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

Libraries

How to use sharadsin/PSCManual_CPT_Model with Transformers:

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

pipe = pipeline("text-generation", model="sharadsin/PSCManual_CPT_Model")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("sharadsin/PSCManual_CPT_Model")
model = AutoModelForCausalLM.from_pretrained("sharadsin/PSCManual_CPT_Model")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use sharadsin/PSCManual_CPT_Model with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sharadsin/PSCManual_CPT_Model"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sharadsin/PSCManual_CPT_Model",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/sharadsin/PSCManual_CPT_Model

SGLang

How to use sharadsin/PSCManual_CPT_Model 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 "sharadsin/PSCManual_CPT_Model" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sharadsin/PSCManual_CPT_Model",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "sharadsin/PSCManual_CPT_Model" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sharadsin/PSCManual_CPT_Model",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

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PSCManual Pre Trained Model

This model is a CPT version of TinyLlama/TinyLlama-1.1B-Chat-v1.0 on the NHSN 2025 Patient Safety Component Manual.

Intended uses & limitations

This is a Continued Pre-Training (CPT) model designed to function primarily as an autocomplete system. It was developed as an experimental exercise to evaluate knowledge injection into a language model, with continued pre-training on the NHSN 2025 Patient Safety Component Manual. This model is not intended for production use. Its outputs may be suboptimal because it was not trained with enough data to meet Chinchilla scaling laws, which recommend approximately 20 tokens per parameter for optimal performance.

Training procedure

CPT (Continued Pre Training) for knowledge injection.

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0002
train_batch_size: 2
eval_batch_size: 2
seed: 42
distributed_type: multi-GPU
num_devices: 4
gradient_accumulation_steps: 4
total_train_batch_size: 32
total_eval_batch_size: 8
optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 10
training_steps: 16

Framework versions

Transformers 4.50.0
Pytorch 2.5.0+cu121
Datasets 3.4.1
Tokenizers 0.21.1

Downloads last month: -

Safetensors

Model size

1B params

Tensor type

BF16

Model tree for sharadsin/PSCManual_CPT_Model

Base model

TinyLlama/TinyLlama-1.1B-Chat-v1.0

Finetuned

(555)

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