Model Stock: All we need is just a few fine-tuned models
Paper • 2403.19522 • Published • 15
How to use Vortex5/Moonlit-Shadow-12B with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="Vortex5/Moonlit-Shadow-12B")
messages = [
{"role": "user", "content": "Who are you?"},
]
pipe(messages) # Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("Vortex5/Moonlit-Shadow-12B")
model = AutoModelForCausalLM.from_pretrained("Vortex5/Moonlit-Shadow-12B")
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]:]))How to use Vortex5/Moonlit-Shadow-12B with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Vortex5/Moonlit-Shadow-12B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "Vortex5/Moonlit-Shadow-12B",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}'docker model run hf.co/Vortex5/Moonlit-Shadow-12B
How to use Vortex5/Moonlit-Shadow-12B with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "Vortex5/Moonlit-Shadow-12B" \
--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": "Vortex5/Moonlit-Shadow-12B",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}'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 "Vortex5/Moonlit-Shadow-12B" \
--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": "Vortex5/Moonlit-Shadow-12B",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}'How to use Vortex5/Moonlit-Shadow-12B with Docker Model Runner:
docker model run hf.co/Vortex5/Moonlit-Shadow-12B
This is a merge of pre-trained language models created using mergekit.
This model was merged using the Model Stock merge method using mistralai/Mistral-Nemo-Instruct-2407 as a base.
The following models were included in the merge:
The following YAML configuration was used to produce this model:
base_model: mistralai/Mistral-Nemo-Instruct-2407
models:
- model: LatitudeGames/Muse-12B
- model: anthracite-org/magnum-v4-12b
- model: yamatazen/NeonMaid-12B-v2
- model: SicariusSicariiStuff/Impish_Nemo_12B
- model: crestf411/MN-Slush
- model: Epiculous/Violet_Twilight-v0.2
- model: LatitudeGames/Wayfarer-12B
- model: inflatebot/MN-12B-Mag-Mell-R1
- model: nothingiisreal/MN-12B-Celeste-V1.9
- model: Nitral-AI/Captain-Eris_Violet-V0.420-12B
- model: PocketDoc/Dans-SakuraKaze-V1.0.0-12b
merge_method: model_stock
dtype: bfloat16
parameters:
normalize: true
tokenizer:
source: union