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

Libraries

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

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

pipe = pipeline("text-generation", model="normalcomputing/extended-mind-mpt-30b", trust_remote_code=True)

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

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use normalcomputing/extended-mind-mpt-30b 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"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "normalcomputing/extended-mind-mpt-30b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

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

SGLang

How to use normalcomputing/extended-mind-mpt-30b 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" \
    --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": "normalcomputing/extended-mind-mpt-30b",
		"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 "normalcomputing/extended-mind-mpt-30b" \
        --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": "normalcomputing/extended-mind-mpt-30b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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

phoebeklett commited on Jun 5, 2024

Commit

7a2fcfd

verified ·

1 Parent(s): da61f3d

Update README.md

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README.md CHANGED Viewed

@@ -27,10 +27,10 @@ from transformers import AutoModelForCausalLM, AutoTokenizer
 ag_wiki_entry = """Alexander Grothendieck (/ˈɡroʊtəndiːk/; German pronunciation: [ˌalɛˈksandɐ ˈɡʁoːtn̩ˌdiːk] (listen); French: [ɡʁɔtɛndik]; 28 March 1928 – 13 November 2014) was a stateless (and then, since 1971, French) mathematician who became the leading figure in the creation of modern algebraic geometry.[7][8] His research extended the scope of the field and added elements of commutative algebra, homological algebra, sheaf theory, and category theory to its foundations, while his so-called "relative" perspective led to revolutionary advances in many areas of pure mathematics.[7][9] He is considered by many to be the greatest mathematician of the twentieth century.[10][11]"""
-tokenizer_hf = AutoTokenizer.from_pretrained("normalcomputing/extended-mind-llama-2-7b")
 memories = tokenizer_hf(ag_wiki_entry).input_ids
-model_hf = AutoModelForCausalLM.from_pretrained("normalcomputing/extended-mind-llama-2-7b", external_memories=memories, trust_remote_code=True)
 ```
 After this, you can generate text with the model as usual. The model will automatically use the memories during generation. You can update any config parameters (we set `topk` below) by passing new values to the `model.generate()` method.

 ag_wiki_entry = """Alexander Grothendieck (/ˈɡroʊtəndiːk/; German pronunciation: [ˌalɛˈksandɐ ˈɡʁoːtn̩ˌdiːk] (listen); French: [ɡʁɔtɛndik]; 28 March 1928 – 13 November 2014) was a stateless (and then, since 1971, French) mathematician who became the leading figure in the creation of modern algebraic geometry.[7][8] His research extended the scope of the field and added elements of commutative algebra, homological algebra, sheaf theory, and category theory to its foundations, while his so-called "relative" perspective led to revolutionary advances in many areas of pure mathematics.[7][9] He is considered by many to be the greatest mathematician of the twentieth century.[10][11]"""
+tokenizer_hf = AutoTokenizer.from_pretrained("normalcomputing/extended-mind-mpt-30b")
 memories = tokenizer_hf(ag_wiki_entry).input_ids
+model_hf = AutoModelForCausalLM.from_pretrained("normalcomputing/extended-mind-mpt-30b", external_memories=memories, trust_remote_code=True)
 ```
 After this, you can generate text with the model as usual. The model will automatically use the memories during generation. You can update any config parameters (we set `topk` below) by passing new values to the `model.generate()` method.