Instructions to use bartowski/starcoder2-15b-instruct-exl2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

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
```

bartowski commited on Mar 6, 2024

Commit

3157c44

verified ·

1 Parent(s): 641e6cc

Update VRAM table

Browse files

Files changed (1) hide show

README.md +7 -10

README.md CHANGED Viewed

@@ -22,16 +22,13 @@ Default arguments used except when the bits per weight is above 6.0, at that poi
 Original model: https://huggingface.co/TechxGenus/starcoder2-15b-instruct
-<a href="https://huggingface.co/bartowski/starcoder2-15b-instruct-exl2/tree/8_0">8.0 bits per weight</a>
-<a href="https://huggingface.co/bartowski/starcoder2-15b-instruct-exl2/tree/6_5">6.5 bits per weight</a>
-<a href="https://huggingface.co/bartowski/starcoder2-15b-instruct-exl2/tree/5_0">5.0 bits per weight</a>
-<a href="https://huggingface.co/bartowski/starcoder2-15b-instruct-exl2/tree/4_25">4.25 bits per weight</a>
-<a href="https://huggingface.co/bartowski/starcoder2-15b-instruct-exl2/tree/3_5">3.5 bits per weight</a>
 ## Download instructions

 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