[whatmodel.sh](../../README.md) / DeepSeek-Coder 6.7B Instruct # DeepSeek-Coder 6.7B Instruct DeepSeek · released 2023-10 · 6.74B params · deepseek **score 48.9** ±0.6 · #392 of 732 · from 11 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 Instruct among every model on that task or test, worst to best. 
task    vs the others         rank  counts  tests
coding  ▁▁▁▃▅▇●▇▅▄▃▁   #290 of 469     76%      6
coding · #290 of 469 · 76% of its score Beats [Qwen3-VL 32B Instruct](qwen3-vl-32b-instruct.md), [Solar Open 100B (102B-A12B)](solar-open-100b.md), [Qwen3-VL 30B-A3B Instruct](qwen3-vl-30b-a3b-instruct.md) here, though they rank higher overall. Trails [DeepSeek LLM 67B Chat](deepseek-llm-67b-chat.md), [Yi 1.5 9B Chat](yi-1-5-9b-chat.md), [Mistral NeMo Instruct (2407)](mistral-nemo-instruct-2407.md) here, though they rank lower overall. 
test                                result  vs the others in its run  difficulty        counts  why
HumanEval                            74.4%  ▁·▁▁▃▄▆▅●▃▁▁ beats 69/99  easy · avg 76%       40%  sharp · at its level · several runs
HumanEval+                           71.3%  ▁·▁▁▁▃▄▄▄●▃▁ beats 76/99  easy · avg 70%       22%  sharp · at its level
LiveCodeBench official (Jan 1–Nov…   17.9%  ▁▁·▁●▄▆█▅▂·▂ beats 4/31   hard · avg 23%      7.1%  sharp
MBPP+                                65.6%  ▁···▁▁▂▅▇●▆▂ beats 53/75  easy · avg 66%      4.4%  at its level
CRUXEval-I                           37.4%  ▁··▁▅▂●▇▁··▁ beats 9/22   medium · avg 52%    1.5%  at its level
CRUXEval-O                           41.2%  ▁·▂▃▁●▁▂···▁ beats 12/22  medium · avg 51%    1.5%  blunt · at its level
other tests (4) 
test                                result  vs the others in its run  difficulty        counts  why
EvoEval (overall)                    46.9%  ▂▂▃▄▄█▁▄●▃▁▃ beats 32/42  medium · avg 52%     12%  at its level
BigCodeBench (Instruct)              35.3%  ▃▂·▆▆█●▆▅▃▃▃ beats 16/33  medium · avg 39%    4.8%  at its level
BigCodeBench (Complete)              43.2%  ▂▁▁▄▂▅▇●▆▂▂▂ beats 35/58  medium · avg 48%    4.6%  at its level
EvalPerf correctness (118 tasks, …   73.6%  ▂·▂▂▂▆●▆▆▆▃█ beats 9/29   easy · avg 74%      2.5%  at its level
## How the score was worked out Each result implies a score on its own, after adjusting for how hard that test is and who ran it. The model's score is their weighted average, with a small pull toward the middle for thinly tested models. Each test's weight is its *counts* column above; here it is added up by who ran the tests. [Method](../method.md) 
source                                    results  share of the score
EvalPlus / EvalPerf published evaluatio…        4   54% ▇▇▇▇▇▇
Top Leaderboard Ranking = Top Coding Pr…        2   27% ▇▇▇
BigCodeBench paper                              2    9% ▇
LiveCodeBench official website results          1    7% ▇
CRUXEval published evaluation results           2    3% ▇
prior: pull toward the middle                        0% ▇
weighted average                                   = 48.9
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)) ## 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 Instruct (Q4_K_M, needs ~4.1 GB, from [huggingface.co/TheBloke](https://huggingface.co/TheBloke/deepseek-coder-6.7B-instruct-GGUF/tree/9e221e6b41cb1bf1c5d8f9718e81e3dc781f7557)) ```sh llama cli -hf TheBloke/deepseek-coder-6.7B-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-6.7B-instruct-GGUF:Q4_K_M ``` Other versions: Q8_0 (~7.2 GB) `llama cli -hf TheBloke/deepseek-coder-6.7B-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 6.7B Instruct (Q4_K_M, needs ~4.1 GB, from [huggingface.co/TheBloke](https://huggingface.co/TheBloke/deepseek-coder-6.7B-instruct-GGUF/tree/9e221e6b41cb1bf1c5d8f9718e81e3dc781f7557)) ```sh lms get https://huggingface.co/TheBloke/deepseek-coder-6.7B-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 (~7.2 GB) `lms get https://huggingface.co/TheBloke/deepseek-coder-6.7B-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 6.7B Instruct (Q4_K_M, needs ~4.1 GB, from [ollama.com](https://ollama.com/library/deepseek-coder/tags)) ```sh ollama run deepseek-coder:6.7b-instruct-q4_K_M ``` Other versions: Q8_0 (~7.2 GB) `ollama run deepseek-coder:6.7b-instruct-q8_0` Model repository and documentation: [huggingface.co/deepseek-ai](https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-instruct) [interactive version](https://whatmodel.sh/model/deepseek-coder-6-7b-instruct) · [JSON](https://whatmodel.sh/api?format=json&all=1)