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

bec1df3

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1 Parent(s): 459985d

Add model card with eval scores

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+---
+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).
+The `main` branch holds the **final checkpoint** (iter 238419). Intermediate checkpoints (iters 58000-238000, every 2000) are available as branches named `iter_XXXXXXX`.
+## Evaluation
+Final checkpoint on the `open-sci-0.01` suite (lm-eval-harness). Metrics collected with `oellm collect-results`.
+| Task | n-shot | Metric | Score |
+|---|---|---|---|
+| arc_challenge | 10 | acc_norm | 0.4761 |
+| arc_easy | 10 | acc_norm | 0.7689 |
+| boolq | 10 | acc | 0.7664 |
+| commonsense_qa | 10 | acc | 0.4676 |
+| copa | 0 | acc | 0.8200 |
+| hellaswag | 10 | acc_norm | 0.7367 |
+| lambada_openai | 0 | acc | 0.7019 |
+| mmlu | 5 | acc | 0.4002 |
+| openbookqa | 0 | acc_norm | 0.4080 |
+| piqa | 10 | acc_norm | 0.7845 |
+| social_iqa | 0 | acc | 0.4463 |
+| winogrande | 0 | acc | 0.6606 |
+| **average** | | | **0.6198** |