\": torch.stack([f[\"input_ids\"] for f in data]),\n                                 \"attention_mask\": torch.stack([f[\"attention_mask\"] for f in data]),\n                                 \"labels\": torch.stack([f[\"input_ids\"] for f in data])}\n)\n\ntrainer.train()\n\n# Save adapter only (6MB vs 16GB)\nmodel.save_pretrained(\".\u002Flora-llama-adapter\")\n```\n\n### QLoRA fine-tuning (memory-efficient)\n\n```python\nfrom transformers import AutoModelForCausalLM, BitsAndBytesConfig\nfrom peft import get_peft_model, LoraConfig, prepare_model_for_kbit_training\n\n# 4-bit quantization config\nbnb_config = BitsAndBytesConfig(\n    load_in_4bit=True,\n    bnb_4bit_quant_type=\"nf4\",           # NormalFloat4 (best for LLMs)\n    bnb_4bit_compute_dtype=\"bfloat16\",   # Compute in bf16\n    bnb_4bit_use_double_quant=True       # Nested quantization\n)\n\n# Load quantized model\nmodel = AutoModelForCausalLM.from_pretrained(\n    \"meta-llama\u002FLlama-3.1-70B\",\n    quantization_config=bnb_config,\n    device_map=\"auto\"\n)\n\n# Prepare for training (enables gradient checkpointing)\nmodel = prepare_model_for_kbit_training(model)\n\n# LoRA config for QLoRA\nlora_config = LoraConfig(\n    r=64,                              # Higher rank for 70B\n    lora_alpha=128,\n    lora_dropout=0.1,\n    target_modules=[\"q_proj\", \"v_proj\", \"k_proj\", \"o_proj\", \"gate_proj\", \"up_proj\", \"down_proj\"],\n    bias=\"none\",\n    task_type=\"CAUSAL_LM\"\n)\n\nmodel = get_peft_model(model, lora_config)\n# 70B model now fits on single 24GB GPU!\n```\n\n## LoRA parameter selection\n\n### Rank (r) - capacity vs efficiency\n\n| Rank | Trainable Params | Memory | Quality | Use Case |\n|------|-----------------|--------|---------|----------|\n| 4 | ~3M | Minimal | Lower | Simple tasks, prototyping |\n| **8** | ~7M | Low | Good | **Recommended starting point** |\n| **16** | ~14M | Medium | Better | **General fine-tuning** |\n| 32 | ~27M | Higher | High | Complex tasks |\n| 64 | ~54M | High | Highest | Domain adaptation, 70B models |\n\n### Alpha (lora_alpha) - scaling factor\n\n```python\n# Rule of thumb: alpha = 2 * rank\nLoraConfig(r=16, lora_alpha=32)  # Standard\nLoraConfig(r=16, lora_alpha=16)  # Conservative (lower learning rate effect)\nLoraConfig(r=16, lora_alpha=64)  # Aggressive (higher learning rate effect)\n```\n\n### Target modules by architecture\n\n```python\n# Llama \u002F Mistral \u002F Qwen\ntarget_modules = [\"q_proj\", \"v_proj\", \"k_proj\", \"o_proj\", \"gate_proj\", \"up_proj\", \"down_proj\"]\n\n# GPT-2 \u002F GPT-Neo\ntarget_modules = [\"c_attn\", \"c_proj\", \"c_fc\"]\n\n# Falcon\ntarget_modules = [\"query_key_value\", \"dense\", \"dense_h_to_4h\", \"dense_4h_to_h\"]\n\n# BLOOM\ntarget_modules = [\"query_key_value\", \"dense\", \"dense_h_to_4h\", \"dense_4h_to_h\"]\n\n# Auto-detect all linear layers\ntarget_modules = \"all-linear\"  # PEFT 0.6.0+\n```\n\n## Loading and merging adapters\n\n### Load trained adapter\n\n```python\nfrom peft import PeftModel, AutoPeftModelForCausalLM\nfrom transformers import AutoModelForCausalLM\n\n# Option 1: Load with PeftModel\nbase_model = AutoModelForCausalLM.from_pretrained(\"meta-llama\u002FLlama-3.1-8B\")\nmodel = PeftModel.from_pretrained(base_model, \".\u002Flora-llama-adapter\")\n\n# Option 2: Load directly (recommended)\nmodel = AutoPeftModelForCausalLM.from_pretrained(\n    \".\u002Flora-llama-adapter\",\n    device_map=\"auto\"\n)\n```\n\n### Merge adapter into base model\n\n```python\n# Merge for deployment (no adapter overhead)\nmerged_model = model.merge_and_unload()\n\n# Save merged model\nmerged_model.save_pretrained(\".\u002Fllama-merged\")\ntokenizer.save_pretrained(\".\u002Fllama-merged\")\n\n# Push to Hub\nmerged_model.push_to_hub(\"username\u002Fllama-finetuned\")\n```\n\n### Multi-adapter serving\n\n```python\nfrom peft import PeftModel\n\n# Load base with first adapter\nmodel = AutoPeftModelForCausalLM.from_pretrained(\".\u002Fadapter-task1\")\n\n# Load additional adapters\nmodel.load_adapter(\".\u002Fadapter-task2\", adapter_name=\"task2\")\nmodel.load_adapter(\".\u002Fadapter-task3\", adapter_name=\"task3\")\n\n# Switch between adapters at runtime\nmodel.set_adapter(\"task1\")  # Use task1 adapter\noutput1 = model.generate(**inputs)\n\nmodel.set_adapter(\"task2\")  # Switch to task2\noutput2 = model.generate(**inputs)\n\n# Disable adapters (use base model)\nwith model.disable_adapter():\n    base_output = model.generate(**inputs)\n```\n\n## PEFT methods comparison\n\n| Method | Trainable % | Memory | Speed | Best For |\n|--------|------------|--------|-------|----------|\n| **LoRA** | 0.1-1% | Low | Fast | General fine-tuning |\n| **QLoRA** | 0.1-1% | Very Low | Medium | Memory-constrained |\n| AdaLoRA | 0.1-1% | Low | Medium | Automatic rank selection |\n| IA3 | 0.01% | Minimal | Fastest | Few-shot adaptation |\n| Prefix Tuning | 0.1% | Low | Medium | Generation control |\n| Prompt Tuning | 0.001% | Minimal | Fast | Simple task adaptation |\n| P-Tuning v2 | 0.1% | Low | Medium | NLU tasks |\n\n### IA3 (minimal parameters)\n\n```python\nfrom peft import IA3Config\n\nia3_config = IA3Config(\n    target_modules=[\"q_proj\", \"v_proj\", \"k_proj\", \"down_proj\"],\n    feedforward_modules=[\"down_proj\"]\n)\nmodel = get_peft_model(model, ia3_config)\n# Trains only 0.01% of parameters!\n```\n\n### Prefix Tuning\n\n```python\nfrom peft import PrefixTuningConfig\n\nprefix_config = PrefixTuningConfig(\n    task_type=\"CAUSAL_LM\",\n    num_virtual_tokens=20,      # Prepended tokens\n    prefix_projection=True       # Use MLP projection\n)\nmodel = get_peft_model(model, prefix_config)\n```\n\n## Integration patterns\n\n### With TRL (SFTTrainer)\n\n```python\nfrom trl import SFTTrainer, SFTConfig\nfrom peft import LoraConfig\n\nlora_config = LoraConfig(r=16, lora_alpha=32, target_modules=\"all-linear\")\n\ntrainer = SFTTrainer(\n    model=model,\n    args=SFTConfig(output_dir=\".\u002Foutput\", max_seq_length=512),\n    train_dataset=dataset,\n    peft_config=lora_config,  # Pass LoRA config directly\n)\ntrainer.train()\n```\n\n### With Axolotl (YAML config)\n\n```yaml\n# axolotl config.yaml\nadapter: lora\nlora_r: 16\nlora_alpha: 32\nlora_dropout: 0.05\nlora_target_modules:\n  - q_proj\n  - v_proj\n  - k_proj\n  - o_proj\nlora_target_linear: true  # Target all linear layers\n```\n\n### With vLLM (inference)\n\n```python\nfrom vllm import LLM\nfrom vllm.lora.request import LoRARequest\n\n# Load base model with LoRA support\nllm = LLM(model=\"meta-llama\u002FLlama-3.1-8B\", enable_lora=True)\n\n# Serve with adapter\noutputs = llm.generate(\n    prompts,\n    lora_request=LoRARequest(\"adapter1\", 1, \".\u002Flora-adapter\")\n)\n```\n\n## Performance benchmarks\n\n### Memory usage (Llama 3.1 8B)\n\n| Method | GPU Memory | Trainable Params |\n|--------|-----------|------------------|\n| Full fine-tuning | 60+ GB | 8B (100%) |\n| LoRA r=16 | 18 GB | 14M (0.17%) |\n| QLoRA r=16 | 6 GB | 14M (0.17%) |\n| IA3 | 16 GB | 800K (0.01%) |\n\n### Training speed (A100 80GB)\n\n| Method | Tokens\u002Fsec | vs Full FT |\n|--------|-----------|------------|\n| Full FT | 2,500 | 1x |\n| LoRA | 3,200 | 1.3x |\n| QLoRA | 2,100 | 0.84x |\n\n### Quality (MMLU benchmark)\n\n| Model | Full FT | LoRA | QLoRA |\n|-------|---------|------|-------|\n| Llama 2-7B | 45.3 | 44.8 | 44.1 |\n| Llama 2-13B | 54.8 | 54.2 | 53.5 |\n\n## Common issues\n\n### CUDA OOM during training\n\n```python\n# Solution 1: Enable gradient checkpointing\nmodel.gradient_checkpointing_enable()\n\n# Solution 2: Reduce batch size + increase accumulation\nTrainingArguments(\n    per_device_train_batch_size=1,\n    gradient_accumulation_steps=16\n)\n\n# Solution 3: Use QLoRA\nfrom transformers import BitsAndBytesConfig\nbnb_config = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_quant_type=\"nf4\")\n```\n\n### Adapter not applying\n\n```python\n# Verify adapter is active\nprint(model.active_adapters)  # Should show adapter name\n\n# Check trainable parameters\nmodel.print_trainable_parameters()\n\n# Ensure model in training mode\nmodel.train()\n```\n\n### Quality degradation\n\n```python\n# Increase rank\nLoraConfig(r=32, lora_alpha=64)\n\n# Target more modules\ntarget_modules = \"all-linear\"\n\n# Use more training data and epochs\nTrainingArguments(num_train_epochs=5)\n\n# Lower learning rate\nTrainingArguments(learning_rate=1e-4)\n```\n\n## Best practices\n\n1. **Start with r=8-16**, increase if quality insufficient\n2. **Use alpha = 2 * rank** as starting point\n3. **Target attention + MLP layers** for best quality\u002Fefficiency\n4. **Enable gradient checkpointing** for memory savings\n5. **Save adapters frequently** (small files, easy rollback)\n6. **Evaluate on held-out data** before merging\n7. **Use QLoRA for 70B+ models** on consumer hardware\n\n## References\n\n- **[Advanced Usage](references\u002Fadvanced-usage.md)** - DoRA, LoftQ, rank stabilization, custom modules\n- **[Troubleshooting](references\u002Ftroubleshooting.md)** - Common errors, debugging, optimization\n\n## Resources\n\n- **GitHub**: https:\u002F\u002Fgithub.com\u002Fhuggingface\u002Fpeft\n- **Docs**: https:\u002F\u002Fhuggingface.co\u002Fdocs\u002Fpeft\n- **LoRA Paper**: arXiv:2106.09685\n- **QLoRA Paper**: arXiv:2305.14314\n- **Models**: https:\u002F\u002Fhuggingface.co\u002Fmodels?library=peft\n","# PEFT (Parameter-Efficient Fine-Tuning)\n\nFine-tune LLMs by training \u003C1% of parameters using LoRA, QLoRA, and 25+ adapter methods.\n\n## When to use PEFT\n\n**Use PEFT\u002FLoRA when:**\n- Fine-tuning 7B-70B models on consumer GPUs (RTX 4090, A100)\n- Need to train \u003C1% parameters (6MB adapters vs 14GB full model)\n- Want fast iteration with multiple task-specific adapters\n- Deploying multiple fine-tuned variants from one base model\n\n**Use QLoRA (PEFT + quantization) when:**\n- Fine-tuning 70B models on single 24GB GPU\n- Memory is the primary constraint\n- Can accept ~5% quality trade-off vs full fine-tuning\n\n**Use full fine-tuning instead when:**\n- Training small models (\u003C1B parameters)\n- Need maximum quality and have compute budget\n- Significant domain shift requires updating all weights\n\n## Quick start\n\n### Installation\n\n```bash\n# Basic installation\npip install peft\n\n# With quantization support (recommended)\npip install peft bitsandbytes\n\n# Full stack\npip install peft transformers accelerate bitsandbytes datasets\n```\n\n### LoRA fine-tuning (standard)\n\n```python\nfrom transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments, Trainer\nfrom peft import get_peft_model, LoraConfig, TaskType\nfrom