[whatmodel.sh](../../README.md) / DeepSeek-Coder 1.3B Base
# DeepSeek-Coder 1.3B Base
DeepSeek · released 2023-10 · 1.3B params · deepseek
**score 34.3** ±0.6 · #634 of 732 · from 12 benchmark results across 5 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 Base among every model on that task or test, worst to best.
task vs the others rank counts tests
coding ▁▁▁●▅▇█▇▅▄▃▁ #431 of 469 86% 7
math ▁▁●▅█▆▄█▄▂▁▁ #392 of 431* 3.1% 1
coding · #431 of 469 · 86% of its score
Beats [DeepSeek R1 Distill Qwen 7B](deepseek-r1-distill-qwen-7b.md), [Qwen3.5 2B](qwen3-5-2b.md), [DeepSeek R1 Distill Llama 8B](deepseek-r1-distill-llama-8b.md) here, though they rank higher overall. Trails [Llama 3.2 1B Instruct](llama-3-2-1b.md), [SmolLM2 1.7B Instruct](smollm2-1-7b.md), [Mistral 7B Instruct v0.1](mistral-7b-instruct-v0-1.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
HumanEval 28.7% ▁·▁▁●▄▆▅█▃▁▁ beats 13/99 easy · avg 76% 52% sharp · at its level · several runs
HumanEval+ 25.6% ▁·▁▁▁●▄▄▄█▃▁ beats 15/99 easy · avg 70% 19% sharp · at its level
MBPP 55.4% ▄▄▄█●▄·▄█·▄▄ beats 6/12 easy · avg 81% 4.9% at its level
MBPP+ 47.9% ▁···▁▁▂●▇█▆▂ beats 17/75 easy · avg 66% 4.6% at its level
DS-1000 16.2% ▄●·▄██▄█·█·▄ beats 1/12 medium · avg 49% 3.2% typical
CRUXEval-I 27.8% ▁··▁●▂█▇▁··▁ beats 2/22 medium · avg 52% 1.3% at its level
CRUXEval-O 31.0% ▁·●▃▁█▁▂···▁ beats 2/22 medium · avg 51% 1.2% blunt · at its level
math · #392 of 431 · 3.1% of its score
Beats [Yi 34B Chat](yi-34b-chat.md), [Command R (03-2024)](command-r-03-2024.md), [DeepSeek LLM 67B Chat](deepseek-llm-67b-chat.md) here, though they rank higher overall. Trails [GPT-Neo 125M](gpt-neo-125m.md), [GPT-2 XL (1.5B)](gpt-2-xl.md), [GPT-Neo 1.3B](gpt-neo-1-3b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
GSM8K 12.6% ▄●▄·▄█·██·█▄ beats 1/12 easy · avg 75% 3.1% sharp · at its level
other tests (2)
test result vs the others in its run difficulty counts why
EvoEval (overall) 20.3% ▂▂●▄▄█▁▄▅▃▁▃ beats 5/42 medium · avg 52% 7.4% typical
BigCodeBench (Complete) 22.3% ●▁▁▄▂▅▇█▆▂▂▂ beats 2/58 medium · avg 48% 3.5% typical
source results share of the score
EvalPlus / EvalPerf published evaluatio… 3 50% ▇▇▇▇▇▇
Top Leaderboard Ranking = Top Coding Pr… 2 22% ▇▇▇
StarCoder 2 and The Stack v2: The Next … 4 21% ▇▇▇
BigCodeBench paper 1 3% ▇
CRUXEval published evaluation results 2 3% ▇
prior: pull toward the middle 0% ▇
weighted average = 34.3
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)) · StarCoder 2 and The Stack v2: The Next Generation ([CC BY-SA 4.0](../../licenses/paper-2402.19173v1-CC-BY-SA-4.0.txt)) · BigCodeBench paper ([CC BY 4.0](http://creativecommons.org/licenses/by/4.0/)) · CRUXEval published evaluation results ([MIT](../../licenses/cruxeval-published-results-MIT.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 1.3B Base (Q4_K_M, needs ~0.9 GB, from [huggingface.co/TheBloke](https://huggingface.co/TheBloke/deepseek-coder-1.3b-base-GGUF/tree/ec89dd32c0a17bd56d27eccca7d6e4195c0f615d))
```sh
llama cli -hf TheBloke/deepseek-coder-1.3b-base-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-base-GGUF:Q4_K_M
```
Other versions: Q8_0 (~1.4 GB) `llama cli -hf TheBloke/deepseek-coder-1.3b-base-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 Base (Q4_K_M, needs ~0.9 GB, from [huggingface.co/TheBloke](https://huggingface.co/TheBloke/deepseek-coder-1.3b-base-GGUF/tree/ec89dd32c0a17bd56d27eccca7d6e4195c0f615d))
```sh
lms get https://huggingface.co/TheBloke/deepseek-coder-1.3b-base-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-base-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 Base (Q4_K_M, needs ~0.874 GB, from [ollama.com](https://ollama.com/library/deepseek-coder/tags))
```sh
ollama run deepseek-coder:1.3b-base-q4_K_M
```
Other versions: Q8_0 (~1.4 GB) `ollama run deepseek-coder:1.3b-base-q8_0`
Model repository and documentation: [huggingface.co/deepseek-ai](https://huggingface.co/deepseek-ai/deepseek-coder-1.3b-base)
[interactive version](https://whatmodel.sh/model/deepseek-coder-1-3b-base) · [JSON](https://whatmodel.sh/api?format=json&all=1)