Model Stock: All we need is just a few fine-tuned models
Paper • 2403.19522 • Published • 15
How to use sthenno/tempesthenno-sft-0314 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="sthenno/tempesthenno-sft-0314")
messages = [
{"role": "user", "content": "Who are you?"},
]
pipe(messages) # Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("sthenno/tempesthenno-sft-0314")
model = AutoModelForCausalLM.from_pretrained("sthenno/tempesthenno-sft-0314")
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 sthenno/tempesthenno-sft-0314 with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sthenno/tempesthenno-sft-0314"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "sthenno/tempesthenno-sft-0314",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}'docker model run hf.co/sthenno/tempesthenno-sft-0314
How to use sthenno/tempesthenno-sft-0314 with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "sthenno/tempesthenno-sft-0314" \
--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": "sthenno/tempesthenno-sft-0314",
"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 "sthenno/tempesthenno-sft-0314" \
--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": "sthenno/tempesthenno-sft-0314",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}'How to use sthenno/tempesthenno-sft-0314 with Docker Model Runner:
docker model run hf.co/sthenno/tempesthenno-sft-0314
This is a merge of pre-trained language models created using mergekit.
This model was merged using the Model Stock merge method using sthenno-com/miscii-14b-0218 as a base.
The following models were included in the merge:
The following YAML configuration was used to produce this model:
name: tempesthenno-sft-0314
merge_method: model_stock
base_model: sthenno-com/miscii-14b-0218
tokenizer:
source: base
dtype: bfloat16
parameters:
normalize: true
rescale: false
models:
- model: sthenno-com/miscii-14b-0218
- model: sthenno/tempesthenno-sft-0309-ckpt10
- model: /home/ubuntu/tmp/models/tempesthenno-sft-0314-stage1-ckpt50
- model: /home/ubuntu/tmp/models/tempesthenno-sft-0314-stage3-ckpt30