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
```

mpt-7b-instruct-sharded / README.md

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	---
	license: apache-2.0
	language:
	- en
	pipeline_tag: text-generation
	inference: false
	datasets:
	- the_pile_books3
	tags:
	- mosaicML
	- sharded
	- instruct
	---

	# mpt-7b-instruct: sharded


	This is a version of the [mpt-7b-instruct](https://huggingface.co/mosaicml/mpt-7b-instruct) model, sharded to 2 GB chunks for low-RAM loading (i.e. Colab).
	The weights are stored in `bfloat16` so in theory you can run this on CPU, though it may take forever.
	Original code and credits go to [mpt-7b-storywriter-sharded](https://huggingface.co/ethzanalytics/mpt-7b-storywriter-sharded).
	See the [community discussion](https://huggingface.co/ethzanalytics/mpt-7b-storywriter-sharded/discussions/2) on how to replicate this.

	Please refer to the previously linked repo for details on usage/implementation/etc. This model was downloaded from the original repo under Apache-2.0 and is redistributed under the same license.


	## Basic Usage

	> Note when using: this is not an instruction-tuned model, so you need to give it sufficient input text to continue generating something on-topic with your prompt
	>
	Install/upgrade packages:

	```bash
	pip install -U torch transformers accelerate einops
	```

	Load the model:

	```python
	import torch
	from transformers import AutoModelForCausalLM, AutoTokenizer

	model_name = 'jprafael/mpt-7b-instruct-sharded'
	model = AutoModelForCausalLM.from_pretrained(
	model_name,
	torch_dtype=torch.bfloat16,
	trust_remote_code=True,
	revision='8d8911ad980f48f8a791e5f5876dea891dcbc064', # optional, but a good idea
	device_map='auto',
	load_in_8bit=False, # install bitsandbytes then set to true for 8-bit
	)
	model = torch.compile(model)
	tokenizer = AutoTokenizer.from_pretrained(model_name)
	```

	Then you can use `model.generate()` as you would normally - see the notebook for details.


	---