Instructions to use bartowski/code-millenials-13b-exl2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use bartowski/code-millenials-13b-exl2 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="bartowski/code-millenials-13b-exl2")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("bartowski/code-millenials-13b-exl2", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use bartowski/code-millenials-13b-exl2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bartowski/code-millenials-13b-exl2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bartowski/code-millenials-13b-exl2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/bartowski/code-millenials-13b-exl2

SGLang

How to use bartowski/code-millenials-13b-exl2 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "bartowski/code-millenials-13b-exl2" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bartowski/code-millenials-13b-exl2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "bartowski/code-millenials-13b-exl2" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bartowski/code-millenials-13b-exl2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use bartowski/code-millenials-13b-exl2 with Docker Model Runner:
```
docker model run hf.co/bartowski/code-millenials-13b-exl2
```

Exllama v2 Quantizations of code-millenials-13b

Using turboderp's ExLlamaV2 v0.0.11 for quantization.

The "main" branch only contains the measurement.json, download one of the other branches for the model (see below)

Each branch contains an individual bits per weight, with the main one containing only the meaurement.json for further conversions.

Original model: https://huggingface.co/budecosystem/code-millenials-13b

No GQA - VRAM requirements will be higher

Branch	Bits	lm_head bits	Size (4k)	Size (16k)	Description
6_5	6.5	8.0	14.4 GB	24.0 GB	Near unquantized performance at vastly reduced size, recommended.
5_0	5.0	6.0	12.1 GB	21.7 GB	Slightly lower perplexity vs 6.5, can fit in 12 GB card with even lower context.
4_25	4.25	6.0	10.9 GB	20.5 GB	GPTQ equivalent bits per weight.
3_75	3.75	6.0	10.1 GB	19.7 GB	Lower quality but still generally usable.
3_0	3.0	6.0	9.1 GB	18.7 GB	Very low quality, not recommended unless you have to.

VRAM requirements listed for both 4k context and 16k context since without GQA the differences are massive (9.6 GB)

Download instructions

With git:

git clone --single-branch --branch 6_5 https://huggingface.co/bartowski/code-millenials-13b-exl2 code-millenials-13b-exl2-6_5

With huggingface hub (credit to TheBloke for instructions):

pip3 install huggingface-hub

To download the main (only useful if you only care about measurement.json) branch to a folder called code-millenials-13b-exl2:

mkdir code-millenials-13b-exl2
huggingface-cli download bartowski/code-millenials-13b-exl2 --local-dir code-millenials-13b-exl2 --local-dir-use-symlinks False

To download from a different branch, add the --revision parameter:

mkdir code-millenials-13b-exl2-6_5
huggingface-cli download bartowski/code-millenials-13b-exl2 --revision 6_5 --local-dir code-millenials-13b-exl2-6_5 --local-dir-use-symlinks False

Downloads last month: -; Downloads are not tracked for this model. How to track

Evaluation results

pass@1 on HumanEval
self-reported

0.762