Instructions to use OpenPeerAI/My-Name-is-Riemann-Prerelease with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OpenPeerAI/My-Name-is-Riemann-Prerelease with Transformers:

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

pipe = pipeline("text-generation", model="OpenPeerAI/My-Name-is-Riemann-Prerelease", trust_remote_code=True)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("OpenPeerAI/My-Name-is-Riemann-Prerelease", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use OpenPeerAI/My-Name-is-Riemann-Prerelease with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/OpenPeerAI/My-Name-is-Riemann-Prerelease

SGLang

How to use OpenPeerAI/My-Name-is-Riemann-Prerelease 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 "OpenPeerAI/My-Name-is-Riemann-Prerelease" \
    --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": "OpenPeerAI/My-Name-is-Riemann-Prerelease",
		"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 "OpenPeerAI/My-Name-is-Riemann-Prerelease" \
        --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": "OpenPeerAI/My-Name-is-Riemann-Prerelease",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use OpenPeerAI/My-Name-is-Riemann-Prerelease with Docker Model Runner:
```
docker model run hf.co/OpenPeerAI/My-Name-is-Riemann-Prerelease
```

My-Name-is-Riemann-Prerelease

Commit History

Oct 21 Yesterday

04:49
Declare _no_split_modules so device_map='auto' can shard the model across GPUs

Mentors4EDU

Verified signature
ec3e3c1

Oct 20 2 days ago

23:44
Model card: link the three demo Spaces

Mentors4EDU

Verified signature
9a031c4
22:25
Repository notes: correct remote-code description

Mentors4EDU

Verified signature
6c49abb
22:24
Restore relative import in remote code; required by transformers dynamic module loader

Mentors4EDU

Verified signature
011a89f
21:51
Remove push_to_hub.py; generalise cloud scripts (no account-specific defaults)

Mentors4EDU

Verified signature
4b08de9
21:40
Publish training pipeline under training/; update repository notes

Mentors4EDU

Verified signature
db3982a
21:35
Model card: point to controller summaries instead of removed logs

Mentors4EDU

Verified signature
9a30bb2
21:34
Add model card and LICENSE; remove training logs and internal step counter

Mentors4EDU

Verified signature
eb9419b
20:35
Make remote-code import absolute; remove stray marker file

Mentors4EDU

Verified signature
6dd342b
20:00
Remove operational logs, keep per-rung pretrain and NTK logs

Mentors4EDU

Verified signature
7f2989e
19:52
Upload folder using huggingface_hub

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5954290
19:52
Upload ntk_controller/stage5_6.66b.json with huggingface_hub

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19:52
Upload ntk_controller/stage5_6.66b.pt with huggingface_hub

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19:52
Upload ntk_controller/stage4_2.65b.json with huggingface_hub

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19:51
Upload ntk_controller/stage4_2.65b.pt with huggingface_hub

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19:51
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19:51
Upload ntk_controller/stage2_538m.json with huggingface_hub

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19:51
Upload ntk_controller/stage2_538m.pt with huggingface_hub

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302b22d
19:51
Upload NTK controller summary (stage6_12.85b)

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6153f77
19:51
Upload NTK gate controller (stage6_12.85b)

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a346a18
19:51
Add files using upload-large-folder tool

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0ecd84b

Oct 19 3 days ago

22:12
initial commit

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e6316c0

Commit History

Oct 21 Yesterday

Declare _no_split_modules so device_map='auto' can shard the model across GPUs

Oct 20 2 days ago

Model card: link the three demo Spaces

Repository notes: correct remote-code description

Restore relative import in remote code; required by transformers dynamic module loader

Remove push_to_hub.py; generalise cloud scripts (no account-specific defaults)

Publish training pipeline under training/; update repository notes

Model card: point to controller summaries instead of removed logs

Add model card and LICENSE; remove training logs and internal step counter

Make remote-code import absolute; remove stray marker file

Remove operational logs, keep per-rung pretrain and NTK logs

Upload folder using huggingface_hub

Upload ntk_controller/stage5_6.66b.json with huggingface_hub

Upload ntk_controller/stage5_6.66b.pt with huggingface_hub

Upload ntk_controller/stage4_2.65b.json with huggingface_hub

Upload ntk_controller/stage4_2.65b.pt with huggingface_hub

Upload ntk_controller/stage3_1.19b.json with huggingface_hub

Upload ntk_controller/stage3_1.19b.pt with huggingface_hub

Upload ntk_controller/stage2_538m.json with huggingface_hub

Upload ntk_controller/stage2_538m.pt with huggingface_hub

Upload NTK controller summary (stage6_12.85b)

Upload NTK gate controller (stage6_12.85b)

Add files using upload-large-folder tool

Oct 19 3 days ago

initial commit