Instructions to use bartowski/starcoder2-15b-instruct-exl2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bartowski/starcoder2-15b-instruct-exl2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="bartowski/starcoder2-15b-instruct-exl2")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("bartowski/starcoder2-15b-instruct-exl2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use bartowski/starcoder2-15b-instruct-exl2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "bartowski/starcoder2-15b-instruct-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/starcoder2-15b-instruct-exl2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/bartowski/starcoder2-15b-instruct-exl2
- SGLang
How to use bartowski/starcoder2-15b-instruct-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/starcoder2-15b-instruct-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/starcoder2-15b-instruct-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/starcoder2-15b-instruct-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/starcoder2-15b-instruct-exl2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use bartowski/starcoder2-15b-instruct-exl2 with Docker Model Runner:
docker model run hf.co/bartowski/starcoder2-15b-instruct-exl2
File size: 2,807 Bytes
641e6cc f2c7bde 641e6cc 3157c44 641e6cc | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 | ---
tags:
- code
- starcoder2
library_name: transformers
pipeline_tag: text-generation
license: bigcode-openrail-m
quantized_by: bartowski
---
## Exllama v2 Quantizations of starcoder2-15b-instruct
Using <a href="https://github.com/turboderp/exllamav2/">turboderp's ExLlamaV2 v0.0.15 preview</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/TechxGenus/starcoder2-15b-instruct
| Branch | Bits | lm_head bits | VRAM (4k) | VRAM (16k) | VRAM (32k) | Description |
| ----- | ---- | ------- | ------ | ------ | ------ | ------------ |
| [8_0](https://huggingface.co/bartowski/starcoder2-15b-instruct-exl2/tree/8_0) | 8.0 | 8.0 | 16.6 GB | 17.5 GB | 18.8 GB | Maximum quality that ExLlamaV2 can produce, near unquantized performance. |
| [6_5](https://huggingface.co/bartowski/starcoder2-15b-instruct-exl2/tree/6_5) | 6.5 | 8.0 | 13.9 GB | 14.9 GB | 16.2 GB | Near unquantized performance at vastly reduced size, **recommended**. |
| [5_0](https://huggingface.co/bartowski/starcoder2-15b-instruct-exl2/tree/5_0) | 5.0 | 6.0 | 11.2 GB | 12.2 GB | 13.5 GB | Slightly lower quality vs 6.5. |
| [4_25](https://huggingface.co/bartowski/starcoder2-15b-instruct-exl2/tree/4_25) | 4.25 | 6.0 | 9.8 GB | 10.7 GB | 12.0 GB | GPTQ equivalent bits per weight. |
| [3_5](https://huggingface.co/bartowski/starcoder2-15b-instruct-exl2/tree/3_5) | 3.5 | 6.0 | 8.4 GB | 9.3 GB | 10.6 GB | Lower quality, not recommended. |
## Download instructions
With git:
```shell
git clone --single-branch --branch 6_5 https://huggingface.co/bartowski/starcoder2-15b-instruct-exl2
```
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 `starcoder2-15b-instruct-exl2`:
```shell
mkdir starcoder2-15b-instruct-exl2
huggingface-cli download bartowski/starcoder2-15b-instruct-exl2 --local-dir starcoder2-15b-instruct-exl2 --local-dir-use-symlinks False
```
To download from a different branch, add the `--revision` parameter:
Linux:
```shell
mkdir starcoder2-15b-instruct-exl2-6_5
huggingface-cli download bartowski/starcoder2-15b-instruct-exl2 --revision 6_5 --local-dir starcoder2-15b-instruct-exl2-6_5 --local-dir-use-symlinks False
```
Windows (which apparently doesn't like _ in folders sometimes?):
```shell
mkdir starcoder2-15b-instruct-exl2-6.5
huggingface-cli download bartowski/starcoder2-15b-instruct-exl2 --revision 6_5 --local-dir starcoder2-15b-instruct-exl2-6.5 --local-dir-use-symlinks False
``` |