Instructions to use normalcomputing/extended-mind-mpt-30b-chat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use normalcomputing/extended-mind-mpt-30b-chat with Transformers:

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

pipe = pipeline("text-generation", model="normalcomputing/extended-mind-mpt-30b-chat", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("normalcomputing/extended-mind-mpt-30b-chat", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use normalcomputing/extended-mind-mpt-30b-chat with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "normalcomputing/extended-mind-mpt-30b-chat"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "normalcomputing/extended-mind-mpt-30b-chat",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/normalcomputing/extended-mind-mpt-30b-chat

SGLang

How to use normalcomputing/extended-mind-mpt-30b-chat 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 "normalcomputing/extended-mind-mpt-30b-chat" \
    --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": "normalcomputing/extended-mind-mpt-30b-chat",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "normalcomputing/extended-mind-mpt-30b-chat" \
        --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": "normalcomputing/extended-mind-mpt-30b-chat",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use normalcomputing/extended-mind-mpt-30b-chat with Docker Model Runner:
```
docker model run hf.co/normalcomputing/extended-mind-mpt-30b-chat
```

extended-mind-mpt-30b-chat / tokenizer_config.json

phoebeklett

Add tokenizer files

46568df verified about 2 years ago

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1.35 kB

	{
	"add_prefix_space": false,
	"bos_token": "<\|endoftext\|>",
	"chat_template": "{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}{% set system_message = messages[0]['content'] %}{% elif not 'system' in messages[0]['role'] %}{% set loop_messages = messages %}{% set system_message = 'A conversation between a user and an LLM-based AI assistant. The assistant gives helpful and honest answers.' %}{% else %}{% set loop_messages = messages %}{% set system_message = false %}{% endif %}{% for message in loop_messages %}{% if loop.index0 == 0 %}{% if system_message != false %}{{ '<\|im_start\|>system\n' + system_message.strip() + '\n'}}{% endif %}{{ '<\|im_start\|>' + message['role'] + '\n' + message['content'] + '<\|im_end\|>' }}{% else %}{{ '\n' + '<\|im_start\|>' + message['role'] + '\n' + message['content'] + '<\|im_end\|>' }}{% endif %}{% if (add_generation_prompt == true and loop.last) %}{{ '\n' + '<\|im_start\|>' + 'assistant' + '\n' }}{% elif (message['role'] == 'assistant') %}{% endif %}{% endfor %}",
	"clean_up_tokenization_spaces": true,
	"eos_token": "<\|endoftext\|>",
	"max_length": null,
	"model_max_length": 8192,
	"pad_to_multiple_of": null,
	"pad_token": "<\|endoftext\|>",
	"pad_token_type_id": 0,
	"padding_side": "right",
	"tokenizer_class": "GPTNeoXTokenizer",
	"unk_token": "<\|endoftext\|>"
	}