Instructions to use gjonesQ02/S1_InstructionGeneratorBeta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use gjonesQ02/S1_InstructionGeneratorBeta with Transformers:

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

pipe = pipeline("text-generation", model="gjonesQ02/S1_InstructionGeneratorBeta")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("gjonesQ02/S1_InstructionGeneratorBeta")
model = AutoModelForMultimodalLM.from_pretrained("gjonesQ02/S1_InstructionGeneratorBeta")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use gjonesQ02/S1_InstructionGeneratorBeta with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "gjonesQ02/S1_InstructionGeneratorBeta"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "gjonesQ02/S1_InstructionGeneratorBeta",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/gjonesQ02/S1_InstructionGeneratorBeta

SGLang

How to use gjonesQ02/S1_InstructionGeneratorBeta 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 "gjonesQ02/S1_InstructionGeneratorBeta" \
    --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": "gjonesQ02/S1_InstructionGeneratorBeta",
		"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 "gjonesQ02/S1_InstructionGeneratorBeta" \
        --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": "gjonesQ02/S1_InstructionGeneratorBeta",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use gjonesQ02/S1_InstructionGeneratorBeta with Docker Model Runner:
```
docker model run hf.co/gjonesQ02/S1_InstructionGeneratorBeta
```

gjonesQ02 commited on Mar 31, 2024

Commit

4c00bbc

verified ·

1 Parent(s): 1ec8f51

Training in progress, step 1000

Browse files

Files changed (2) hide show

model.safetensors +1 -1
runs/Mar31_11-08-51_ilpa1209/events.out.tfevents.1711897766.ilpa1209.2365860.0 +2 -2

model.safetensors CHANGED Viewed

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:53a4effc460550385369a750cdd2315adf1446e82f3e85735e9c31f1d1d5d189
 size 327657928

 version https://git-lfs.github.com/spec/v1
+oid sha256:68ffc03cff965114750e531b5c40bd0dd35eca26a28d028792d8b51563985fc9
 size 327657928

runs/Mar31_11-08-51_ilpa1209/events.out.tfevents.1711897766.ilpa1209.2365860.0 CHANGED Viewed

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:6ab918b3522d9c90375cfb460191abebc0318b85ff413b93b74f68c76531c99f
-size 5769

 version https://git-lfs.github.com/spec/v1
+oid sha256:13ee32bb154e32a43242b918d951ccc73b732860ca3da83ff316dbe46a9298f9
+size 6522