[whatmodel.sh](../../README.md) / DeepSeek-Coder-V2 Lite Instruct (16B-A2.4B)
# DeepSeek-Coder-V2 Lite Instruct (16B-A2.4B)
DeepSeek · released 2024-06 · 15.71B params (2.4B active) · deepseek
**score 46.2** ±1.1 · #450 of 732 · from 10 benchmark results across 7 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 Lite Instruct (16B-A2.4B) among every model on that task or test, worst to best.
task vs the others rank counts tests
agent tasks ●▁▃▆█▄▅▅▄▄▂▁ #310 of 313* 2.9% 1
coding ▁▁▁▃▅●█▇▅▄▃▁ #321 of 469 34% 6
agent tasks · #310 of 313 · 2.9% of its score
Beats [QwQ-32B-Preview](qwq-32b-preview.md) here, though they rank higher overall. Trails [Granite 4.0 350M](granite-4-0-350m.md), [Granite 4.0 H 350M](granite-4-0-h-350m.md), [Qwen2.5 0.5B Instruct](qwen2-5-0-5b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
BFCL v3 21.0% ▁●▁▁▁▃▅█▅▄▁▁ beats 4/114 medium · avg 51% 2.9% blunt · at its level
coding · #321 of 469 · 34% of its score
Beats [Solar Open 100B (102B-A12B)](solar-open-100b.md), [Qwen3 4B Instruct 2507](qwen3-4b-instruct-2507.md), [Qwen3-VL 8B Instruct](qwen3-vl-8b-instruct.md) here, though they rank higher overall. Trails [DeepSeek LLM 67B Chat](deepseek-llm-67b-chat.md), [WizardLM 70B V1.0](wizardlm-70b-v1-0.md), [Code Tulu 2 34B](code-tulu-2-34b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
LiveCodeBench official (Jan 1–Nov… 20.6% ▁▁·▁▂●▆█▅▂·▂ beats 5/31 hard · avg 23% 19% sharp
WebApp1K-React pass@1 (onekq-ai) 46.1% ▃▁▁█▁●▅▃▄▇▁▄ beats 11/31 medium · avg 40% 4.8% sharp · at its level · noisy
CodeReviewQA: solution identifica… 12.8% ▃··▁▂●▃▂█▃▄▂ beats 10/47 medium · avg 51% 3.9% sharp · at its level · noisy
CodeReviewQA: change localisation… 6.2% █▂▁▁▂●▂▃▅▅▅▇ beats 15/47 hard · avg 33% 3.5% sharp · noisy
CodeReviewQA: automated code refi… 27.0% ▁▁▁▁▁▁▂▂●█▅▄ beats 18/47 hard · avg 32% 3.0% at its level
CodeReviewQA: change type recogni… 75.7% ▁▁▁······▁▃● beats 31/47 easy · avg 67% 0.1% blunt · at its level · noisy
other tests (3)
test result vs the others in its run difficulty counts why
LiveBench 2024-07-26 29.5% ▁▁▁▂▂▃▂●▆▃▅▃ beats 26/61 medium · avg 38% 48% at its level
EvalPerf correctness (118 tasks, … 79.0% ▂·▂▂▂▆▅▆●▆▃█ beats 16/29 easy · avg 74% 8.9% at its level
DSEffi-Bench Pass (1,000 data-sci… 17.1% ●··▃▅·█·▃██▃ beats 0/15 hard · avg 23% 5.9% typical
source results share of the score
LiveBench model judgments 1 48% ▇▇▇▇▇▇
LiveCodeBench official website results 1 19% ▇▇
CodeReviewQA: The Code Review Comprehen… 4 11% ▇
EvalPlus / EvalPerf published evaluatio… 1 9% ▇
DSEffi-Bench: Demystifying Large Langua… 1 6% ▇
WebApp1K models leaderboard 1 5% ▇
Berkeley Function Calling Leaderboard r… 1 3% ▇
prior: pull toward the middle 1% ▇
weighted average = 46.2
