Windows Errors? Fix Them Before They Spread
Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstallCrashes, No Sound, or Screen Glitches?
Random freezes, missing sound and display glitches usually trace back to one bad driver. Find and replace yours safely.Free scan · under a minuteYou can fine-tune a DeepSeek-R1 distilled checkpoint on custom supervised examples using LoRA supervised fine-tuning (SFT). Alibaba Cloud’s Platform for AI (PAI) documents a hosted workflow for six distilled models; this is not a recipe for retraining the full DeepSeek-R1 model or reproducing DeepSeek’s original reinforcement-learning pipeline. The steps below use the 7B Qwen-derived checkpoint as an example, so check the chosen model’s current details page for its exact data format and settings.
Know what you are fine-tuning
DeepSeek-R1 and DeepSeek-R1-Distill are different training targets. DeepSeek describes R1 as a 671-billion-parameter model with 37 billion parameters activated, trained from DeepSeek-V3-Base. Its six released dense distilled checkpoints are 1.5B, 7B, 8B, 14B, 32B, and 70B; these smaller models are based on Qwen2.5 and Llama models and were fine-tuned using samples generated by R1. See the DeepSeek-R1 repository for model details.
| # | Preview | Product | Price | |
|---|---|---|---|---|
| 1 |
|
MINISFORUM MS-02 Ultra Workstation Mini PC, Intel Core Ultra 9 285HX (24C/24T, up to 5.5GHz), PCIe... | $1,659.00 | Buy on Amazon |
| 2 |
|
GMKtec EVO-X2 AI Mini PC Ryzen Al Max+ 395 Superchip 128GB LPDDR5X 2TB SSD | $3,649.99 | Buy on Amazon |
The PAI walkthrough covers LoRA SFT: it trains adapter parameters against your examples rather than repeating DeepSeek’s original multi-stage process. DeepSeek’s paper describes that original training approach, which includes supervised fine-tuning and reinforcement-learning stages: DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.
Choose a checkpoint that fits your task and compute
For a first run, the 7B Qwen-derived checkpoint is a practical example because Alibaba’s PAI guide includes a quick start for it. That does not make it the best choice for every dataset or application. Compare model scale, available compute, model family, and license before selecting a checkpoint.
What’s actually slowing this PC down?
Pick the symptom - the matching free tool is one click away.
#1 Best Overall
- High-Performance AI Processor：The MS-02 Ultra features an Intel Core Ultra 9 285HX (24C/24T, up to 5.5 GHz, 13 TOPS NPU), delivering fast and efficient performance for AI inference, algorithm development, and media workloads. A PCIe x16 expansion slot supports desktop-class GPU upgrades for advanced model training and accelerated computing tasks. It's ideal for creators, engineers, and teams handling intensive parallel workloads.
- 4 × M.2 PCIe 4.0 + 4 × DDR5 SODIMM slots:Four DDR5 SODIMM slots support up to 256 GB of memory, while ECC helps maintain data integrity in mission-critical environments. Four PCIe 4.0 M.2 slots support up to 24 TB of storage, supporting RAID 0/1/5/10, combining high-speed performance with data protection. It allows for the creation of independent scratch disks, media libraries, and project drives, providing high-throughput for production workflows.
- PCIe & USB 4.0 v2: Up to three PCIe slots can be equipped, including a dual-slot x16 GPU. The main slot supports PCIe 5.0, meeting the needs of high-bandwidth creative and computing workloads. USB 4.0 v2 (80Gbps) supports high-bandwidth external storage and displays.
- Ultra-fast Networking: Wi-Fi 7 further enhances wireless performance with next-generation speeds and low-latency stability. Intelligent bandwidth switching optimizes throughput in different network environments, ensuring optimal performance for enterprise or local networks. Dual 25GbE ports (providing up to approximately 3.125 GB/s bandwidth, about 25 times faster than traditional 1GbE), enabling seamless large-scale file transfers and parallel computing. 10GbE and 2.5GbE ports, with support for Intel vPro technology, ensure enterprise-grade remote management and deployment flexibility.
