Instructions to use Cozzy4Life/CozzyAI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Cozzy4Life/CozzyAI with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("unsloth/qwen2.5-7b-instruct-bnb-4bit")
model = PeftModel.from_pretrained(base_model, "Cozzy4Life/CozzyAI")

Transformers

How to use Cozzy4Life/CozzyAI with Transformers:

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

pipe = pipeline("text-generation", model="Cozzy4Life/CozzyAI")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Cozzy4Life/CozzyAI", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Cozzy4Life/CozzyAI with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Cozzy4Life/CozzyAI"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Cozzy4Life/CozzyAI",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Cozzy4Life/CozzyAI

SGLang

How to use Cozzy4Life/CozzyAI 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 "Cozzy4Life/CozzyAI" \
    --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": "Cozzy4Life/CozzyAI",
		"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 "Cozzy4Life/CozzyAI" \
        --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": "Cozzy4Life/CozzyAI",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use Cozzy4Life/CozzyAI with Docker Model Runner:
```
docker model run hf.co/Cozzy4Life/CozzyAI
```

Model Card for Model ID

Model Details

Model Description

Developed by: CozzyDev
Funded by [optional]: CozzyDev
Shared by [optional]: CozzyDev
Model type: C9-1QE
Language(s) (NLP): NLP
License: MIT
Finetuned from model [optional]: KlammerAI

Model Sources [optional]

Repository: Cozzy4Life/CozzyAI
Paper [optional]: [More Information Needed]
Demo [optional]: [More Information Needed]

Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

Hardware Type: [More Information Needed]
Hours used: 4 Hours
Cloud Provider: Huggingface, Kaggle, Google Colab
Compute Region: [More Information Needed]
Carbon Emitted: [More Information Needed]

Model Card Contact

[More Information Needed]

Framework versions

PEFT 0.18.1

Downloads last month: 8

Model tree for Cozzy4Life/CozzyAI

Base model

Qwen/Qwen2.5-7B

Finetuned

Qwen/Qwen2.5-Coder-7B

Quantized

unsloth/Qwen2.5-Coder-7B-bnb-4bit

Adapter

(17)

this model

Paper for Cozzy4Life/CozzyAI

Quantifying the Carbon Emissions of Machine Learning

Paper • 1910.09700 • Published Oct 21, 2019 • 61