Model Stock: All we need is just a few fine-tuned models
Paper • 2403.19522 • Published • 15
How to use mergekit-community/sexeh_time_testing with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="mergekit-community/sexeh_time_testing")
messages = [
{"role": "user", "content": "Who are you?"},
]
pipe(messages) # Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("mergekit-community/sexeh_time_testing")
model = AutoModelForCausalLM.from_pretrained("mergekit-community/sexeh_time_testing")
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 mergekit-community/sexeh_time_testing with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mergekit-community/sexeh_time_testing"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "mergekit-community/sexeh_time_testing",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}'docker model run hf.co/mergekit-community/sexeh_time_testing
How to use mergekit-community/sexeh_time_testing with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "mergekit-community/sexeh_time_testing" \
--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": "mergekit-community/sexeh_time_testing",
"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 "mergekit-community/sexeh_time_testing" \
--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": "mergekit-community/sexeh_time_testing",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}'How to use mergekit-community/sexeh_time_testing with Docker Model Runner:
docker model run hf.co/mergekit-community/sexeh_time_testing
This is a merge of pre-trained language models created using mergekit.
This model was merged using the Model Stock merge method using DreadPoor/Alita99-8B-LINEAR as a base.
The following models were included in the merge:
The following YAML configuration was used to produce this model:
models:
- model: tannedbum/L3-Nymeria-v2-8B+surya-narayanan/sociology
- model: tannedbum/L3-Nymeria-v2-8B+surya-narayanan/psychology
- model: tannedbum/L3-Nymeria-v2-8B+kloodia/lora-8b-physic
- model: tannedbum/L3-Nymeria-v2-8B+kloodia/lora-8b-bio
- model: tannedbum/L3-Nymeria-v2-8B+ResplendentAI/Llama3_RP_ORPO_LoRA
- model: tannedbum/L3-Nymeria-v2-8B+vincentyandex/lora_llama3_chunked_novel_bs128
- model: tannedbum/L3-Nymeria-v2-8B+eeeebbb2/3aff0ea7-4262-4abb-97b1-1879f340d32e
- model: tannedbum/L3-Nymeria-v2-8B+Blackroot/Llama-3-LongStory-LORA
- model: tannedbum/L3-Nymeria-v2-8B+surya-narayanan/formal_logic
- model: tannedbum/L3-Nymeria-v2-8B+surya-narayanan/health
- model: tannedbum/L3-Nymeria-v2-8B+surya-narayanan/conceptual_physics
- model: tannedbum/L3-Nymeria-v2-8B+surya-narayanan/professional_psychology
- model: tannedbum/L3-Nymeria-v2-8B+surya-narayanan/professional_medicine
- model: tannedbum/L3-Nymeria-v2-8B+surya-narayanan/anatomy
- model: tannedbum/L3-Nymeria-v2-8B+surya-narayanan/human_sexuality
merge_method: model_stock
base_model: DreadPoor/Alita99-8B-LINEAR
dtype: bfloat16