- Server-grade thermal architecture: Utilizing a dedicated CPU/GPU airflow design, equipped with a 6-pipe dual-fan cooler, it maintains stable performance even under sustained loads, delivering up to 140W Turbo power while maintaining a 100W TDP, and operating with noise levels as low as 36 dB. An integrated 350W power supply ensures stable and reliable output for demanding computing tasks and fully loaded extended configurations.
| Checkpoint size | Family | PAI guide’s example GPU configuration |
|---|---|---|
| 1.5B | Qwen-derived | One A10 with 24 GB video memory |
| 7B | Qwen-derived | One A10 with 24 GB video memory |
| 8B | Llama-derived | One A10 with 24 GB video memory |
| 14B | Qwen-derived | One 48 GB GU8IS |
| 32B | Qwen-derived | Two 48 GB GU8IS GPUs |
| 70B | Llama-derived | Eight 80 GB GU100 GPUs |
These are Alibaba’s configurations for the guide’s default hyperparameters and provided dataset, not universal minimums or retail GPU recommendations. Longer sequences, larger batches, dataset characteristics, and platform implementation can change memory use. Check the requirements for your selected checkpoint and setup in Alibaba’s PAI fine-tuning guide, last updated May 27, 2026.
Prepare the dataset in the checkpoint’s required format
Do not assume there is one universal JSON schema or chat template for every distilled model and platform. PAI directs users to the selected model’s details page for the required SFT data format. Follow that page and the model’s own tokenizer and configuration settings: DeepSeek’s repository notes that distilled-model configs and tokenizers were changed and advises using the repository settings.
- Check that each training example’s prompt and target response conform to the selected model’s documented format.
- Remove duplicates, malformed examples, and material you are not authorized to use; avoid including sensitive data that should not be sent to the training service.
- Set aside a held-out evaluation split before training. Keep it out of the training upload so it can provide a meaningful check on behavior the model has not trained on.
DeepSeek’s repository says the Qwen-derived 1.5B, 7B, 14B, and 32B variants were trained using 800,000 curated samples. That figure describes DeepSeek’s own training data; it is not a required or recommended size for your custom dataset.
Upload data and configure a PAI job
Alibaba’s documented hosted path uses Platform for AI and Object Storage Service (OSS). Exact interface labels can vary as the service changes, so use the current model details page and guide rather than relying on a generic template.
- Select the model: In PAI, open the details page for the distilled checkpoint you intend to fine-tune. Confirm its data format and configuration before preparing or uploading examples.
- Upload the dataset: Put the formatted custom data in an OSS bucket accessible to the training workflow, following the guide’s instructions.
- Set the output location: Choose an output path for the trained result or adapter, as requested by the PAI workflow.
- Choose compute: Select the supported compute configuration for the checkpoint and job settings. The GPU configurations in the table above are specific to Alibaba’s documented setup.
- Set and review hyperparameters: Configure the supported values in the job interface, check that the selected data and output paths are correct, then launch the training job.
Understand the 7B example settings
The PAI guide’s 7B quick-start values are examples for its workflow, not universal prescriptions. They provide a reproducible starting point for that documented setup; change them only with an understanding of the trade-offs and the platform’s supported options.
Rank #2
- EVOLUTION RYZEN AI MAX+ 395 MINI PC - GMKtec EVO-X2 is the next evolution in AI mini PC Ryzen Strix Halo series. Thanks to AMD Simultaneous Multithreading (SMT) the core-count is effectively doubled, to 32 threads. Ryzen AI Max+ 395 has 64 MB of L3 cache and can boost up to 5.1 GHz, depending on the workload. The Ryzen AI Max+ 395 is currently rated as the "most powerful x86 APU" on the market for AI computing.
