[whatmodel.sh](../../README.md) / XwinCoder-34B
# XwinCoder-34B
Xwin-LM · released 2023-11 · llama2
**score 49.7** ±0.9 · #377 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 XwinCoder-34B among every model on that task or test, worst to best.
task vs the others rank counts tests
coding ▁▁▁▃▅▇●▇▅▄▃▁ #286 of 469 100% 3
coding · #286 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), [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 75.6% ▁·▁▁▃▄▆▅●▃▁▁ beats 72/99 easy · avg 76% 48% sharp
HumanEval+ 69.5% ▁·▁▁▁▃▄▄▄●▃▁ beats 68/99 easy · avg 70% 43% sharp · at its level
MBPP+ 64.8% ▁···▁▁▂▅▇●▆▂ beats 51/75 easy · avg 66% 8.9% at its level
source results share of the score
EvalPlus / EvalPerf published evaluatio… 3 100% ▇▇▇▇▇▇▇▇▇▇▇▇
prior: pull toward the middle 0% ▇
weighted average = 49.7
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 XwinCoder-34B (Q4_K_M, needs ~20.2 GB, from [huggingface.co/TheBloke](https://huggingface.co/TheBloke/XwinCoder-34B-GGUF/tree/feaeb25382770c132fd79c7bb4dcee3f6a2918a9))
```sh
llama cli -hf TheBloke/XwinCoder-34B-GGUF:Q4_K_M
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf TheBloke/XwinCoder-34B-GGUF:Q4_K_M
```
Other versions: Q8_0 (~35.9 GB) `llama cli -hf TheBloke/XwinCoder-34B-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 XwinCoder-34B (Q4_K_M, needs ~20.2 GB, from [huggingface.co/TheBloke](https://huggingface.co/TheBloke/XwinCoder-34B-GGUF/tree/feaeb25382770c132fd79c7bb4dcee3f6a2918a9))
```sh
lms get https://huggingface.co/TheBloke/XwinCoder-34B-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 (~35.9 GB) `lms get https://huggingface.co/TheBloke/XwinCoder-34B-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 XwinCoder-34B (Q4_K_M, needs ~20.2 GB, from [huggingface.co/TheBloke](https://huggingface.co/TheBloke/XwinCoder-34B-GGUF/tree/feaeb25382770c132fd79c7bb4dcee3f6a2918a9))
```sh
ollama run hf.co/TheBloke/XwinCoder-34B-GGUF:Q4_K_M
```
Other versions: Q8_0 (~35.9 GB) `ollama run hf.co/TheBloke/XwinCoder-34B-GGUF:Q8_0`
### Other ways to run it
1. Run it as published (BF16, needs ~67.49 GB, from [huggingface.co/Xwin-LM](https://huggingface.co/Xwin-LM/XwinCoder-34B/tree/c331e0f9203efcdfcc8916a24fcec52cd8897dcc))
```sh
uv run --with torch --with 'transformers<5' --with accelerate python -c "from transformers import pipeline; print(pipeline('text-generation', model='Xwin-LM/XwinCoder-34B', torch_dtype='auto', device_map='auto')('Once upon a time', max_new_tokens=40)[0]['generated_text'])"
```
Model repository and documentation: [huggingface.co/Xwin-LM](https://huggingface.co/Xwin-LM/XwinCoder-34B)
[interactive version](https://whatmodel.sh/model/xwincoder-34b) · [JSON](https://whatmodel.sh/api?format=json&all=1)