[whatmodel.sh](../../README.md) / DeepSeek-V2 Chat (0628)
# DeepSeek-V2 Chat (0628)
DeepSeek · released 2024-07 · 235.74B params (21B active) · other (deepseek)
**score 54.5** ±0.9 · #295 of 732 · from 6 benchmark results across 3 sources
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks DeepSeek-V2 Chat (0628) among every model on that task or test, worst to best.
task vs the others rank counts tests
coding ▁▁▁▃▅▇█●▅▄▃▁ #206 of 469* 36% 2
math ▁▁▃▅●▆▄█▄▂▁▁ #283 of 431* 6.5% 1
coding · #206 of 469 · 36% of its score
Beats [Gemini 2.5 Flash](gemini-2-5-flash.md), [Qwen3 30B-A3B Instruct 2507](qwen3-30b-a3b-instruct-2507.md), [Qwen3.5 4B](qwen3-5-4b.md) here, though they rank higher overall. Trails [QwQ-32B-Preview](qwq-32b-preview.md), [Ministral 3 3B Reasoning 2512](ministral-3-3b-reasoning.md), [DeepSeek-Coder-V2 Instruct (0724)](deepseek-coder-v2-0724.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
CRUXEval (ZeroEval, 800 problems) 70.5% ▁▂▆▃▆●▆▃▂·▁▁ beats 31/47 medium · avg 57% 20% sharp · at its level
Aider code editing (133 Exercism … 69.9% ▁·▁▂▄▁▆▄█●▂▂ beats 48/62 medium · avg 51% 16% sharp · at its level
math · #283 of 431 · 6.5% of its score
Beats [Molmo 2 8B](molmo-2-8b.md), [Llama 3.3 70B Instruct](llama-3-3-70b.md), [DeepSeek-V2.5](deepseek-v2-5.md) here, though they rank higher overall. Trails [DeepSeek R1 Distill Qwen 1.5B](deepseek-r1-distill-qwen-1-5b.md), [Gemma 7B (base)](gemma-7b-base.md), [Mistral 7B v0.1 (base)](mistral-7b-v0-1.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
GSM8K (ZeroEval, zero-shot) 93.9% ▁▄·▁▄█▅▅▅●██ beats 31/43 very easy · avg 87% 6.5% sharp · maxed out
other tests (3)
test result vs the others in its run difficulty counts why
MMLU-Redux (ZeroEval, 2,778 quest… 80.8% ▁·▁▁▄▄▄▅●▄▂▁ beats 30/53 easy · avg 75% 31% typical
ZebraLogic (ZeroEval, 1,000 puzzl… 22.7% ▁▁▃▆●█▁▁·▁▁▁ beats 35/57 hard · avg 15% 16% sharp · at its level
WildBench v2 WB-Score (task macro) 53.9943 WB-Score ▁·▁▁·▃█▄▇▆▅● beats 45/52 avg 43.019 WB-Score 9.3% sharp
source results share of the score
WildEval ZeroEval published results 4 74% ▇▇▇▇▇▇▇▇▇
Aider code editing benchmark runs 1 16% ▇▇
WildBench v2 leaderboard data 1 9% ▇
prior: pull toward the middle 0% ▇
weighted average = 54.5
Licences: WildEval ZeroEval published results ([Apache-2.0](../../licenses/zeroeval-published-results-Apache-2.0.txt)) · Aider code editing benchmark runs ([Apache-2.0](../../licenses/aider-polyglot-runs-Apache-2.0.txt)) · WildBench v2 leaderboard data ([Apache-2.0](https://github.com/allenai/WildBench/blob/d6b8dcaf377d173d031980f97c16e1a82618c03d/LICENSE))
## How to run it
### llama.cpp (recommended)
1. Install llama.cpp. Homebrew and winget installs name the commands llama-cli and llama-server instead. [Other ways to install](https://github.com/ggml-org/llama.cpp/blob/master/docs/install.md)
```sh
# macOS / Linux
curl -LsSf https://llama.app/install.sh | sh
# Windows
irm https://llama.app/install.ps1 | iex
```
2. Run DeepSeek-V2 Chat (0628) (Q4_K_M, needs ~142.5 GB, from [huggingface.co/bartowski](https://huggingface.co/bartowski/DeepSeek-V2-Chat-0628-GGUF/tree/930dbf63a11aa75b1f696ba640bb170f8d75c57d))
```sh
llama cli -hf bartowski/DeepSeek-V2-Chat-0628-GGUF:Q4_K_M
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf bartowski/DeepSeek-V2-Chat-0628-GGUF:Q4_K_M
```
Other versions: IQ2_XXS (~61.5 GB) `llama cli -hf bartowski/DeepSeek-V2-Chat-0628-GGUF:IQ2_XXS`
### LM Studio
1. Install LM Studio. Open the app once before using the lms command. [Download it (macOS / Windows / Linux)](https://lmstudio.ai/download)
2. Run DeepSeek-V2 Chat (0628) (Q4_K_M, needs ~142.5 GB, from [huggingface.co/bartowski](https://huggingface.co/bartowski/DeepSeek-V2-Chat-0628-GGUF/tree/930dbf63a11aa75b1f696ba640bb170f8d75c57d))
```sh
lms get https://huggingface.co/bartowski/DeepSeek-V2-Chat-0628-GGUF@Q4_K_M -y
```
3. Then run lms ls to see its name, and lms chat followed by that name
Other versions: IQ2_XXS (~61.5 GB) `lms get https://huggingface.co/bartowski/DeepSeek-V2-Chat-0628-GGUF@IQ2_XXS -y`
### Other ways to run it
1. Run it as published (BF16, needs ~471.49 GB, from [huggingface.co/deepseek-ai](https://huggingface.co/deepseek-ai/DeepSeek-V2-Chat-0628/tree/5d09e272c2b223830f4e84359cd9dd047a5d7c78))
```sh
uv run --with torch --with 'transformers<5' --with accelerate python -c "from transformers import pipeline; print(pipeline('text-generation', model='deepseek-ai/DeepSeek-V2-Chat-0628', torch_dtype='auto', device_map='auto', trust_remote_code=True)('Once upon a time', max_new_tokens=40)[0]['generated_text'])"
```
Model repository and documentation: [huggingface.co/deepseek-ai](https://huggingface.co/deepseek-ai/DeepSeek-V2-Chat-0628)
[interactive version](https://whatmodel.sh/model/deepseek-v2-chat-0628) · [JSON](https://whatmodel.sh/api?format=json&all=1)