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<main>
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<h1 id="dpo-training-with-llama-factory"><a class="header" href="#dpo-training-with-llama-factory">DPO Training with LLaMA-Factory</a></h1>
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||
<p>This tutorial demonstrates how to use Direct Preference Optimization (DPO) to fine-tune a language model using the LLaMA-Factory framework. DPO is a method for training models based on human preferences, allowing for more aligned and user-centric outputs.</p>
|
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<h2 id="installation"><a class="header" href="#installation">Installation</a></h2>
|
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<h3 id="step-1-create-a-conda-environment-and-suit-it-for-ktransformers"><a class="header" href="#step-1-create-a-conda-environment-and-suit-it-for-ktransformers">Step 1: Create a conda environment and suit it for KTransformers</a></h3>
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||
<pre><code class="language-Bash">conda create -n Kllama python=3.12 # choose from : [3.11, 3.12, 3.13]
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conda install -y -c conda-forge libstdcxx-ng gcc_impl_linux-64
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conda install -y -c nvidia/label/cuda-12.8.0 cuda-runtime
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||
</code></pre>
|
||
<h3 id="step-2-install-the-llama-factory-environment"><a class="header" href="#step-2-install-the-llama-factory-environment">Step 2: Install the LLaMA-Factory environment</a></h3>
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||
<pre><code class="language-Bash">git clone --depth 1 https://github.com/hiyouga/LLaMA-Factory.git
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||
cd LLaMA-Factory
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||
pip install -e ".[torch,metrics]" --no-build-isolation
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||
</code></pre>
|
||
<h3 id="step-3-install-ktransformers"><a class="header" href="#step-3-install-ktransformers">Step 3: Install KTransformers</a></h3>
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||
<h4 id="option-1-install-the-ktransformers-wheel-that-matches-your-torch-and-python-versions-from-httpsgithubcomkvcache-aiktransformersreleasestagv044"><a class="header" href="#option-1-install-the-ktransformers-wheel-that-matches-your-torch-and-python-versions-from-httpsgithubcomkvcache-aiktransformersreleasestagv044">Option 1: Install the KTransformers wheel that matches your Torch and Python versions, from https://github.com/kvcache-ai/ktransformers/releases/tag/v0.4.4</a></h4>
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<p>(Note: The CUDA version can differ from that in the wheel filename.)</p>
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<pre><code class="language-Bash">pip install ktransformers-0.4.4+cu128torch28fancy-cp312-cp312-linux_x86_64.whl
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</code></pre>
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<h4 id="option-2-install-ktransformers-from-source"><a class="header" href="#option-2-install-ktransformers-from-source">Option 2: Install KTransformers from source</a></h4>
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<pre><code class="language-Bash">git clone --depth 1 https://github.com/kvcache-ai/ktransformers.git
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cd ktransformers/kt-sft
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export TORCH_CUDA_ARCH_LIST="8.0;8.9;9.0" # set according to your GPU
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pip install -r "requirements-sft.txt"
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KTRANSFORMERS_FORCE_BUILD=TRUE pip install -v . --no-build-isolation
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|
||
</code></pre>
|
||
<h3 id="step-4-install-the-flash-attention-wheel-that-matches-your-torch-and-python-versions-from-httpsgithubcomdao-ailabflash-attentionreleases"><a class="header" href="#step-4-install-the-flash-attention-wheel-that-matches-your-torch-and-python-versions-from-httpsgithubcomdao-ailabflash-attentionreleases">Step 4: Install the Flash-attention wheel that matches your Torch and Python versions, from: https://github.com/Dao-AILab/flash-attention/releases</a></h3>
