[whatmodel.sh](../../README.md) / DeepSeek-Coder 1.3B Instruct
# DeepSeek-Coder 1.3B Instruct
DeepSeek · released 2023-10 · 1.35B params · deepseek
**score 44.0** ±0.6 · #500 of 732 · from 14 benchmark results across 6 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 1.3B Instruct among every model on that task or test, worst to best.
task vs the others rank counts tests
coding ▁▁▁▃▅●█▇▅▄▃▁ #346 of 469 82% 10
coding · #346 of 469 · 82% of its score
Beats [LFM2.5 2.6B](lfm2-5-2-6b.md), [LFM2.5 8B-A1B](lfm2-5-8b-a1b.md), [Qwen3-VL 2B Thinking](qwen3-vl-2b-thinking.md) here, though they rank higher overall. Trails [Phi-3-mini-128k-instruct (Apr 2024)](phi-3-mini-128k-instruct.md), [DeepSeek LLM 67B Chat](deepseek-llm-67b-chat.md), [WizardLM 70B V1.0](wizardlm-70b-v1-0.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
HumanEval 65.9% ▁·▁▁▃▄▆●█▃▁▁ beats 51/99 easy · avg 76% 45% sharp · at its level · several runs
HumanEval+ 60.4% ▁·▁▁▁▃▄▄●█▃▁ beats 53/99 easy · avg 70% 23% sharp · at its level
LiveCodeBench official (Jan 1–Nov… 6.5% ▁●·▁▂▄▆█▅▂·▂ beats 1/31 hard · avg 23% 4.5% sharp
MBPP+ 54.8% ▁···▁▁▂▅●█▆▂ beats 28/75 easy · avg 66% 4.4% at its level
CRUXEval-I 27.2% ▁··●▅▂█▇▁··▁ beats 1/22 medium · avg 52% 1.4% at its level
CRUXEval-O 28.7% ▁·●▃▁█▁▂···▁ beats 1/22 medium · avg 51% 1.3% blunt · at its level
CodeReviewQA: solution identifica… 0.0% ●··▁▂▂▃▂█▃▄▂ beats 3/47 medium · avg 51% 1.0% sharp · at its level · noisy
CodeReviewQA: change localisation… 0.0% ●▂▁▁▂▂▂▃▅▅▅▇ beats 4/47 hard · avg 33% 0.8% sharp · noisy
CodeReviewQA: automated code refi… 11.7% ▁▁▁▁▁●▂▂▃█▅▄ beats 7/47 hard · avg 32% 0.8% typical
CodeReviewQA: change type recogni… 1.9% ▁▁●······▁▃█ beats 3/47 easy · avg 67% 0.0% blunt · at its level · noisy
other tests (3)
test result vs the others in its run difficulty counts why
EvoEval (overall) 34.9% ▂▂▃▄▄●▁▄▅▃▁▃ beats 24/42 medium · avg 52% 10% at its level
BigCodeBench (Complete) 29.3% ▂▁▁●▂▅▇█▆▂▂▂ beats 10/58 medium · avg 48% 4.0% at its level
BigCodeBench (Instruct) 22.7% ●▂·▆▆██▆▅▃▃▃ beats 1/33 medium · avg 39% 4.0% at its level
source results share of the score
EvalPlus / EvalPerf published evaluatio… 3 55% ▇▇▇▇▇▇▇
Top Leaderboard Ranking = Top Coding Pr… 2 27% ▇▇▇
BigCodeBench paper 2 8% ▇
LiveCodeBench official website results 1 5% ▇
CRUXEval published evaluation results 2 3% ▇
CodeReviewQA: The Code Review Comprehen… 4 3% ▇
prior: pull toward the middle 0% ▇
weighted average = 44.0
Licences: EvalPlus / EvalPerf published evaluation runs ([Apache-2.0](../../licenses/evalplus-published-runs-Apache-2.0.txt)) · Top Leaderboard Ranking = Top Coding Proficiency ([CC BY 4.0](../../licenses/paper-2403.19114v1-CC-BY-4.0.txt)) · BigCodeBench paper ([CC BY 4.0](http://creativecommons.org/licenses/by/4.0/)) · LiveCodeBench official website results ([CC-BY-SA-4.0](../../licenses/lcb-sharealike-NOTICE.txt)) · CRUXEval published evaluation results ([MIT](../../licenses/cruxeval-published-results-MIT.txt)) · CodeReviewQA: The Code Review Comprehension Assessment for Large Language Models ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/))
## 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 1.3B Instruct (Q4_K_M, needs ~0.9 GB, from [huggingface.co/TheBloke](https://huggingface.co/TheBloke/deepseek-coder-1.3b-instruct-GGUF/tree/4595af8c3dff738094bd6c86054dfb5a90d5c41e))
```sh
llama cli -hf TheBloke/deepseek-coder-1.3b-instruct-GGUF:Q4_K_M
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf TheBloke/deepseek-coder-1.3b-instruct-GGUF:Q4_K_M
```
Other versions: Q8_0 (~1.4 GB) `llama cli -hf TheBloke/deepseek-coder-1.3b-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 1.3B Instruct (Q4_K_M, needs ~0.9 GB, from [huggingface.co/TheBloke](https://huggingface.co/TheBloke/deepseek-coder-1.3b-instruct-GGUF/tree/4595af8c3dff738094bd6c86054dfb5a90d5c41e))
```sh
lms get https://huggingface.co/TheBloke/deepseek-coder-1.3b-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 (~1.4 GB) `lms get https://huggingface.co/TheBloke/deepseek-coder-1.3b-instruct-GGUF@Q8_0 -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 1.3B Instruct (Q4_K_M, needs ~0.874 GB, from [ollama.com](https://ollama.com/library/deepseek-coder/tags))
```sh
ollama run deepseek-coder:1.3b-instruct-q4_K_M
```
Other versions: Q8_0 (~1.4 GB) `ollama run deepseek-coder:1.3b-instruct-q8_0`
Model repository and documentation: [huggingface.co/deepseek-ai](https://huggingface.co/deepseek-ai/deepseek-coder-1.3b-instruct)
[interactive version](https://whatmodel.sh/model/deepseek-coder-1-3b-instruct) · [JSON](https://whatmodel.sh/api?format=json&all=1)