[whatmodel.sh](../../README.md) / DeepSeek-Coder 6.7B Base
# DeepSeek-Coder 6.7B Base
DeepSeek · released 2023-10 · 6.74B params · deepseek
**score 40.8** ±0.6 · #551 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 6.7B Base among every model on that task or test, worst to best.
task vs the others rank counts tests
coding ▁▁▁▃●▇█▇▅▄▃▁ #394 of 469 85% 7
math ▁▁▃▅●▆▄█▄▂▁▁ #313 of 431* 3.2% 1
coding · #394 of 469 · 85% 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 [Qwen2.5 1.5B Instruct](qwen2-5-1-5b.md), [WizardLM 13B V1.2](wizardlm-13b-v1-2.md), [Falcon3 3B Instruct](falcon3-3b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
HumanEval 47.6% ▁·▁▁▃▄●▅█▃▁▁ beats 32/99 easy · avg 76% 51% sharp · at its level · several runs
HumanEval+ 39.6% ▁·▁▁▁▃▄●▄█▃▁ beats 30/99 easy · avg 70% 20% sharp · at its level
MBPP+ 58.7% ▁···▁▁▂▅●█▆▂ beats 34/75 easy · avg 66% 4.0% at its level
MBPP 70.2% ▄▄▄██▄·▄█·●▄ beats 11/12 easy · avg 81% 3.7% at its level
DS-1000 28.9% ▄▄·▄██▄●·█·▄ beats 9/12 medium · avg 49% 3.4% at its level
CRUXEval-I 41.9% ▁··▁▅▂●▇▁··▁ beats 13/22 medium · avg 52% 1.2% at its level
CRUXEval-O 43.5% ▁·▂▃▁●▁▂···▁ beats 17/22 medium · avg 51% 1.1% blunt · at its level
math · #313 of 431 · 3.2% of its score
Beats [Molmo 2 8B](molmo-2-8b.md), [GPT-4 (0613)](gpt-4-0613.md), [Athene-70B](athene-70b.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 41.9% ▄▄▄·▄█·█●·█▄ beats 9/12 easy · avg 75% 3.2% sharp · at its level
other tests (2)
test result vs the others in its run difficulty counts why
EvoEval (overall) 32.9% ▂▂▃▄▄●▁▄▅▃▁▃ beats 19/42 medium · avg 52% 8.3% at its level
BigCodeBench (Complete) 41.8% ▂▁▁▄▂▅▇●▆▂▂▂ beats 31/58 medium · avg 48% 3.4% at its level
source results share of the score
EvalPlus / EvalPerf published evaluatio… 3 50% ▇▇▇▇▇▇
Top Leaderboard Ranking = Top Coding Pr… 2 24% ▇▇▇
StarCoder 2 and The Stack v2: The Next … 4 20% ▇▇
BigCodeBench paper 1 3% ▇
CRUXEval published evaluation results 2 2% ▇
prior: pull toward the middle 0% ▇
weighted average = 40.8
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 6.7B Base (Q4_K_M, needs ~4.1 GB, from [huggingface.co/TheBloke](https://huggingface.co/TheBloke/deepseek-coder-6.7B-base-GGUF/tree/65fb23ad31e6d8f90286d532bcf0fbcdae248ec4))
```sh
llama cli -hf TheBloke/deepseek-coder-6.7B-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-6.7B-base-GGUF:Q4_K_M
```
Other versions: Q8_0 (~7.2 GB) `llama cli -hf TheBloke/deepseek-coder-6.7B-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 6.7B Base (Q4_K_M, needs ~4.1 GB, from [huggingface.co/TheBloke](https://huggingface.co/TheBloke/deepseek-coder-6.7B-base-GGUF/tree/65fb23ad31e6d8f90286d532bcf0fbcdae248ec4))
```sh
lms get https://huggingface.co/TheBloke/deepseek-coder-6.7B-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 (~7.2 GB) `lms get https://huggingface.co/TheBloke/deepseek-coder-6.7B-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 6.7B Base (Q4_K_M, needs ~4.1 GB, from [ollama.com](https://ollama.com/library/deepseek-coder/tags))
```sh
ollama run deepseek-coder:6.7b-base-q4_K_M
```
Other versions: Q8_0 (~7.2 GB) `ollama run deepseek-coder:6.7b-base-q8_0`
Model repository and documentation: [huggingface.co/deepseek-ai](https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-base)
[interactive version](https://whatmodel.sh/model/deepseek-coder-6-7b-base) · [JSON](https://whatmodel.sh/api?format=json&all=1)