Instructions to use cpayne1303/cp2024 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use cpayne1303/cp2024 with Transformers:

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

pipe = pipeline("text-generation", model="cpayne1303/cp2024")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("cpayne1303/cp2024")
model = AutoModelForCausalLM.from_pretrained("cpayne1303/cp2024")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use cpayne1303/cp2024 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "cpayne1303/cp2024"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "cpayne1303/cp2024",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/cpayne1303/cp2024

SGLang

How to use cpayne1303/cp2024 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 "cpayne1303/cp2024" \
    --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": "cpayne1303/cp2024",
		"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 "cpayne1303/cp2024" \
        --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": "cpayne1303/cp2024",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use cpayne1303/cp2024 with Docker Model Runner:
```
docker model run hf.co/cpayne1303/cp2024
```

Model Description

This is a model using the llama2 architecture and only 30 million parameters. It is trained on approximately 2 billion tokens of diverse web data from the first 1000000 rows of the uncleaned c4 english dataset.

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	3.61
IFEval (0-Shot)	16.58
BBH (3-Shot)	2.74
MATH Lvl 5 (4-Shot)	0.00
GPQA (0-shot)	0.78
MuSR (0-shot)	0.46
MMLU-PRO (5-shot)	1.12

Downloads last month: 8

Safetensors

Model size

31.2M params

Tensor type

BF16

Model tree for cpayne1303/cp2024

Finetunes

1 model

Dataset used to train cpayne1303/cp2024

Evaluation results

strict accuracy on IFEval (0-Shot)
Open LLM Leaderboard

16.580
normalized accuracy on BBH (3-Shot)
Open LLM Leaderboard

2.740
exact match on MATH Lvl 5 (4-Shot)
Open LLM Leaderboard

0.000
acc_norm on GPQA (0-shot)
Open LLM Leaderboard

0.780
acc_norm on MuSR (0-shot)
Open LLM Leaderboard

0.460
accuracy on MMLU-PRO (5-shot)
test set Open LLM Leaderboard

1.120