Instructions to use open-sci/open-sci-ref-v0.02-1.7b-dclm-1T-4096-rope_theta-100k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use open-sci/open-sci-ref-v0.02-1.7b-dclm-1T-4096-rope_theta-100k with Transformers:

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

pipe = pipeline("text-generation", model="open-sci/open-sci-ref-v0.02-1.7b-dclm-1T-4096-rope_theta-100k", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("open-sci/open-sci-ref-v0.02-1.7b-dclm-1T-4096-rope_theta-100k", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use open-sci/open-sci-ref-v0.02-1.7b-dclm-1T-4096-rope_theta-100k with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "open-sci/open-sci-ref-v0.02-1.7b-dclm-1T-4096-rope_theta-100k"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "open-sci/open-sci-ref-v0.02-1.7b-dclm-1T-4096-rope_theta-100k",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/open-sci/open-sci-ref-v0.02-1.7b-dclm-1T-4096-rope_theta-100k

SGLang

How to use open-sci/open-sci-ref-v0.02-1.7b-dclm-1T-4096-rope_theta-100k 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 "open-sci/open-sci-ref-v0.02-1.7b-dclm-1T-4096-rope_theta-100k" \
    --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": "open-sci/open-sci-ref-v0.02-1.7b-dclm-1T-4096-rope_theta-100k",
		"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 "open-sci/open-sci-ref-v0.02-1.7b-dclm-1T-4096-rope_theta-100k" \
        --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": "open-sci/open-sci-ref-v0.02-1.7b-dclm-1T-4096-rope_theta-100k",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use open-sci/open-sci-ref-v0.02-1.7b-dclm-1T-4096-rope_theta-100k with Docker Model Runner:
```
docker model run hf.co/open-sci/open-sci-ref-v0.02-1.7b-dclm-1T-4096-rope_theta-100k
```

Harsh1729 commited on May 30

Commit

7e213fb

verified ·

1 Parent(s): bec1df3

Fix model card title (v0.02)

Browse files

Files changed (1) hide show

README.md +1 -1

README.md CHANGED Viewed

@@ -3,7 +3,7 @@ library_name: transformers
 pipeline_tag: text-generation
 ---
-# open-sci-ref-v0.01-1.7b-dclm-1T-4096-rope_theta-100k
 1.7B open-sci-ref model trained on **DCLM** for **1T tokens** (sequence length 4096, RoPE theta = 100000).

 pipeline_tag: text-generation
 ---
+# open-sci-ref-v0.02-1.7b-dclm-1T-4096-rope_theta-100k
 1.7B open-sci-ref model trained on **DCLM** for **1T tokens** (sequence length 4096, RoPE theta = 100000).