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", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

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
```

code-millenials-13b-exl2 / README.md

bartowski

measurement.json

cc21c9b verified over 2 years ago

preview code

Raw

History Blame Contribute Delete

2.86 kB

	---
	license: llama2
	library_name: transformers
	tags:
	- code
	model-index:
	- name: Code Millenials
	results:
	- task:
	type: text-generation
	dataset:
	type: openai_humaneval
	name: HumanEval
	metrics:
	- name: pass@1
	type: pass@1
	value: 0.7621
	verified: false
	quantized_by: bartowski
	pipeline_tag: text-generation
	---

	## Exllama v2 Quantizations of code-millenials-13b

	Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.0.11">turboderp's ExLlamaV2 v0.0.11</a> 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](https://huggingface.co/Bartowski/code-millenials-13b-exl2/tree/6_5) \| 6.5 \| 8.0 \| 14.4 GB \| 24.0 GB \| Near unquantized performance at vastly reduced size, recommended. \|
	\| [5_0](https://huggingface.co/Bartowski/code-millenials-13b-exl2/tree/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](https://huggingface.co/Bartowski/code-millenials-13b-exl2/tree/4_25) \| 4.25 \| 6.0 \| 10.9 GB \| 20.5 GB \| GPTQ equivalent bits per weight. \|
	\| [3_75](https://huggingface.co/Bartowski/code-millenials-13b-exl2/tree/3_75) \| 3.75 \| 6.0 \| 10.1 GB \| 19.7 GB \| Lower quality but still generally usable. \|
	\| [3_0](https://huggingface.co/Bartowski/code-millenials-13b-exl2/tree/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:

	```shell
	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):

	```shell
	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`:

	```shell
	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:

	```shell
	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
	```