Instructions to use LyraNovaHeart/Prismatic-12b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LyraNovaHeart/Prismatic-12b with Transformers:

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

pipe = pipeline("text-generation", model="LyraNovaHeart/Prismatic-12b")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("LyraNovaHeart/Prismatic-12b")
model = AutoModelForCausalLM.from_pretrained("LyraNovaHeart/Prismatic-12b")

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use LyraNovaHeart/Prismatic-12b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LyraNovaHeart/Prismatic-12b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LyraNovaHeart/Prismatic-12b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/LyraNovaHeart/Prismatic-12b

SGLang

How to use LyraNovaHeart/Prismatic-12b 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 "LyraNovaHeart/Prismatic-12b" \
    --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": "LyraNovaHeart/Prismatic-12b",
		"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 "LyraNovaHeart/Prismatic-12b" \
        --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": "LyraNovaHeart/Prismatic-12b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use LyraNovaHeart/Prismatic-12b with Docker Model Runner:
```
docker model run hf.co/LyraNovaHeart/Prismatic-12b
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Prismatic 12b v0.0

The sparkling courage I longed for, what I got is small... My tears are surely the prism of tomorrow... Say "Hello!" to the ideal future, let's go see them~

Listen to the song on youtube: https://www.youtube.com/watch?v=v3I6EVlyPx4

One off merge for a friend, though it came out rather good, I like it, so try it?

mistralai/Mistral-Nemo-Base-2407 inflatebot/MN-12b-Mag-Mell-R1 nbeerbower/Mistral-Nemo-Prism-12B-v5

License for this model Apache 2.0

Format: Mistral Tekken or ChatML

Thank you to AuriAetherwiing for helping me merge the models and for providing compute (A40).

Details

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

Merge Details

Merge Method

This model was merged using the ties merge method using mistralai_Mistral-Nemo-Base-2407 as a base.

Models Merged

Models Merged The following models were included in the merge:

/inflatebot_MN-12B-Mag-Mell-R1 /nbeerbower_Mistral-Nemo-Prism-12B-v5

Configuration

The following YAML configuration was used to produce this model:

models:

model: /inflatebot_MN-12B-Mag-Mell-R1 parameters: weight: 0.3 density: 0.5
model: /nbeerbower_Mistral-Nemo-Prism-12B-v5 parameters: weight: 0.4 density: 0.75 base_model: /mistralai_Mistral-Nemo-Base-2407 parameters: epsilon: 0.05 normalize: true lambda: 1 merge_method: ties dtype: bfloat16

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Safetensors

Model size

12B params

Tensor type

BF16

Model tree for LyraNovaHeart/Prismatic-12b

Quantizations

8 models