iDUS
Collection
4 items β’ Updated
How to use Cartinoe5930/SOLAR-DUS-implement with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="Cartinoe5930/SOLAR-DUS-implement") # Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("Cartinoe5930/SOLAR-DUS-implement")
model = AutoModelForCausalLM.from_pretrained("Cartinoe5930/SOLAR-DUS-implement")How to use Cartinoe5930/SOLAR-DUS-implement with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Cartinoe5930/SOLAR-DUS-implement"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "Cartinoe5930/SOLAR-DUS-implement",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker model run hf.co/Cartinoe5930/SOLAR-DUS-implement
How to use Cartinoe5930/SOLAR-DUS-implement with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "Cartinoe5930/SOLAR-DUS-implement" \
--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": "Cartinoe5930/SOLAR-DUS-implement",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'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 "Cartinoe5930/SOLAR-DUS-implement" \
--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": "Cartinoe5930/SOLAR-DUS-implement",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'How to use Cartinoe5930/SOLAR-DUS-implement with Docker Model Runner:
docker model run hf.co/Cartinoe5930/SOLAR-DUS-implement
SOLAR-DUS-implement is a merge of the following model using LazyMergekit:
For more detailed information, please refer to GitHub Repository.
GitHub Repository: https://github.com/gauss5930/iDUS
slices:
- sources:
- model: Cartinoe5930/Llama2_init_Mistral
layer_range: [0, 24]
- sources:
- model: Cartinoe5930/Llama2_init_Mistral
layer_range: [8, 32]
merge_method: passthrough
dtype: float16
| Model | ARC | HellaSwag | MMLU | TruthfulQA | Winogrande | GSM8K | Average |
|---|---|---|---|---|---|---|---|
| SOLAR-10.7B-DUS-Implementation | 59.56 | 81.18 | 63.68 | 40.72 | 76.48 | 26.99 | 58.1 |
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "Cartinoe5930/SOLAR-DUS-implement"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])