Set up the publisher PyTorch reference inference stack for deepseek-ai/DeepSeek-V4-Flash on a Linux machine or rented GPU, run the interactive generator, and complete a small reviewable coding task through the documented interactive client.
Set up the publisher PyTorch reference inference stack for deepseek-ai/DeepSeek-V4-Flash on a Linux machine or rented GPU, run the interactive generator, and complete a small reviewable coding task through the documented interactive client.
This setup uses the package documented for this task. Review its source and supported platforms before starting.
The publisher has not provided a complete memory target for this task. Check its hardware guidance before starting.
This setup uses Publisher PyTorch reference. The exact checkpoint format, context, and runtime overhead determine its memory need; a weight-file size alone is not a GPU recommendation.
Pick your operating system. Every command below is for the selected package and runtime.
The publisher supplies Python code but does not specify an operating system. Linux is a practical starting environment, not a publisher-tested machine. Run the Python snippets in one session, in order.
On your Linux machine or rented GPU instance, install PyTorch so that the torchrun launcher is available on PATH. Then obtain the deepseek-ai/DeepSeek-V4-Flash repository from Hugging Face, since the documented workflow uses its convert.py and generate.py scripts.
Set the variables used by the publisher conversion script. HF_CKPT_PATH must point at the downloaded Hugging Face checkpoint directory for deepseek-ai/DeepSeek-V4-Flash. SAVE_PATH is the directory where the converted project-format weights will be written. The documented example uses 256 experts and model parallel size 4.
export EXPERTS=256
export MP=4
export CONFIG=config.json
python convert.py --hf-ckpt-path ${HF_CKPT_PATH} --save-path ${SAVE_PATH} --n-experts ${EXPERTS} --model-parallel ${MP}Launch the publisher PyTorch reference generator in interactive mode with the converted checkpoint and config. Keep MP at the same value used during conversion unless you deliberately re-convert.
torchrun --nproc-per-node ${MP} generate.py --ckpt-path ${SAVE_PATH} --config ${CONFIG} --interactiveAt the interactive prompt, ask for a small self-contained coding task that you can read and review. For example: Write a Python function `parse_duration(text: str) -> int` that accepts strings like "1h 30m 15s", "90m", and "45s", and returns the total number of seconds. Include a short docstring and three example asserts. Keep it under 40 lines and explain each decision briefly.
Read the generated function and asserts in the terminal output. Confirm that the model returned a Python function with the requested signature and that the example asserts cover hours, minutes, and seconds. Paste the function into a local file and run the asserts to confirm they pass before you accept the response.
Continue in the same interactive session to request follow-up code changes. Keep each request small enough that you can read the whole output and verify it locally before moving on.
Ask for a self-contained example, run it in a disposable file, and inspect the code and output before using it in a real project.
This is a source-linked setup, not a YouRunAI hardware test. Confirm your exact runtime version, package, and output before relying on it.
The documented example uses MP=4. Set MP to a value that matches the number of GPUs you actually have, and re-run the conversion before starting generate.py so both stages agree.
Remove the "expert_dtype": "fp4" entry from config.json and pass --expert-dtype fp8 to convert.py when converting the checkpoint.
Run the batch form instead: torchrun --nproc-per-node ${MP} generate.py --ckpt-path ${SAVE_PATH} --config ${CONFIG} --input-file ${FILE}, with FILE pointing at your prompt file.
Use the documented multi-node form: torchrun --nnodes ${NODES} --nproc-per-node $((MP / NODES)) --node-rank $RANK --master-addr $ADDR generate.py --ckpt-path ${SAVE_PATH} --config ${CONFIG} --input-file ${FILE}, setting NODES, RANK, and ADDR for your cluster.
Original instructions, model files, and compatibility notes behind this setup.
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