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

Libraries

How to use aloobun/CosmicNoodle-7B with Transformers:

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

pipe = pipeline("text-generation", model="aloobun/CosmicNoodle-7B", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("aloobun/CosmicNoodle-7B", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("aloobun/CosmicNoodle-7B", trust_remote_code=True)

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use aloobun/CosmicNoodle-7B with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/aloobun/CosmicNoodle-7B

SGLang

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

Docker Model Runner
How to use aloobun/CosmicNoodle-7B with Docker Model Runner:
```
docker model run hf.co/aloobun/CosmicNoodle-7B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

This is an experimental model.

The idea is :

Calculate the difference in weights between a donor model(meta-math/MetaMath-Mistral-7B) and the base model(mistralai/Mistral-7B-v0.1). This difference represents how much each parameter needs to be adjusted to go from the base state to the donor state.

vector = math_model.state_dict()[k] - base_model.state_dict()[k]

Vector retrieved from the result of step one, is added to third model(lex-hue/Delexa-7b). This should transfer math skills to our third model.

vector = new_math_model.state_dict()[k]
new_v = v + vector.to(v.device)
v.copy_(new_v)

Example:

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_name = "aloobun/CosmicNoodle-7B"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, device_map="auto")


prompt = "For the natural number A, the quotient of A divided by 9 is 6 and the remainder is 5. What is the value of A?\n"
input_ids = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt")
tokens = model.generate(input_ids.to(device=model.device), max_new_tokens=128, temperature=0.99, top_p=0.95, do_sample=True)
out = tokenizer.decode(tokens[0], skip_special_tokens=True)
print(out)

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Safetensors

Model size

7B params

Tensor type

F16

Model tree for aloobun/CosmicNoodle-7B

Quantizations

3 models

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Collection

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