[whatmodel.sh](../../README.md) / DeepSeek-Coder-V2 Instruct (0724)
# DeepSeek-Coder-V2 Instruct (0724)
DeepSeek · released 2024-09 · 235.74B params (21B active) · other (deepseek-license)
**score 54.0** ±0.9 · #304 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-Coder-V2 Instruct (0724) among every model on that task or test, worst to best.
task vs the others rank counts tests
agent tasks ▁▁▃●█▄▅▅▄▄▂▁ #246 of 313* 2.1% 1
coding ▁▁▁▃▅▇█●▅▄▃▁ #198 of 469* 39% 2
math ▁▁▃▅●▆▄█▄▂▁▁ #303 of 431* 7.3% 1
agent tasks · #246 of 313 · 2.1% of its score
Beats [o3-mini](o3-mini.md), [Llama 4 Maverick (402B-A17B)](llama-4-maverick.md), [DeepSeek R1 0528 Qwen3 8B](deepseek-r1-0528-qwen3-8b.md) here, though they rank higher overall. Trails [Granite 4.0 1B](granite-4-0-1b.md), [SmolLM3 3B](smollm3-3b.md), [Phi-3.5-mini-instruct](phi-3-5-mini-instruct.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
BFCL v3 54.8% ▁▁▁▁▁▃▅●▅▄▁▁ beats 57/114 medium · avg 51% 2.1% blunt · at its level
coding · #198 of 469 · 39% 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) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
CRUXEval (ZeroEval, 800 problems) 69.5% ▁▂▆▃▆●▆▃▂·▁▁ beats 27/47 medium · avg 57% 22% sharp · at its level
Aider code editing (133 Exercism … 72.9% ▁·▁▂▄▁▆▄█●▂▂ beats 55/62 medium · avg 51% 17% sharp · at its level
math · #303 of 431 · 7.3% of its score
Beats [Molmo 2 8B](molmo-2-8b.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) 91.5% ▁▄·▁▄█▅▅●███ beats 24/43 very easy · avg 87% 7.3% sharp · maxed out
other tests (2)
test result vs the others in its run difficulty counts why
MMLU-Redux (ZeroEval, 2,778 quest… 80.2% ▁·▁▁▄▄▄●█▄▂▁ beats 28/53 easy · avg 75% 34% typical
ZebraLogic (ZeroEval, 1,000 puzzl… 20.5% ▁▁▃▆●█▁▁·▁▁▁ beats 31/57 hard · avg 15% 17% sharp · at its level
source results share of the score
WildEval ZeroEval published results 4 80% ▇▇▇▇▇▇▇▇▇▇
Aider code editing benchmark runs 1 17% ▇▇
Berkeley Function Calling Leaderboard r… 1 2% ▇
prior: pull toward the middle 0% ▇
weighted average = 54.0
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)) · Berkeley Function Calling Leaderboard result archive ([Apache-2.0](../../licenses/bfcl-result-archive-v4-Apache-2.0.txt))
## 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-Coder-V2 Instruct (0724) (Q4_K_M, needs ~142.5 GB, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/DeepSeek-Coder-V2-Instruct-0724-GGUF/tree/c174826c543bed5a2225f2eab8bc2e76a94f6dce))
```sh
llama cli -hf lmstudio-community/DeepSeek-Coder-V2-Instruct-0724-GGUF:Q4_K_M
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf lmstudio-community/DeepSeek-Coder-V2-Instruct-0724-GGUF:Q4_K_M
```
Other versions: Q8_0 (~250.6 GB) `llama cli -hf lmstudio-community/DeepSeek-Coder-V2-Instruct-0724-GGUF:Q8_0`
### 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-Coder-V2 Instruct (0724) (Q4_K_M, needs ~142.5 GB, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/DeepSeek-Coder-V2-Instruct-0724-GGUF/tree/c174826c543bed5a2225f2eab8bc2e76a94f6dce))
```sh
lms get https://huggingface.co/lmstudio-community/DeepSeek-Coder-V2-Instruct-0724-GGUF@Q4_K_M -y
```
3. Then run lms ls to see its name, and lms chat followed by that name
Other versions: Q8_0 (~250.6 GB) `lms get https://huggingface.co/lmstudio-community/DeepSeek-Coder-V2-Instruct-0724-GGUF@Q8_0 -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-Coder-V2-Instruct-0724/tree/80378f79777fd4d713ecc3e387de5512d84f1b87))
```sh
uv run --with torch --with 'transformers<5' --with accelerate python -c "from transformers import pipeline; print(pipeline('text-generation', model='deepseek-ai/DeepSeek-Coder-V2-Instruct-0724', 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-Coder-V2-Instruct-0724)
[interactive version](https://whatmodel.sh/model/deepseek-coder-v2-0724) · [JSON](https://whatmodel.sh/api?format=json&all=1)