datasets import load_dataset\n\n# Load base model\nmodel_name = \"meta-llama\u002FLlama-3.1-8B\"\nmodel = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=\"auto\", device_map=\"auto\")\ntokenizer = AutoTokenizer.from_pretrained(model_name)\ntokenizer.pad_token = tokenizer.eos_token\n\n# LoRA configuration\nlora_config = LoraConfig(\n    task_type=TaskType.CAUSAL_LM,\n    r=16,                          # Rank (8-64, higher = more capacity)\n    lora_alpha=32,                 # Scaling factor (typically 2*r)\n    lora_dropout=0.05,             # Dropout for regularization\n    target_modules=[\"q_proj\", \"v_proj\", \"k_proj\", \"o_proj\"],  # Attention layers\n    bias=\"none\"                    # Don't train biases\n)\n\n# Apply LoRA\nmodel = get_peft_model(model, lora_config)\nmodel.print_trainable_parameters()\n# Output: trainable params: 13,631,488 || all params: 8,043,307,008 || trainable%: 0.17%\n\n# Prepare dataset\ndataset = load_dataset(\"databricks\u002Fdatabricks-dolly-15k\", split=\"train\")\n\ndef tokenize(example):\n    text = f\"### Instruction:\\n{example['instruction']}\\n\\n### Response:\\n{example['response']}\"\n    return tokenizer(text, truncation=True, max_length=512, padding=\"max_length\")\n\ntokenized = dataset.map(tokenize, remove_columns=dataset.column_names)\n\n# Training\ntraining_args = TrainingArguments(\n    output_dir=\".\u002Flora-llama\",\n    num_train_epochs=3,\n    per_device_train_batch_size=4,\n    gradient_accumulation_steps=4,\n    learning_rate=2e-4,\n    fp16=True,\n    logging_steps=10,\n    save_strategy=\"epoch\"\n)\n\ntrainer = Trainer(\n    model=model,\n    args=training_args,\n    train_dataset=tokenized,\n    data_collator=lambda data: {\"input_ids\": torch.stack([f[\"input_ids\"] for f in data]),\n                                 \"attention_mask\": torch.stack([f[\"attention_mask\"] for f in data]),\n                                 \"labels\": torch.stack([f[\"input_ids\"] for f in data])}\n)\n\ntrainer.train()\n\n# Save adapter only (6MB vs 16GB)\nmodel.save_pretrained(\".\u002Flora-llama-adapter\")\n```\n\n### QLoRA fine-tuning (memory-efficient)\n\n```python\nfrom transformers import AutoModelForCausalLM, BitsAndBytesConfig\nfrom peft import get_peft_model, LoraConfig, prepare_model_for_kbit_training\n\n# 4-bit quantization config\nbnb_config = BitsAndBytesConfig(\n    load_in_4bit=True,\n    bnb_4bit_quant_type=\"nf4\",           # NormalFloat4 (best for LLMs)\n    bnb_4bit_compute_dtype=\"bfloat16\",   # Compute in bf16\n    bnb_4bit_use_double_quant=True       # Nested quantization\n)\n\n# Load quantized model\nmodel = AutoModelForCausalLM.from_pretrained(\n    \"meta-llama\u002FLlama-3.1-70B\",\n    quantization_config=bnb_config,\n    device_map=\"auto\"\n)\n\n# Prepare for training (enables gradient checkpointing)\nmodel = prepare_model_for_kbit_training(model)\n\n# LoRA config for QLoRA\nlora_config = LoraConfig(\n    r=64,                              # Higher rank for 70B\n    lora_alpha=128,\n    lora_dropout=0.1,\n    target_modules=[\"q_proj\", \"v_proj\", \"k_proj\", \"o_proj\", \"gate_proj\", \"up_proj\", \"down_proj\"],\n    bias=\"none\",\n    task_type=\"CAUSAL_LM\"\n)\n\nmodel = get_peft_model(model, lora_config)\n# 70B model now fits on single 24GB GPU!