Instructions to use siyah1/malayalam-moe-pretrain with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use siyah1/malayalam-moe-pretrain with Transformers:

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

pipe = pipeline("text-generation", model="siyah1/malayalam-moe-pretrain")

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("siyah1/malayalam-moe-pretrain", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use siyah1/malayalam-moe-pretrain with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "siyah1/malayalam-moe-pretrain"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "siyah1/malayalam-moe-pretrain",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/siyah1/malayalam-moe-pretrain

SGLang

How to use siyah1/malayalam-moe-pretrain 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 "siyah1/malayalam-moe-pretrain" \
    --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": "siyah1/malayalam-moe-pretrain",
		"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 "siyah1/malayalam-moe-pretrain" \
        --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": "siyah1/malayalam-moe-pretrain",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use siyah1/malayalam-moe-pretrain with Docker Model Runner:
```
docker model run hf.co/siyah1/malayalam-moe-pretrain
```

malayalam-moe-pretrain

This model is a fine-tuned version of siyah1/malayalam-moe-pretrain on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 1.1966

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: 4
eval_batch_size: 4
seed: 42
gradient_accumulation_steps: 64
total_train_batch_size: 256
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: linear
lr_scheduler_warmup_steps: 0.02
num_epochs: 12
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
95.2033	1.2500	500	1.4825
81.1107	2.5000	1000	1.3183
79.8998	3.0	1200	1.3110
78.8520	3.7500	1500	1.2993
72.8643	5.0	2000	1.2758
68.8933	6.0	2400	1.2886
70.5932	6.2500	2500	1.3040
65.6363	7.5000	3000	1.3147
60.5359	8.7500	3500	1.3476
60.8426	9.0	3600	1.3474
79.4108	1.3464	4000	1.2411
77.1449	1.5147	4500	1.1966

Framework versions

Transformers 5.16.1
Pytorch 2.10.0+cu128
Datasets 5.0.1
Tokenizers 0.23.1

Downloads last month: 1,119

Safetensors

Model size

0.4B params

Tensor type

F32

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