[whatmodel.sh](../../README.md) / Magicoder-S-DS 6.7B
# Magicoder-S-DS 6.7B
UIUC (ise-uiuc) · released 2023-12 · 6.74B params · deepseek
**score 49.9** ±0.7 · #373 of 732 · from 10 benchmark results across 4 sources
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks Magicoder-S-DS 6.7B among every model on that task or test, worst to best.
task vs the others rank counts tests
coding ▁▁▁▃▅▇●▇▅▄▃▁ #281 of 469 73% 5
coding · #281 of 469 · 73% 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 76.8% ▁·▁▁▃▄▆▅●▃▁▁ beats 78/99 easy · avg 76% 42% sharp · several runs
HumanEval+ 71.3% ▁·▁▁▁▃▄▄▄●▃▁ beats 74/99 easy · avg 70% 23% sharp · at its level
MBPP+ 69.0% ▁···▁▁▂▅▇█●▂ beats 59/75 easy · avg 66% 4.9% at its level
CRUXEval-I 41.7% ▁··▁▅▂●▇▁··▁ beats 12/22 medium · avg 52% 1.7% at its level
CRUXEval-O 44.4% ▁·▂▃▁█●▂···▁ beats 18/22 medium · avg 51% 1.6% blunt · at its level
other tests (4)
test result vs the others in its run difficulty counts why
EvoEval (overall) (maker) 50.0% ▂▂▃▄▄█▁▄●▃▁▃ beats 35/42 medium · avg 52% 13% at its level
BigCodeBench (Instruct) (maker) 36.0% ▃▂·▆▆█●▆▅▃▃▃ beats 19/33 medium · avg 39% 5.4% at its level
BigCodeBench (Complete) (maker) 47.5% ▂▁▁▄▂▅▇█●▂▂▂ beats 46/58 medium · avg 48% 5.2% at its level
EvalPerf correctness (118 tasks, … 69.8% ▂·▂▂▂●▅▆▆▆▃█ beats 7/29 easy · avg 74% 2.7% at its level
source results share of the score
EvalPlus / EvalPerf published evaluatio… 4 57% ▇▇▇▇▇▇▇
Top Leaderboard Ranking = Top Coding Pr… 2 29% ▇▇▇
BigCodeBench paper (submitted / maker-r… 2 11% ▇
CRUXEval published evaluation results 2 3% ▇
prior: pull toward the middle 0% ▇
weighted average = 49.9
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/)) · 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 Magicoder-S-DS 6.7B (Q4_K_M, needs ~4.1 GB, from [huggingface.co/TheBloke](https://huggingface.co/TheBloke/Magicoder-S-DS-6.7B-GGUF/tree/d41a33f9393ebc06c6beb44b1ded091b6d19496c))
```sh
llama cli -hf TheBloke/Magicoder-S-DS-6.7B-GGUF:Q4_K_M
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf TheBloke/Magicoder-S-DS-6.7B-GGUF:Q4_K_M
```
Other versions: Q8_0 (~7.2 GB) `llama cli -hf TheBloke/Magicoder-S-DS-6.7B-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 Magicoder-S-DS 6.7B (Q4_K_M, needs ~4.1 GB, from [huggingface.co/TheBloke](https://huggingface.co/TheBloke/Magicoder-S-DS-6.7B-GGUF/tree/d41a33f9393ebc06c6beb44b1ded091b6d19496c))
```sh
lms get https://huggingface.co/TheBloke/Magicoder-S-DS-6.7B-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/Magicoder-S-DS-6.7B-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 Magicoder-S-DS 6.7B (Q4_K_M, needs ~4.1 GB, from [huggingface.co/TheBloke](https://huggingface.co/TheBloke/Magicoder-S-DS-6.7B-GGUF/tree/d41a33f9393ebc06c6beb44b1ded091b6d19496c))
```sh
ollama run hf.co/TheBloke/Magicoder-S-DS-6.7B-GGUF:Q4_K_M
```
Other versions: Q8_0 (~7.2 GB) `ollama run hf.co/TheBloke/Magicoder-S-DS-6.7B-GGUF:Q8_0`
### Other ways to run it
1. Run it as published (fp32, needs ~26.96 GB, from [huggingface.co/ise-uiuc](https://huggingface.co/ise-uiuc/Magicoder-S-DS-6.7B/tree/b3ed7cb1578a3643ceaf2ebf996a3d8e85f75d8f))
```sh
uv run --with torch --with 'transformers<5' --with accelerate python -c "from transformers import pipeline; print(pipeline('text-generation', model='ise-uiuc/Magicoder-S-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/ise-uiuc](https://huggingface.co/ise-uiuc/Magicoder-S-DS-6.7B)
[interactive version](https://whatmodel.sh/model/magicoder-s-ds-6-7b) · [JSON](https://whatmodel.sh/api?format=json&all=1)