\n```\n\n## LoRA parameter selection\n\n### Rank (r) - capacity vs efficiency\n\n| Rank | Trainable Params | Memory | Quality | Use Case |\n|------|-----------------|--------|---------|----------|\n| 4 | ~3M | Minimal | Lower | Simple tasks, prototyping |\n| **8** | ~7M | Low | Good | **Recommended starting point** |\n| **16** | ~14M | Medium | Better | **General fine-tuning** |\n| 32 | ~27M | Higher | High | Complex tasks |\n| 64 | ~54M | High | Highest | Domain adaptation, 70B models |\n\n### Alpha (lora_alpha) - scaling factor\n\n```python\n# Rule of thumb: alpha = 2 * rank\nLoraConfig(r=16, lora_alpha=32)  # Standard\nLoraConfig(r=16, lora_alpha=16)  # Conservative (lower learning rate effect)\nLoraConfig(r=16, lora_alpha=64)  # Aggressive (higher learning rate effect)\n```\n\n### Target modules by architecture\n\n```python\n# Llama \u002F Mistral \u002F Qwen\ntarget_modules = [\"q_proj\", \"v_proj\", \"k_proj\", \"o_proj\", \"gate_proj\", \"up_proj\", \"down_proj\"]\n\n# GPT-2 \u002F GPT-Neo\ntarget_modules = [\"c_attn\", \"c_proj\", \"c_fc\"]\n\n# Falcon\ntarget_modules = [\"query_key_value\", \"dense\", \"dense_h_to_4h\", \"dense_4h_to_h\"]\n\n# BLOOM\ntarget_modules = [\"query_key_value\", \"dense\", \"dense_h_to_4h\", \"dense_4h_to_h\"]\n\n# Auto-detect all linear layers\ntarget_modules = \"all-linear\"  # PEFT 0.6.0+\n```\n\n## Loading and merging adapters\n\n### Load trained adapter\n\n```python\nfrom peft import PeftModel, AutoPeftModelForCausalLM\nfrom transformers import AutoModelForCausalLM\n\n# Option 1: Load with PeftModel\nbase_model = AutoModelForCausalLM.from_pretrained(\"meta-llama\u002FLlama-3.1-8B\")\nmodel = PeftModel.from_pretrained(base_model, \".\u002Flora-llama-adapter\")\n\n# Option 2: Load directly (recommended)\nmodel = AutoPeftModelForCausalLM.from_pretrained(\n    \".\u002Flora-llama-adapter\",\n    device_map=\"auto\"\n)\n```\n\n### Merge adapter into base model\n\n```python\n# Merge for deployment (no adapter overhead)\nmerged_model = model.merge_and_unload()\n\n# Save merged model\nmerged_model.save_pretrained(\".\u002Fllama-merged\")\ntokenizer.save_pretrained(\".\u002Fllama-merged\")\n\n# Push to Hub\nmerged_model.push_to_hub(\"username\u002Fllama-finetuned\")\n```\n\n### Multi-adapter serving\n\n```python\nfrom peft import PeftModel\n\n# Load base with first adapter\nmodel = AutoPeftModelForCausalLM.from_pretrained(\".\u002Fadapter-task1\")\n\n# Load additional adapters\nmodel.load_adapter(\".\u002Fadapter-task2\", adapter_name=\"task2\")\nmodel.load_adapter(\".\u002Fadapter-task3\", adapter_name=\"task3\")\n\n# Switch between adapters at runtime\nmodel.set_adapter(\"task1\")  # Use task1 adapter\noutput1 = model.generate(**inputs)\n\nmodel.set_adapter(\"task2\")  # Switch to task2\noutput2 = model.generate(**inputs)\n\n# Disable adapters (use base model)\nwith model.disable_adapter():\n    base_output = model.generate(**inputs)\n```\n\n## PEFT methods comparison\n\n| Method | Trainable % | Memory | Speed | Best For |\n|--------|------------|--------|-------|----------|\n| **LoRA** | 0.1-1% | Low | Fast | General fine-tuning |\n| **QLoRA** | 0.1-1% | Very Low | Medium | Memory-constrained |\n| AdaLoRA | 0.1-1% | Low | Medium | Automatic rank selection |\n| IA3 | 0.01% | Minimal | Fastest | Few-shot adaptation |\n| Prefix Tuning | 0.1% | Low | Medium | Generation control |\n| Prompt Tuning | 0.001% | Minimal | Fast | Simple task adaptation |\n| P-Tuning v2 | 0.1% | Low | Medium | NLU tasks |\n\n### IA3 (minimal parameters)\n\n```python\nfrom peft import IA3Config\n\nia3_config = IA3Config(\n    target_modules=[\"q_proj\", \"v_proj\", \"k_proj\", \"down_proj\"],\n    feedforward_modules=[\"down_proj\"]\n)\nmodel = get_peft_model(model, ia3_config)\n# Trains only 0.01% of parameters!