Instructions to use jprafael/mpt-7b-instruct-sharded with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jprafael/mpt-7b-instruct-sharded with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="jprafael/mpt-7b-instruct-sharded", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("jprafael/mpt-7b-instruct-sharded", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("jprafael/mpt-7b-instruct-sharded", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use jprafael/mpt-7b-instruct-sharded with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jprafael/mpt-7b-instruct-sharded"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jprafael/mpt-7b-instruct-sharded",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/jprafael/mpt-7b-instruct-sharded

SGLang

How to use jprafael/mpt-7b-instruct-sharded with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "jprafael/mpt-7b-instruct-sharded" \
    --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": "jprafael/mpt-7b-instruct-sharded",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

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 "jprafael/mpt-7b-instruct-sharded" \
        --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": "jprafael/mpt-7b-instruct-sharded",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use jprafael/mpt-7b-instruct-sharded with Docker Model Runner:
```
docker model run hf.co/jprafael/mpt-7b-instruct-sharded
```

pszemraj commited on May 9, 2023

Commit

ae54cae

1 Parent(s): 9741fe8

✨ gradient checkpointing

Browse files

Files changed (1) hide show

modeling_mpt.py +4 -1

modeling_mpt.py CHANGED Viewed

@@ -33,7 +33,10 @@ class MPTPreTrainedModel(PreTrainedModel):
     base_model_prefix = "model"
     supports_gradient_checkpointing = True
     _no_split_modules = ["MPTBlock"]
 class MPTModel(MPTPreTrainedModel):
     def __init__(self, config: MPTConfig):

     base_model_prefix = "model"
     supports_gradient_checkpointing = True
     _no_split_modules = ["MPTBlock"]
+    def _set_gradient_checkpointing(self, module, value=False):
+        if isinstance(module, MPTModel):
+            module.gradient_checkpointing = value
 class MPTModel(MPTPreTrainedModel):
     def __init__(self, config: MPTConfig):