Instructions to use Kukedlc/NeuralMaxime-7B-slerp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Kukedlc/NeuralMaxime-7B-slerp with Transformers:

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

pipe = pipeline("text-generation", model="Kukedlc/NeuralMaxime-7B-slerp")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Kukedlc/NeuralMaxime-7B-slerp")
model = AutoModelForCausalLM.from_pretrained("Kukedlc/NeuralMaxime-7B-slerp")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Kukedlc/NeuralMaxime-7B-slerp with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Kukedlc/NeuralMaxime-7B-slerp"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Kukedlc/NeuralMaxime-7B-slerp",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Kukedlc/NeuralMaxime-7B-slerp

SGLang

How to use Kukedlc/NeuralMaxime-7B-slerp 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 "Kukedlc/NeuralMaxime-7B-slerp" \
    --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": "Kukedlc/NeuralMaxime-7B-slerp",
		"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 "Kukedlc/NeuralMaxime-7B-slerp" \
        --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": "Kukedlc/NeuralMaxime-7B-slerp",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Kukedlc/NeuralMaxime-7B-slerp with Docker Model Runner:
```
docker model run hf.co/Kukedlc/NeuralMaxime-7B-slerp
```

Love the name

by mlabonne - opened Feb 18, 2024

Discussion

mlabonne

Feb 18, 2024

😂

Kukedlc

Owner Feb 18, 2024

Haha, a merge of your models had to have some reference to the creator! Without a doubt, the "Monarch" models are the best there is on the leaderboard (that work without bugs). Whenever I have the chance, I'm going to thank you for all your contributions... Question: I'm training a merge of your models with the alpaca programming dataset. It's going to take about 18 hours to train, and it's looking pretty good. My question is based on some scientific papers I read about "emergent properties", like Chain of Thought (CoT), I read that when they started training the LLMs with code, that's somewhat when they became more "intelligent", and CoT emerged, which is the model's way of "reasoning in steps". My question is, do you think it's a good approach? What other programming datasets would you use? Alpaca has a 20k rows

mlabonne

Feb 19, 2024

I'd recommend Magicoder datasets that are a lot more evolved, especially for what you want to build. I think that CodeAlpaca is a little outdated at this point. Happy to see your results!

Kukedlc

Owner Feb 19, 2024

Thanks Maxime!

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