[whatmodel.sh](../../README.md) / Artigenz-Coder-DS-6.7B
# Artigenz-Coder-DS-6.7B
Artigenz · released 2024-04 · 6.74B params · other (deepseek)
**score 50.5** ±0.9 · #361 of 732 · from 3 benchmark results across 1 source
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks Artigenz-Coder-DS-6.7B among every model on that task or test, worst to best.
task vs the others rank counts tests
coding ▁▁▁▃▅▇●▇▅▄▃▁ #282 of 469 100% 3
coding · #282 of 469 · 100% of its score
Beats [Qwen3-VL 32B Instruct](qwen3-vl-32b-instruct.md), [Solar Open 100B (102B-A12B)](solar-open-100b.md), [GLM-4.7-Flash](glm-4-7-flash.md) here, though they rank higher overall. Trails [DeepSeek LLM 67B Chat](deepseek-llm-67b-chat.md), [Mistral NeMo Instruct (2407)](mistral-nemo-instruct-2407.md), [QwQ-32B-Preview](qwq-32b-preview.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
HumanEval 75.6% ▁·▁▁▃▄▆▅●▃▁▁ beats 73/99 easy · avg 76% 47% sharp
HumanEval+ 72.6% ▁·▁▁▁▃▄▄▄●▃▁ beats 81/99 easy · avg 70% 43% sharp · at its level
MBPP+ 69.6% ▁···▁▁▂▅▇█●▂ beats 63/75 easy · avg 66% 9.1% at its level
source results share of the score
EvalPlus / EvalPerf published evaluatio… 3 100% ▇▇▇▇▇▇▇▇▇▇▇▇
prior: pull toward the middle 0% ▇
weighted average = 50.5
Licences: EvalPlus / EvalPerf published evaluation runs ([Apache-2.0](../../licenses/evalplus-published-runs-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 Artigenz-Coder-DS-6.7B (Q4_K_M, needs ~4.1 GB, from [huggingface.co/brunopio](https://huggingface.co/brunopio/Artigenz-Coder-DS-6.7B-Q4_K_M-GGUF/tree/da28dd3b6837f5c26891f2fdfc980a270dcc8ca9))
```sh
llama cli -hf brunopio/Artigenz-Coder-DS-6.7B-Q4_K_M-GGUF:q4_k_m
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf brunopio/Artigenz-Coder-DS-6.7B-Q4_K_M-GGUF:q4_k_m
```
### 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 Artigenz-Coder-DS-6.7B (Q4_K_M, needs ~4.1 GB, from [huggingface.co/brunopio](https://huggingface.co/brunopio/Artigenz-Coder-DS-6.7B-Q4_K_M-GGUF/tree/da28dd3b6837f5c26891f2fdfc980a270dcc8ca9))
```sh
lms get https://huggingface.co/brunopio/Artigenz-Coder-DS-6.7B-Q4_K_M-GGUF@q4_k_m -y
```
3. Then run lms ls to see its name, and lms chat followed by that name
### Ollama
1. Install Ollama. [Download it (macOS / Windows)](https://ollama.com/download)
```sh
# Linux
curl -fsSL https://ollama.com/install.sh | sh
```
2. Run Artigenz-Coder-DS-6.7B (Q4_K_M, needs ~4.1 GB, from [huggingface.co/brunopio](https://huggingface.co/brunopio/Artigenz-Coder-DS-6.7B-Q4_K_M-GGUF/tree/da28dd3b6837f5c26891f2fdfc980a270dcc8ca9))
```sh
ollama run hf.co/brunopio/Artigenz-Coder-DS-6.7B-Q4_K_M-GGUF:q4_k_m
```
### Other ways to run it
1. Run it as published (BF16, needs ~13.48 GB, from [huggingface.co/Artigenz](https://huggingface.co/Artigenz/Artigenz-Coder-DS-6.7B/tree/a0dea4a1c6cfdef8043c8accffa803887f444f45))
```sh
uv run --with torch --with 'transformers<5' --with accelerate python -c "from transformers import pipeline; print(pipeline('text-generation', model='Artigenz/Artigenz-Coder-DS-6.7B', torch_dtype='auto', device_map='auto')('Once upon a time', max_new_tokens=40)[0]['generated_text'])"
```
Model repository and documentation: [huggingface.co/Artigenz](https://huggingface.co/Artigenz/Artigenz-Coder-DS-6.7B)
[interactive version](https://whatmodel.sh/model/artigenz-coder-ds-6-7b) · [JSON](https://whatmodel.sh/api?format=json&all=1)