Instructions to use v000000/Psyonic-Rose-20B-Higher-Quality with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use v000000/Psyonic-Rose-20B-Higher-Quality with Transformers:

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

pipe = pipeline("text-generation", model="v000000/Psyonic-Rose-20B-Higher-Quality")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("v000000/Psyonic-Rose-20B-Higher-Quality")
model = AutoModelForCausalLM.from_pretrained("v000000/Psyonic-Rose-20B-Higher-Quality")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use v000000/Psyonic-Rose-20B-Higher-Quality with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "v000000/Psyonic-Rose-20B-Higher-Quality"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "v000000/Psyonic-Rose-20B-Higher-Quality",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/v000000/Psyonic-Rose-20B-Higher-Quality

SGLang

How to use v000000/Psyonic-Rose-20B-Higher-Quality 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 "v000000/Psyonic-Rose-20B-Higher-Quality" \
    --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": "v000000/Psyonic-Rose-20B-Higher-Quality",
		"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 "v000000/Psyonic-Rose-20B-Higher-Quality" \
        --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": "v000000/Psyonic-Rose-20B-Higher-Quality",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use v000000/Psyonic-Rose-20B-Higher-Quality with Docker Model Runner:
```
docker model run hf.co/v000000/Psyonic-Rose-20B-Higher-Quality
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Psyonic-Rose 20B FP32

Speculative recreation of jebcarter Psyonic-Rose-20B (Llama2)

Thanks mradermacher for the quants!

merge

This is a merge of pre-trained language models created using mergekit.

Merge Details

Merge Method

This model was merged using the linear merge method.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

models:
  - model: DavidAU/Psyonic-Cetacean-V1-20B-Ultra-Quality-Float32
    parameters:
      weight: 1.0
  - model: tavtav/Rose-20B(fp16)
    parameters:
      weight: 0.05
merge_method: linear
dtype: float32

credits jebcarter
credits DavidAU
credtis tavtav
credits NeverSleep
credits CalderaAI

{{{Alpaca instruct format}}}

Downloads last month: 2

Safetensors

Model size

20B params

Tensor type

F32

Model tree for v000000/Psyonic-Rose-20B-Higher-Quality

Base model

tavtav/Rose-20B

Finetuned

(1)

this model

Quantizations

3 models

Paper for v000000/Psyonic-Rose-20B-Higher-Quality

Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time

Paper • 2203.05482 • Published Mar 10, 2022 • 9