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 / configuration_riemann.py

Mentors4EDU

Add files using upload-large-folder tool

0ecd84b verified about 23 hours ago

Raw History Blame Contribute Delete

1.23 kB

from __future__ import annotations
from transformers import PretrainedConfig


class RiemannConfig(PretrainedConfig):
    model_type = "riemann_openpeer"

    def __init__(
        self,
        vocab_size: int = 50257,
        hidden_size: int = 768,
        num_hidden_layers: int = 12,
        num_attention_heads: int = 12,
        intermediate_size: int = 3072,
        max_position_embeddings: int = 1024,
        layer_norm_eps: float = 1e-05,
        hidden_dropout: float = 0.1,
        attention_dropout: float = 0.1,
        use_gradient_checkpointing: bool = False,
        **kwargs,
    ):
        self.vocab_size = vocab_size
        self.hidden_size = hidden_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.intermediate_size = intermediate_size
        self.max_position_embeddings = max_position_embeddings
        self.layer_norm_eps = layer_norm_eps
        self.hidden_dropout = hidden_dropout
        self.attention_dropout = attention_dropout
        self.use_gradient_checkpointing = use_gradient_checkpointing
        kwargs.setdefault("tie_word_embeddings", False)
        super().__init__(**kwargs)

	from __future__ import annotations
	from transformers import PretrainedConfig


	class RiemannConfig(PretrainedConfig):
	model_type = "riemann_openpeer"

	def __init__(
	self,
	vocab_size: int = 50257,
	hidden_size: int = 768,
	num_hidden_layers: int = 12,
	num_attention_heads: int = 12,
	intermediate_size: int = 3072,
	max_position_embeddings: int = 1024,
	layer_norm_eps: float = 1e-05,
	hidden_dropout: float = 0.1,
	attention_dropout: float = 0.1,
	use_gradient_checkpointing: bool = False,
	**kwargs,
	):
	self.vocab_size = vocab_size
	self.hidden_size = hidden_size
	self.num_hidden_layers = num_hidden_layers
	self.num_attention_heads = num_attention_heads
	self.intermediate_size = intermediate_size
	self.max_position_embeddings = max_position_embeddings
	self.layer_norm_eps = layer_norm_eps
	self.hidden_dropout = hidden_dropout
	self.attention_dropout = attention_dropout
	self.use_gradient_checkpointing = use_gradient_checkpointing
	kwargs.setdefault("tie_word_embeddings", False)
	super().__init__(**kwargs)