\n```\n\n### Prefix Tuning\n\n```python\nfrom peft import PrefixTuningConfig\n\nprefix_config = PrefixTuningConfig(\n    task_type=\"CAUSAL_LM\",\n    num_virtual_tokens=20,      # Prepended tokens\n    prefix_projection=True       # Use MLP projection\n)\nmodel = get_peft_model(model, prefix_config)\n```\n\n## Integration patterns\n\n### With TRL (SFTTrainer)\n\n```python\nfrom trl import SFTTrainer, SFTConfig\nfrom peft import LoraConfig\n\nlora_config = LoraConfig(r=16, lora_alpha=32, target_modules=\"all-linear\")\n\ntrainer = SFTTrainer(\n    model=model,\n    args=SFTConfig(output_dir=\".\u002Foutput\", max_seq_length=512),\n    train_dataset=dataset,\n    peft_config=lora_config,  # Pass LoRA config directly\n)\ntrainer.train()\n```\n\n### With Axolotl (YAML config)\n\n```yaml\n# axolotl config.yaml\nadapter: lora\nlora_r: 16\nlora_alpha: 32\nlora_dropout: 0.05\nlora_target_modules:\n  - q_proj\n  - v_proj\n  - k_proj\n  - o_proj\nlora_target_linear: true  # Target all linear layers\n```\n\n### With vLLM (inference)\n\n```python\nfrom vllm import LLM\nfrom vllm.lora.request import LoRARequest\n\n# Load base model with LoRA support\nllm = LLM(model=\"meta-llama\u002FLlama-3.1-8B\", enable_lora=True)\n\n# Serve with adapter\noutputs = llm.generate(\n    prompts,\n    lora_request=LoRARequest(\"adapter1\", 1, \".\u002Flora-adapter\")\n)\n```\n\n## Performance benchmarks\n\n### Memory usage (Llama 3.1 8B)\n\n| Method | GPU Memory | Trainable Params |\n|--------|-----------|------------------|\n| Full fine-tuning | 60+ GB | 8B (100%) |\n| LoRA r=16 | 18 GB | 14M (0.17%) |\n| QLoRA r=16 | 6 GB | 14M (0.17%) |\n| IA3 | 16 GB | 800K (0.01%) |\n\n### Training speed (A100 80GB)\n\n| Method | Tokens\u002Fsec | vs Full FT |\n|--------|-----------|------------|\n| Full FT | 2,500 | 1x |\n| LoRA | 3,200 | 1.3x |\n| QLoRA | 2,100 | 0.84x |\n\n### Quality (MMLU benchmark)\n\n| Model | Full FT | LoRA | QLoRA |\n|-------|---------|------|-------|\n| Llama 2-7B | 45.3 | 44.8 | 44.1 |\n| Llama 2-13B | 54.8 | 54.2 | 53.5 |\n\n## Common issues\n\n### CUDA OOM during training\n\n```python\n# Solution 1: Enable gradient checkpointing\nmodel.gradient_checkpointing_enable()\n\n# Solution 2: Reduce batch size + increase accumulation\nTrainingArguments(\n    per_device_train_batch_size=1,\n    gradient_accumulation_steps=16\n)\n\n# Solution 3: Use QLoRA\nfrom transformers import BitsAndBytesConfig\nbnb_config = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_quant_type=\"nf4\")\n```\n\n### Adapter not applying\n\n```python\n# Verify adapter is active\nprint(model.active_adapters)  # Should show adapter name\n\n# Check trainable parameters\nmodel.print_trainable_parameters()\n\n# Ensure model in training mode\nmodel.train()\n```\n\n### Quality degradation\n\n```python\n# Increase rank\nLoraConfig(r=32, lora_alpha=64)\n\n# Target more modules\ntarget_modules = \"all-linear\"\n\n# Use more training data and epochs\nTrainingArguments(num_train_epochs=5)\n\n# Lower learning rate\nTrainingArguments(learning_rate=1e-4)\n```\n\n## Best practices\n\n1. **Start with r=8-16**, increase if quality insufficient\n2. **Use alpha = 2 * rank** as starting point\n3. **Target attention + MLP layers** for best quality\u002Fefficiency\n4. **Enable gradient checkpointing** for memory savings\n5. **Save adapters frequently** (small files, easy rollback)\n6. **Evaluate on held-out data** before merging\n7. **Use QLoRA for 70B+ models** on consumer hardware\n\n## References\n\n- **[Advanced Usage](references\u002Fadvanced-usage.md)** - DoRA, LoftQ, rank