QuixiAI/dolphin
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How to use iamplus/mpt-30b-v4 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="iamplus/mpt-30b-v4", trust_remote_code=True) # Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("iamplus/mpt-30b-v4", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("iamplus/mpt-30b-v4", trust_remote_code=True)How to use iamplus/mpt-30b-v4 with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "iamplus/mpt-30b-v4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "iamplus/mpt-30b-v4",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker model run hf.co/iamplus/mpt-30b-v4
How to use iamplus/mpt-30b-v4 with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "iamplus/mpt-30b-v4" \
--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": "iamplus/mpt-30b-v4",
"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 "iamplus/mpt-30b-v4" \
--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": "iamplus/mpt-30b-v4",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'How to use iamplus/mpt-30b-v4 with Docker Model Runner:
docker model run hf.co/iamplus/mpt-30b-v4
Base Model : mosaicml/mpt-30b
Tool : MosaicML's llm-foundry (https://github.com/mosaicml/llm-foundry)
Dataset : Entire flan1m-GPT4 dataset
Config yaml with Model Params : https://huggingface.co/iamplus/mpt-30b-v4/blob/main/mpt-30b_v4.yaml
Description : mosaicml/mpt-30b -> Finetuning on (Entire flan3m-GPT3.5 dataset for 4 epochs)
Prompt Format :
<system>: [system prompt]
<human>: [question]
<bot>:
docker model run hf.co/iamplus/mpt-30b-v4