Instructions to use singtan/my-llm-finetuned-pdf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use singtan/my-llm-finetuned-pdf with Transformers:

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

pipe = pipeline("text-generation", model="singtan/my-llm-finetuned-pdf")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("singtan/my-llm-finetuned-pdf")
model = AutoModelForCausalLM.from_pretrained("singtan/my-llm-finetuned-pdf")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use singtan/my-llm-finetuned-pdf with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/singtan/my-llm-finetuned-pdf

SGLang

How to use singtan/my-llm-finetuned-pdf 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 "singtan/my-llm-finetuned-pdf" \
    --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": "singtan/my-llm-finetuned-pdf",
		"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 "singtan/my-llm-finetuned-pdf" \
        --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": "singtan/my-llm-finetuned-pdf",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use singtan/my-llm-finetuned-pdf with Docker Model Runner:
```
docker model run hf.co/singtan/my-llm-finetuned-pdf
```

Fine-tuned Language Model (PDF Optimized)

This model is a specialized version of gpt2, fine-tuned for high-context coherence based on technical documentation extracted from PDF sources.

Model Summary

This model was developed to improve text generation accuracy and contextual understanding for specific domains covered in the provided PDF assets. It leverages the underlying architecture of gpt2 and is optimized for the structure and vocabulary found in document-based datasets.

Key Specifications

Attribute	Value
Base Architecture	gpt2
Format	PyTorch (Transformers)
Task	Causal Language Modeling
Language	English (en)

Training Configuration

The model was fine-tuned using the following high-level hyperparameters to ensure stability and convergence:

Epochs: 3
Batch Size: 1
Learning Rate: 5e-05
Optimized for: Cross-Entropy Loss
Hardware: cuda

Performance Metrics

Total Training Loss: 3.680997848510742
Training Runtime: 3.6704 seconds

Data Preprocessing

The training data was sourced from dummy.pdf. The pipeline included:

Extraction: Text recovery using pypdf.
Normalization: Regex-based whitespace cleaning and token normalization.
Tokenization: Model-specific subword tokenization with a max sequence length of 512 tokens.

Usage Instructions

To utilize this model for inference, use the following snippet:

from transformers import AutoTokenizer, AutoModelForCausalLM

checkpoint = "singtan/my-llm-finetuned-pdf"
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForCausalLM.from_pretrained(checkpoint)

prompt = "Insert your context here"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=150)
print(tokenizer.decode(outputs[0]))

Contact

Developed by Bibek - Senior AI Engineering Portfolio.

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Safetensors

Model size

0.1B params

Tensor type

F32

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openai-community/gpt2

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