stabilization, custom modules\n- **[Troubleshooting](references\u002Ftroubleshooting.md)** - Common errors, debugging, optimization\n\n## Resources\n\n- **GitHub**: https:\u002F\u002Fgithub.com\u002Fhuggingface\u002Fpeft\n- **Docs**: https:\u002F\u002Fhuggingface.co\u002Fdocs\u002Fpeft\n- **LoRA Paper**: arXiv:2106.09685\n- **QLoRA Paper**: arXiv:2305.14314\n- **Models**: https:\u002F\u002Fhuggingface.co\u002Fmodels?library=peft\n",{"name":5,"tags":44,"author":52,"license":14,"version":53,"description":7,"dependencies":54},[45,21,46,47,48,49,50,30,51],"Fine-Tuning","LoRA","QLoRA","Parameter-Efficient","Adapters","Low-Rank","Multi-Adapter","Orchestra Research","1.0.0",[55,56,57,58],"peft>=0.13.0","transformers>=4.45.0","torch>=2.0.0","bitsandbytes>=0.43.0","main","f19b254e8975d0bfddb394686706106930d7964d","AI-Training\u002Fpeft","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus","https:\u002F\u002Fgithub.com\u002Fendman100","2026-11-11 18:19:10",{"source":8,"skillName":5,"commands":66,"prompt":76,"zipAvailable":77},[67,70,73],{"tool":68,"command":69},"npx","npx skills add endman100\u002Fskill-router-nexus --skill peft-fine-tuning",{"tool":71,"command":72},"bunx","bunx skills add endman100\u002Fskill-router-nexus --skill peft-fine-tuning",{"tool":74,"command":75},"pnpm","pnpm dlx skills add endman100\u002Fskill-router-nexus --skill peft-fine-tuning","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FAI-Training\u002Fpeft\u002FSKILL.md",true,[79,85],{"path":80,"resourceType":81,"resourceTypeDesc":82,"sizeBytes":83,"sourceHtmlUrl":84},"references\u002Fadvanced-usage.md","REFERENCE","参考文档",12541,"https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FAI-Training\u002Fpeft\u002Freferences\u002Fadvanced-usage.md",{"path":86,"resourceType":81,"resourceTypeDesc":82,"sizeBytes":87,"sourceHtmlUrl":88},"references\u002Ftroubleshooting.md",10344,"https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FAI-Training\u002Fpeft\u002Freferences\u002Ftroubleshooting.md",[90,114,151,173,182,206],{"skillNo":91,"name":92,"nameZh":6,"description":93,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":94,"sourceHtmlUrl":95,"categories":96,"keywords":102,"publishedAt":39},"01M6MPCT5BXVER8HQZTW3QE9AS","create-master","基于佛教经典文献，生成特定高僧大德的 AI 教学角色","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FMetaphysics-and-Spirituality\u002FMaster-skill\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FMetaphysics-and-Spirituality\u002FMaster-skill\u002FSKILL.md",[97],{"categoryNo":98,"categoryName":99,"taxonomyType":100,"taxonomyTypeDesc":101},"01JBSEEDCAT000000000000039","教师与教育","PROFESSION","适用职业",[103,104,105,106,107,108,109,110,111,112,113],"create master","master","create","生成","true","webfetch","edit","write","read","grep","Bash",{"skillNo":115,"name":116,"nameZh":6,"description":117,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":118,"sourceHtmlUrl":119,"categories":120,"keywords":132,"publishedAt":39},"01M6M43AKE95Y64E1JRXEF5QQ3","writing-dna-skill","从至少 20 篇完整文章中蒸馏可复用的写作 DNA，分析语言、文章结构、选题逻辑、素材策略、认知框架和视觉风格，并生成 Writing-DNA.md；按该 DNA 写作时会读取全部蒸馏产物和 5 篇相关原文。用于中英文作者、账号、品牌或出版物的风格分析与一致性写作。 