Instructions to use DedeProGames/mini-chennus-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DedeProGames/mini-chennus-2 with Transformers:

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

pipe = pipeline("text-generation", model="DedeProGames/mini-chennus-2")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("DedeProGames/mini-chennus-2")
model = AutoModelForCausalLM.from_pretrained("DedeProGames/mini-chennus-2")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use DedeProGames/mini-chennus-2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DedeProGames/mini-chennus-2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DedeProGames/mini-chennus-2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/DedeProGames/mini-chennus-2

SGLang

How to use DedeProGames/mini-chennus-2 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 "DedeProGames/mini-chennus-2" \
    --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": "DedeProGames/mini-chennus-2",
		"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 "DedeProGames/mini-chennus-2" \
        --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": "DedeProGames/mini-chennus-2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use DedeProGames/mini-chennus-2 with Docker Model Runner:
```
docker model run hf.co/DedeProGames/mini-chennus-2
```

mini-chennus-2

This model is a fine-tuned version of EleutherAI/pythia-14m-deduped on an unknown dataset. It achieves the following results on the evaluation set:

Loss: nan
Accuracy: 0.0

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
num_epochs: 6

Training results

Epoch	Step	Validation Loss
0.1616	200	nan
0.3231	400	nan
0.4847	600	nan
0.6462	800	nan
0.8078	1000	nan
0.9693	1200	nan
1.1309	1400	nan
1.2924	1600	nan
1.4540	1800	nan
1.6155	2000	nan
1.7771	2200	nan
1.9386	2400	nan
2.1002	2600	nan
2.2617	2800	nan
2.4233	3000	nan
2.5848	3200	nan
2.7464	3400	nan
2.9079	3600	nan
3.0695	3800	nan
3.2310	4000	nan
3.3926	4200	nan
3.5541	4400	nan
3.7157	4600	nan
3.8772	4800	nan
4.0388	5000	nan
4.2003	5200	nan
4.3619	5400	nan
4.5234	5600	nan
4.6850	5800	nan
4.8465	6000	nan
5.0081	6200	nan
5.1696	6400	nan
5.3312	6600	nan
5.4927	6800	nan
5.6543	7000	nan
5.8158	7200	nan
5.9774	7400	nan

Framework versions

Transformers 5.0.0
Pytorch 2.10.0+cu128
Datasets 4.0.0
Tokenizers 0.22.2

Downloads last month: 10

Safetensors

Model size

14.1M params

Tensor type

F16

Model tree for DedeProGames/mini-chennus-2

Base model

EleutherAI/pythia-14m-deduped

Finetuned

(28)

this model

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