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[Doc] Update the weight download URL. (#5238)

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### What this PR does / why we need it?
Update the weight download URL. Because the model was renamed.
### Does this PR introduce _any_ user-facing change?

### How was this patch tested?

- vLLM version: release/v0.13.0
- vLLM main:
ad32e3e19c

---------

Signed-off-by: menogrey <1299267905@qq.com>
This commit is contained in:
zhangyiming
2025-12-23 08:53:30 +08:00
committed by GitHub
parent c3a8d13ca7
commit f883a2edb9
2 changed files with 21 additions and 21 deletions
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14
docs/source/tutorials/DeepSeek-V3.2.md
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@@ -19,7 +19,7 @@ Refer to [feature guide](../user_guide/feature_guide/index.md) to get the featur
- `DeepSeek-V3.2-Exp`(BF16 version): require 2 Atlas 800 A3 (64G × 16) nodes or 4 Atlas 800 A2 (64G × 8) nodes. [Download model weight](https://modelers.cn/models/Modelers_Park/DeepSeek-V3.2-Exp-BF16)
- `DeepSeek-V3.2-Exp-w8a8`(Quantized version): require 1 Atlas 800 A3 (64G × 16) node or 2 Atlas 800 A2 (64G × 8) nodes. [Download model weight](https://modelers.cn/models/Modelers_Park/DeepSeek-V3.2-Exp-w8a8)
- `DeepSeek-V3.2`(BF16 version): require 2 Atlas 800 A3 (64G × 16) nodes or 4 Atlas 800 A2 (64G × 8) nodes. Model weight in BF16 not found now.
- `DeepSeek-V3.2-w8a8`(Quantized version): require 1 Atlas 800 A3 (64G × 16) node or 2 Atlas 800 A2 (64G × 8) nodes. [Download model weight](https://modelers.cn/models/Eco-Tech/DeepSeek-V3.2-w8a8-QuaRot)
- `DeepSeek-V3.2-w8a8`(Quantized version): require 1 Atlas 800 A3 (64G × 16) node or 2 Atlas 800 A2 (64G × 8) nodes. [Download model weight](https://modelers.cn/models/Eco-Tech/DeepSeek-V3.2-w8a8-mtp-QuaRot)
It is recommended to download the model weight to the shared directory of multiple nodes, such as `/root/.cache/`
@@ -289,7 +289,7 @@ Before you start, please
export VLLM_ASCEND_ENABLE_FLASHCOMM1=1
vllm serve /root/.cache/Eco-Tech/DeepSeek-V3.2-w8a8-QuaRot \
vllm serve /root/.cache/Eco-Tech/DeepSeek-V3.2-w8a8-mtp-QuaRot \
--host 0.0.0.0 \
--port $2 \
--data-parallel-size $3 \
@@ -364,7 +364,7 @@ Before you start, please
export VLLM_ASCEND_ENABLE_FLASHCOMM1=1
vllm serve /root/.cache/Eco-Tech/DeepSeek-V3.2-w8a8-QuaRot \
vllm serve /root/.cache/Eco-Tech/DeepSeek-V3.2-w8a8-mtp-QuaRot \
--host 0.0.0.0 \
--port $2 \
--data-parallel-size $3 \
@@ -441,7 +441,7 @@ Before you start, please
export VLLM_ASCEND_ENABLE_MLAPO=1
vllm serve /root/.cache/Eco-Tech/DeepSeek-V3.2-w8a8-QuaRot \
vllm serve /root/.cache/Eco-Tech/DeepSeek-V3.2-w8a8-mtp-QuaRot \
--host 0.0.0.0 \
--port $2 \
--data-parallel-size $3 \
@@ -519,7 +519,7 @@ Before you start, please
export VLLM_ASCEND_ENABLE_MLAPO=1
vllm serve /root/.cache/Eco-Tech/DeepSeek-V3.2-w8a8-QuaRot \
vllm serve /root/.cache/Eco-Tech/DeepSeek-V3.2-w8a8-mtp-QuaRot \
--host 0.0.0.0 \
--port $2 \
--data-parallel-size $3 \
@@ -626,7 +626,7 @@ As an example, take the `gsm8k` dataset as a test dataset, and run accuracy eval
```shell
lm_eval \
--model local-completions \
--model_args model=/root/.cache/Eco-Tech/DeepSeek-V3.2-w8a8-QuaRot,base_url=http://127.0.0.1:8000/v1/completions,tokenized_requests=False,trust_remote_code=True \
--model_args model=/root/.cache/Eco-Tech/DeepSeek-V3.2-w8a8-mtp-QuaRot,base_url=http://127.0.0.1:8000/v1/completions,tokenized_requests=False,trust_remote_code=True \
--tasks gsm8k \
--output_path ./
```
@@ -654,7 +654,7 @@ Take the `serve` as an example. Run the code as follows.
```shell
export VLLM_USE_MODELSCOPE=true
vllm bench serve --model /root/.cache/Eco-Tech/DeepSeek-V3.2-w8a8-QuaRot --dataset-name random --random-input 200 --num-prompt 200 --request-rate 1 --save-result --result-dir ./
vllm bench serve --model /root/.cache/Eco-Tech/DeepSeek-V3.2-w8a8-mtp-QuaRot --dataset-name random --random-input 200 --num-prompt 200 --request-rate 1 --save-result --result-dir ./
```
After about several minutes, you can get the performance evaluation result. With this tutorial, the performance result is: