Instructions to use amrithanandini/deepseek-coder-arc-agi-finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use amrithanandini/deepseek-coder-arc-agi-finetuned with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("deepseek-ai/deepseek-coder-6.7b-base")
model = PeftModel.from_pretrained(base_model, "amrithanandini/deepseek-coder-arc-agi-finetuned")

Transformers

How to use amrithanandini/deepseek-coder-arc-agi-finetuned with Transformers:

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

pipe = pipeline("text-generation", model="amrithanandini/deepseek-coder-arc-agi-finetuned")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("amrithanandini/deepseek-coder-arc-agi-finetuned", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use amrithanandini/deepseek-coder-arc-agi-finetuned with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/amrithanandini/deepseek-coder-arc-agi-finetuned

SGLang

How to use amrithanandini/deepseek-coder-arc-agi-finetuned 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 "amrithanandini/deepseek-coder-arc-agi-finetuned" \
    --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": "amrithanandini/deepseek-coder-arc-agi-finetuned",
		"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 "amrithanandini/deepseek-coder-arc-agi-finetuned" \
        --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": "amrithanandini/deepseek-coder-arc-agi-finetuned",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use amrithanandini/deepseek-coder-arc-agi-finetuned with Docker Model Runner:
```
docker model run hf.co/amrithanandini/deepseek-coder-arc-agi-finetuned
```

deepseek-coder-arc-agi-finetuned

This model is a fine-tuned version of deepseek-ai/deepseek-coder-6.7b-base on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.2464

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: 0.0002
train_batch_size: 2
eval_batch_size: 4
seed: 42
gradient_accumulation_steps: 8
total_train_batch_size: 16
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 100
num_epochs: 4

Training results

Training Loss	Epoch	Step	Validation Loss
0.3729	0.4124	25	0.3884
0.2512	0.8247	50	0.2950
0.2074	1.2474	75	0.2809
0.2416	1.6598	100	0.2723
0.2966	2.0825	125	0.2663
0.1781	2.4948	150	0.2596
0.2821	2.9072	175	0.2534
0.1999	3.3299	200	0.2501
0.1572	3.7423	225	0.2464

Framework versions

PEFT 0.17.1
Transformers 4.51.3
Pytorch 2.7.0+cu126
Datasets 4.1.1
Tokenizers 0.21.1

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