Instructions to use Navyaforaa/LitGram-1.5B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Navyaforaa/LitGram-1.5B with Transformers:

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

pipe = pipeline("text-generation", model="Navyaforaa/LitGram-1.5B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Navyaforaa/LitGram-1.5B")
model = AutoModelForCausalLM.from_pretrained("Navyaforaa/LitGram-1.5B", device_map="auto")
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 Settings

vLLM

How to use Navyaforaa/LitGram-1.5B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Navyaforaa/LitGram-1.5B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Navyaforaa/LitGram-1.5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Navyaforaa/LitGram-1.5B

SGLang

How to use Navyaforaa/LitGram-1.5B 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 "Navyaforaa/LitGram-1.5B" \
    --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": "Navyaforaa/LitGram-1.5B",
		"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 "Navyaforaa/LitGram-1.5B" \
        --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": "Navyaforaa/LitGram-1.5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use Navyaforaa/LitGram-1.5B with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Navyaforaa/LitGram-1.5B to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Navyaforaa/LitGram-1.5B to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Navyaforaa/LitGram-1.5B to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="Navyaforaa/LitGram-1.5B",
    max_seq_length=2048,
)

Docker Model Runner
How to use Navyaforaa/LitGram-1.5B with Docker Model Runner:
```
docker model run hf.co/Navyaforaa/LitGram-1.5B
```

Introducing LitGram-1.5B — English literature, poetics & grammar in one specialized model

by Navyaforaa - opened 1 day ago

Discussion

Navyaforaa

Owner 1 day ago

I’m excited to share LitGram-1.5B: a 1.54B-parameter, domain-adapted model built for serious work in English literature and grammar.

Instead of treating literary analysis and language mechanics as separate tasks, LitGram brings together:

text-grounded literary theory and criticism
line-by-line metrical scansion, poetic forms, and rhetoric
advanced English grammar and generative syntax
UGC NET English Literature and research-methodology support
grounding in canonical public-domain texts from Project Gutenberg

It is fine-tuned from Qwen2.5-1.5B-Instruct and released under Apache 2.0. The aim is to give students, researchers, teachers, and literary critics a compact model that can move from close reading to syntax and prosody with clear, defensible explanations.

Try it here: https://huggingface.co/Navyaforaa/LitGram-1.5B

I’d love feedback, especially on scansion, literary-theory analysis, and advanced grammar prompts. If you test it, please share examples of what works well and where it can improve.

Upload images, audio, and videos by dragging in the text input, pasting, or clicking here.

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