Licences: LiveBench model judgments ([Apache-2.0 (stated in the LiveBench datasheet)](https://www.apache.org/licenses/LICENSE-2.0)) · LiveCodeBench official website results ([CC-BY-SA-4.0](../../licenses/lcb-sharealike-NOTICE.txt)) · CodeReviewQA: The Code Review Comprehension Assessment for Large Language Models ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)) · EvalPlus / EvalPerf published evaluation runs ([Apache-2.0](../../licenses/evalplus-published-runs-Apache-2.0.txt)) · DSEffi-Bench: Demystifying Large Language Models' Capability in Efficient Data Science Code Generation ([CC BY 4.0](../../licenses/paper-2608.30248v1-CC-BY-4.0.txt)) · WebApp1K models leaderboard ([MIT](../../licenses/webapp1k-leaderboard-MIT.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 Lite Instruct (16B-A2.4B) (Q4_K_M, needs ~10.4 GB, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/DeepSeek-Coder-V2-Lite-Instruct-GGUF/tree/81793aca588452deb110103ff5400cc32c44e7f9))
```sh
llama cli -hf lmstudio-community/DeepSeek-Coder-V2-Lite-Instruct-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-Lite-Instruct-GGUF:Q4_K_M
```
Other versions: Q8_0 (~16.7 GB) `llama cli -hf lmstudio-community/DeepSeek-Coder-V2-Lite-Instruct-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 Lite Instruct (16B-A2.4B) (Q4_K_M, needs ~10.4 GB, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/DeepSeek-Coder-V2-Lite-Instruct-GGUF/tree/81793aca588452deb110103ff5400cc32c44e7f9))
```sh
lms get https://huggingface.co/lmstudio-community/DeepSeek-Coder-V2-Lite-Instruct-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 (~16.7 GB) `lms get https://huggingface.co/lmstudio-community/DeepSeek-Coder-V2-Lite-Instruct-GGUF@Q8_0 -y` · 4bit (~8.8 GB, Mac only) `lms get https://huggingface.co/mlx-community/DeepSeek-Coder-V2-Lite-Instruct-4bit-mlx -y` · 8bit (~16.7 GB, Mac only) `lms get https://huggingface.co/mlx-community/DeepSeek-Coder-V2-Lite-Instruct-8bit -y`
### Ollama
1. Install Ollama. [Download it (macOS / Windows)](https://ollama.com/download)
```sh
# Linux
curl -fsSL https://ollama.com/install.sh | sh
```
2. Run DeepSeek-Coder-V2 Lite Instruct (16B-A2.4B) (Q4_K_M, needs ~9.93 GB, from [ollama.com](https://ollama.com/library/deepseek-coder-v2/tags))
```sh
ollama run deepseek-coder-v2:16b-lite-instruct-q4_K_M
```
Other versions: Q8_0 (~19.35 GB) `ollama run deepseek-coder-v2:16b-lite-instruct-q8_0`
### MLX (Mac)
1. Install MLX (Mac). Apple silicon Macs only, text only.
```sh
# with uv
uv tool install mlx-lm
# or
pip install mlx-lm
```
2. Run DeepSeek-Coder-V2 Lite Instruct (16B-A2.4B) (4bit, needs ~8.8 GB, Mac only, from [huggingface.co/mlx-community](https://huggingface.co/mlx-community/DeepSeek-Coder-V2-Lite-Instruct-4bit-mlx/tree/5f60ee33ba169428b5bc249b05b5b99f827d2e5e))
```sh
mlx_lm.chat --model mlx-community/DeepSeek-Coder-V2-Lite-Instruct-4bit-mlx
```
3. Or start an OpenAI-compatible server at http://localhost:8080/v1
```sh
mlx_lm.server --model mlx-community/DeepSeek-Coder-V2-Lite-Instruct-4bit-mlx
```
Other versions: 8bit (~16.7 GB, Mac only) `mlx_lm.chat --model mlx-community/DeepSeek-Coder-V2-Lite-Instruct-8bit`
Model repository and documentation: [huggingface.co/deepseek-ai](https://huggingface.co/deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct)
[interactive version](https://whatmodel.sh/model/deepseek-coder-v2-lite-instruct) · [JSON](https://whatmodel.sh/api?format=json&all=1)