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||
<pre><code class="language-Bash"># abi=True/False can find from below
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# import torch
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# print(torch._C._GLIBCXX_USE_CXX11_ABI)
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|
||
pip install https://github.com/Dao-AILab/flash-attention/releases/download/v2.8.3/flash_attn-2.8.3+cu12torch2.8cxx11abiTRUE-cp312-cp312-linux_x86_64.whl
|
||
</code></pre>
|
||
<h3 id="step-5-optional-if-you-want-to-use-flash_infer-otherwise-it-defaults-to-triton"><a class="header" href="#step-5-optional-if-you-want-to-use-flash_infer-otherwise-it-defaults-to-triton">Step 5: (Optional) If you want to use flash_infer (otherwise it defaults to triton)</a></h3>
|
||
<pre><code class="language-Bash">git clone https://github.com/kvcache-ai/custom_flashinfer.git
|
||
pip install custom_flashinfer/
|
||
</code></pre>
|
||
<h2 id="prepare-models"><a class="header" href="#prepare-models">Prepare Models</a></h2>
|
||
<p>We use <code>deepseek-ai/DeepSeek-V2-Lite</code> as an example here. You can replace it with other models such as Kimi K2.</p>
|
||
<h2 id="how-to-start"><a class="header" href="#how-to-start">How to start</a></h2>
|
||
<pre><code class="language-Python"># For LoRA SFT
|
||
USE_KT=1 llamafactory-cli train examples/train_lora/deepseek2_lora_dpo_kt.yaml
|
||
# For Chat with model after LoRA SFT
|
||
llamafactory-cli chat examples/inference/deepseek2_lora_dpo_kt.yaml
|
||
# For API with model after LoRA SFT
|
||
llamafactory-cli api examples/inference/deepseek2_lora_dpo_kt.yaml
|
||
</code></pre>
|
||
<p>For example, we provide the YAML file as follows:</p>
|
||
<p>（1）examples/train_lora/deepseek2_lora_dpo_kt.yaml</p>
|
||
<pre><code class="language-YAML">### model
|
||
model_name_or_path: deepseek-ai/DeepSeek-V2-Lite
|
||
trust_remote_code: true
|
||
|
||
### method
|
||
stage: dpo
|
||
do_train: true
|
||
finetuning_type: lora
|
||
lora_rank: 8
|
||
lora_target: all
|
||
pref_beta: 0.1
|
||
pref_loss: sigmoid # choices: [sigmoid (dpo), orpo, simpo]
|
||
|
||
### dataset
|
||
dataset: dpo_en_demo
|
||
template: llama3
|
||
cutoff_len: 2048
|
||
max_samples: 1000
|
||
overwrite_cache: true
|
||
preprocessing_num_workers: 16
|
||
dataloader_num_workers: 4
|
||
|
||
### output
|
||
output_dir: saves/Kllama_deepseekV2_DPO
|
||
logging_steps: 10
|
||
save_steps: 500
|
||
plot_loss: true
|
||
overwrite_output_dir: true
|
||
save_only_model: false
|
||
report_to: none # choices: [none, wandb, tensorboard, swanlab, mlflow]
|
||
|
||
### train
|
||
per_device_train_batch_size: 1
|
||
gradient_accumulation_steps: 8
|
||
learning_rate: 5.0e-6
|
||
num_train_epochs: 3
|
||
lr_scheduler_type: cosine
|
||
warmup_ratio: 0.1
|
||
bf16: true
|
||
ddp_timeout: 180000000
|
||
resume_from_checkpoint: null
|
||
|
||
### ktransformers
|
||
use_kt: true # use KTransformers as LoRA sft backend
|
||
kt_optimize_rule: examples/kt_optimize_rules/DeepSeek-V2-Lite-Chat-sft-amx.yaml
|
||
cpu_infer: 64
|
||
chunk_size: 8192
|
||
</code></pre>
|
||
<p>For more details about –kt_optimize_rule, please refer to https://github.com/kvcache-ai/ktransformers/blob/main/doc/en/KTransformers-Fine-Tuning_User-Guide.md</p>
|
||
<p>Then, you can use the lora adapter saved in <code>saves/Kllama_deepseekV2_DPO</code> for inference the same as the sft training. For example,</p>
|
||
<pre><code class="language-YAML">model_name_or_path: DeepSeek-V2-Lite-Chat
|
||
adapter_name_or_path: saves/Kllama_deepseekV2_DPO
|
||
template: deepseek
|
||
infer_backend: ktransformers # choices: [huggingface, vllm, sglang, ktransformers]
|
||
trust_remote_code: true
|
||
|
||
use_kt: true # use KTransformers as LoRA sft backend to inference
|
||
kt_optimize_rule: examples/kt_optimize_rules/DeepSeek-V2-Lite-Chat-sft-amx.yaml
|
||
cpu_infer: 32
|
||
chunk_size: 8192
|
||
|
||
</code></pre>
|
||
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