Distill reusable Writing DNA from at least 20 complete articles for Chinese or English authors, publications, brands, and accounts; use for language, structure, topic logic, source strategy, cognitive-frame, and visual-style analysis.","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fwriting-dna-skill\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fwriting-dna-skill\u002FSKILL.md",[121,126,129],{"categoryNo":122,"categoryName":123,"taxonomyType":124,"taxonomyTypeDesc":125},"01JBSEEDCAT000000000000018","写作与文案","SCENARIO","使用场景",{"categoryNo":127,"categoryName":128,"taxonomyType":124,"taxonomyTypeDesc":125},"01JBSEEDCAT000000000000023","Skill与智能体开发",{"categoryNo":130,"categoryName":131,"taxonomyType":100,"taxonomyTypeDesc":101},"01JBSEEDCAT000000000000036","内容创作者",[133,134,135,136,137,138,139,140,141,142,143,144,145,106,146,147,148,25,149,150],"writing dna skill","DNA","writing","strategy","english","logic","visual","structure","chinese","reusable","use","style","distill","analysis","frame","source","at","complete",{"skillNo":152,"name":153,"nameZh":6,"description":154,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":155,"sourceHtmlUrl":156,"categories":157,"keywords":160,"publishedAt":39},"01M6M428TTCGQ4ME3KY99VN037","stop-slop","Remove AI writing patterns from prose. Use when drafting, editing, or reviewing text to eliminate predictable AI tells.","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fstop-slop\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fstop-slop\u002FSKILL.md",[158,159],{"categoryNo":122,"categoryName":123,"taxonomyType":124,"taxonomyTypeDesc":125},{"categoryNo":130,"categoryName":131,"taxonomyType":100,"taxonomyTypeDesc":101},[161,162,163,164,23,165,166,167,168,143,169,170,171,25,172,135],"slop","stop slop","stop","drafting","drafts","https","patterns","prose","remove","editing","text","content",{"skillNo":174,"name":175,"nameZh":6,"description":176,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":177,"sourceHtmlUrl":178,"categories":179,"keywords":180,"publishedAt":39},"01M6M41X0B7EBX42TK435DMGGR","shuorenhua","检查和清理中英文文本里的 AI 套路，适用于“去 AI 味”“说人话”“自然一点”“别像模板”“先标问题”这类改写和审稿需求；按场景控制力度，同时保留事实、术语、语域和责任主体。","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fshuorenhua\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fshuorenhua\u002FSKILL.md",[],[175,181],"文本",{"skillNo":183,"name":184,"nameZh":6,"description":185,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":186,"sourceHtmlUrl":187,"categories":188,"keywords":189,"publishedAt":39},"01M6M3ZD12WBEW5KXKDM4N6DFG","naming","Name products, SaaS, brands, open source projects, bots, and apps. Use when the user needs to name something, find a brand name, or pick a product name. Metaphor-driven process that produces memorable, meaningful names and avoids AI slop.","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fnaming-main\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fnaming-main\u002FSKILL.md",[],[184,190,191,192,193,194,143,195,111,148,196,197,198,199,113,200,201,202,203,23,204,205,161],"repo","websearch","product name","apps","bots","view","brand","pick","gh","projects","open","saas","open source","product","describe","ai slop",{"skillNo":207,"name":208,"nameZh":6,"description":209,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":210,"sourceHtmlUrl":211,"categories":212,"keywords":214,"publishedAt":39},"01M6M3YVXAFAAWJ7F6JMPFREYE","humanizer-zh","去除文本中的 AI 生成痕迹。适用于编辑或审阅文本，使其听起来更自然、更像人类书写。\n基于维基百科的\"AI 写作特征\"综合指南。检测并修复以下模式：夸大的象征意义、\n宣传性语言、以 -ing 结尾的肤浅分析、模糊的归因、破折号过度使用、三段式法则、\nAI 词汇、否定式排比、过多的连接性短语。","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fhumanizer-zh\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fhumanizer-zh\u002FSKILL.md",[213],{"categoryNo":122,"categoryName":123,"taxonomyType":124,"taxonomyTypeDesc":125},[215,216,217,111,110,109,135,106,181,218,161,163],"humanizer","zh","humanizer zh","翻译"]