Instructions to use anujjamwal/OpenMath-Nemotron-1.5B-PruneAware with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use anujjamwal/OpenMath-Nemotron-1.5B-PruneAware with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("anujjamwal/OpenMath-Nemotron-1.5B-PruneAware")
model = AutoModelForCausalLM.from_pretrained("anujjamwal/OpenMath-Nemotron-1.5B-PruneAware")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use anujjamwal/OpenMath-Nemotron-1.5B-PruneAware with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/anujjamwal/OpenMath-Nemotron-1.5B-PruneAware

SGLang

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

Docker Model Runner
How to use anujjamwal/OpenMath-Nemotron-1.5B-PruneAware with Docker Model Runner:
```
docker model run hf.co/anujjamwal/OpenMath-Nemotron-1.5B-PruneAware
```

anujjamwal commited on Mar 15

Commit

d8ac8d0

verified ·

1 Parent(s): 956fa6c

Update README.md

Browse files

Files changed (1) hide show

README.md +15 -6

README.md CHANGED Viewed

@@ -6,11 +6,20 @@ tags:
 - generated_from_trainer
 - sft
 - trl
 licence: license
 ---
 # Model Card for OpenMath-Nemotron-1.5B-PruneAware
 This model is a fine-tuned version of [anujjamwal/OpenMath-Nemotron-1.5B-PruneAware](https://huggingface.co/anujjamwal/OpenMath-Nemotron-1.5B-PruneAware).
 It has been trained using [TRL](https://github.com/huggingface/trl).
@@ -48,11 +57,11 @@ This model was trained with SFT.
 Cite TRL as:
 ```bibtex
-@software{vonwerra2020trl,
-  title   = {{TRL: Transformers Reinforcement Learning}},
-  author  = {von Werra, Leandro and Belkada, Younes and Tunstall, Lewis and Beeching, Edward and Thrush, Tristan and Lambert, Nathan and Huang, Shengyi and Rasul, Kashif and Gallouédec, Quentin},
-  license = {Apache-2.0},
-  url     = {https://github.com/huggingface/trl},
-  year    = {2020}
 }
 ```

 - generated_from_trainer
 - sft
 - trl
+- custom_generate
 licence: license
+datasets:
+- anujjamwal/OpenMathReasoning-Sampled-Hierarchical-Cot
 ---
 # Model Card for OpenMath-Nemotron-1.5B-PruneAware
+This model implements [Cognitive Compression](https://github.com/anujjamwal/cognitive-compression) an approach to produce hierarchical
+structured chain of thought that can be actively pruned at inference time while maintaining the solution quality.
+Tradition Chain-of-Thought is append-onl; a token once generated remains in context for ever. Context compression introduces hierarchical
+reasoning where reasoning is broken into subproblems. Once the subproblem is solved, its full chain of thought can be discarded and
+replaced with the **summary and solution** dramatically reducing the context window pressure.
 This model is a fine-tuned version of [anujjamwal/OpenMath-Nemotron-1.5B-PruneAware](https://huggingface.co/anujjamwal/OpenMath-Nemotron-1.5B-PruneAware).
 It has been trained using [TRL](https://github.com/huggingface/trl).
 Cite TRL as:
 ```bibtex
+@misc{jamwal2026cognitivecompression,
+  title   = {{Cognitive Compression: Hierarchical Chain of Thought for Efficient LLM Reasoning}},
+  author  = {Jamwal, Anuj},
+  url     = {huggingface.co/anujjamwal/OpenMath-Nemotron-1.5B-PruneAware},
+  year    = {2026},
+  note    = {CS224N Winter '26 Final Project: Stanford University}
 }
 ```