Instructions to use asrith05/slm_finetuned_T1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use asrith05/slm_finetuned_T1 with Transformers:

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

pipe = pipeline("text-generation", model="asrith05/slm_finetuned_T1")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("asrith05/slm_finetuned_T1")
model = AutoModelForCausalLM.from_pretrained("asrith05/slm_finetuned_T1")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use asrith05/slm_finetuned_T1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "asrith05/slm_finetuned_T1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "asrith05/slm_finetuned_T1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/asrith05/slm_finetuned_T1

SGLang

How to use asrith05/slm_finetuned_T1 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 "asrith05/slm_finetuned_T1" \
    --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": "asrith05/slm_finetuned_T1",
		"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 "asrith05/slm_finetuned_T1" \
        --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": "asrith05/slm_finetuned_T1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use asrith05/slm_finetuned_T1 with Docker Model Runner:
```
docker model run hf.co/asrith05/slm_finetuned_T1
```

asrith05/slm_finetuned_T1

This is a fine-tuned multilingual model specialized for entity extraction tasks, based on DeepSeek architecture.

Model Details

Base Model: DeepSeek multilingual pretrained model
Languages: English, Telugu, Sanskrit
Task: Entity extraction and named entity recognition
Fine-tuning: Specialized for entity extraction from structured data
Size: ~1251MB

Description

This model has been fine-tuned specifically for entity extraction tasks across multiple languages. It can identify and extract various types of entities from text, including:

Person names
Locations
Organizations
Products
Events
Dates and times
Custom domain-specific entities

Usage

from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "asrith05/slm_finetuned_T1"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Example entity extraction
prompt = "Extract entities from: John Smith works at Microsoft in Seattle and attended the AI Conference."
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(
    **inputs, 
    max_new_tokens=100,
    temperature=0.3,
    do_sample=True
)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

Training Details

Base Model: Multilingual DeepSeek pretrained model
Fine-tuning Dataset: 20K entity extraction examples
Validation Set: 5K examples
Test Set: 5K examples
Training: 1 epoch with validation monitoring
Learning Rate: 5e-5
Batch Size: 16 (effective)

Performance

The model demonstrates strong performance on:

Multilingual entity recognition: Works across English, Telugu, and Sanskrit
Cross-domain extraction: Handles various domains and entity types
Structured data processing: Can process and extract from formatted data

Training Data Format

The model was trained on data in the format:

Input: "Extract entities from: [text]"
Output: "Entities: [extracted entities with labels]"

Limitations

Optimized for entity extraction tasks
May require prompt formatting for best results
Performance may vary across different domains
Generated content should be validated for accuracy

Related Models

Base Model: asrith05/deepseek_pretrain_90k
General Fine-tuned: asrith05/slm

License

This model is released under the Apache 2.0 License.

Citation

If you use this model, please cite:

@model{asrith05_slm_finetuned_T1,
  author = {Asrith},
  title = {Multilingual Entity Extraction Model},
  year = {2025},
  publisher = {Hugging Face},
  url = {https://huggingface.co/asrith05/slm_finetuned_T1}
}

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Safetensors

Model size

0.1B params

Tensor type

F32

Model tree for asrith05/slm_finetuned_T1

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asrith05/deepseek_pretrain_90k

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