- AI NPU with XDNA 2 ARCHITECTURE - Powered by 16 “Zen 5” CPU cores, 50+ peak AI TOPS XDNA 2 NPU and a truly massive integrated GPU driven by 40 AMD RDNA 3.5 CUs, the Ryzen AI MAX+ 395 is a transformative upgrade and delivers a significant performance boost over the competition. The Ryzen AI Max+ 395 excels in consumer AI workloads like the llama.cpp-powered application: LM Studio. Shaping up to be the must-have app for client LLM workloads, LM Studio allows users to locally run the latest language model without any technical knowledge required and unleash their creativity and productivity.
- AMD RADEON 8090S iGPU GAMING PC - The AMD Radeon RX 8060S offers all 40 CUs with up to 2.9 GHz graphics clock and uses the new RDNA 3.5 architecture. The powerful iGPU is positioned between an RTX 4060 and 4070 laptop GPU and therefore enables gaming in FHD at maximum details in most demanding games. The 8060S can also utilize the full 128GB pool, which is perfect for running LLMs such as Deepseek 70B Q8, which runs comfortably on this machine.
- EIGHT CHANNEL LPDDR5X - LPDDR5X is a new ground breaking memory small form factor installed on-board. With blazing speeds up to to 8000MT/s, it runs 1.5x faster than the DDR5 SODIMMs; 90% better performance over DDR5 SODIMMs in video conferencing and photo editing; 30% better performance in productivity apps; 12% better performance in digital content workloads.
- QUAD SCREEN 8K DISPLAY SUPPORT - EVO-X2 AI Mini PC support 4-screen 4K/8K output via HDMI 2.1 (8K@60Hz), DisplayPort 1.4 (4K@60Hz), and dual USB 4 40Gbps Transfer speed (supporting PD3.0/DP1.4/DATA). Ideal for gaming, video editing, and multitasking, it provides expansive and crisp multi-display support.
| Setting | PAI 7B example | What it controls |
|---|---|---|
| Learning rate | 5e-6 | How large the optimizer’s parameter updates are during training. |
| Epochs | 6 | How many passes training makes through the dataset. |
| Per-device batch size | 2 | Examples processed per device in a training step. |
| Gradient accumulation | 2 | Training steps accumulated before an optimizer update; it affects the effective batch size. |
| Maximum sequence length | 1024 | The maximum sequence length used by this example. Longer sequences can require more memory. |
| LoRA rank | 8 | The rank, or adapter capacity, used for the low-rank update. |
| LoRA alpha | 16 | A scaling value for the LoRA update. |
| LoRA dropout | 0 | The dropout value used for the LoRA adapter in this example. |
These values are quoted from the PAI guide’s 7B example. In particular, a maximum length of 1024 is not a guarantee that longer examples will fit on the same hardware. The QLoRA paper describes a general quantized fine-tuning method, but it does not establish that a particular QLoRA setup is supported for your selected DeepSeek checkpoint or PAI job: QLoRA: Efficient Finetuning of Quantized LLMs.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Evaluate the result before using it
After training, test the resulting adapter or checkpoint on the held-out examples and inspect outputs for task accuracy, format compliance, and unwanted changes in behavior. Compare it with the original checkpoint on the same prompts; a training run alone does not establish that the result is better for your use case. DeepSeek’s model card recommends evaluating with multiple tests and averaging results, but it does not define a fine-tuning-specific benchmark protocol: DeepSeek-R1 model card.
If the tuned model is inconsistent, inspect the examples and formatting first, then adjust the training configuration and rerun a controlled evaluation. Avoid relying only on examples seen during training, since they cannot show whether the model learned the intended behavior beyond those examples.
Check licensing and deployment conditions
DeepSeek’s repository states that the R1 series supports commercial use and permits modifications and derivative works, including distillation for training other language models. It also notes that Qwen-derived variants carry Qwen upstream terms and Llama-derived variants carry Llama licenses. Review the exact selected checkpoint’s license and upstream conditions, along with rights to your training data, before commercial deployment.
PAI’s walkthrough documents a training workflow; it does not establish a universal training cost, runtime, serving configuration, or production performance. Decide whether the trained result meets your own quality and operational requirements before putting it into service.
Quick Recap
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
