Instructions to use yodayo-ai/nephra_v1.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use yodayo-ai/nephra_v1.0 with Transformers:

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

pipe = pipeline("text-generation", model="yodayo-ai/nephra_v1.0")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("yodayo-ai/nephra_v1.0")
model = AutoModelForCausalLM.from_pretrained("yodayo-ai/nephra_v1.0", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use yodayo-ai/nephra_v1.0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "yodayo-ai/nephra_v1.0"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "yodayo-ai/nephra_v1.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/yodayo-ai/nephra_v1.0

SGLang

How to use yodayo-ai/nephra_v1.0 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 "yodayo-ai/nephra_v1.0" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "yodayo-ai/nephra_v1.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "yodayo-ai/nephra_v1.0" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "yodayo-ai/nephra_v1.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use yodayo-ai/nephra_v1.0 with Docker Model Runner:
```
docker model run hf.co/yodayo-ai/nephra_v1.0
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Overview

nephra v1 is primarily a model built for roleplaying sessions, trained on roleplay and instruction-style datasets.

Model Details

Developed by: Sao10K
Model type: Text-based Large Language Model
License: Meta Llama 3 Community License Agreement
Finetuned from model: Meta-Llama-3-8B

Inference Guidelines

import transformers
import torch

model_id = "yodayo-ai/nephra_v1.0"

pipeline = transformers.pipeline(
  "text-generation",
  model=model_id,
  model_kwargs={"torch_dtype": torch.bfloat16},
  device_map="auto",
)

messages = [
  {"role": "system", "content": "You are to play the role of a cheerful assistant."},
  {"role": "user", "content": "Hi there, how's your day?"},
]

prompt = pipeline.tokenizer.apply_chat_template(
  messages,
  tokenize=False,
  add_generation_prompt=True
)

outputs = pipeline(
  prompt,
  max_new_tokens=512,
  eos_token_id=[
    pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>"),
    pipeline.tokenizer.eos_token_id,
  ],
  do_sample=True,
  temperature=1.12,
  min_p=0.075,
)
print(outputs[0]["generated_text"][len(prompt):])

Recommended Settings

To guide the model to generate high-quality responses, here are the ideal settings:

Prompt Format: Same Prompt Format as Llama-3-Instruct
Temperature - 1.12
min-p: 0.075
Repetition Penalty: 1.1
Custom Stopping Strings: "\n{{user}}", "<" , "```" , -> Has occasional broken generations.