Instructions to use reaperdoesntknow/SMOLM2Prover with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use reaperdoesntknow/SMOLM2Prover with Transformers:

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

pipe = pipeline("text-generation", model="reaperdoesntknow/SMOLM2Prover")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("reaperdoesntknow/SMOLM2Prover")
model = AutoModelForCausalLM.from_pretrained("reaperdoesntknow/SMOLM2Prover")
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 reaperdoesntknow/SMOLM2Prover with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "reaperdoesntknow/SMOLM2Prover"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "reaperdoesntknow/SMOLM2Prover",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/reaperdoesntknow/SMOLM2Prover

SGLang

How to use reaperdoesntknow/SMOLM2Prover 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 "reaperdoesntknow/SMOLM2Prover" \
    --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": "reaperdoesntknow/SMOLM2Prover",
		"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 "reaperdoesntknow/SMOLM2Prover" \
        --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": "reaperdoesntknow/SMOLM2Prover",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use reaperdoesntknow/SMOLM2Prover with Docker Model Runner:
```
docker model run hf.co/reaperdoesntknow/SMOLM2Prover
```

reaperdoesntknow commited on Mar 31

Commit

8781f06

verified ·

1 Parent(s): 02f9663

Update model card: added convergentintel tag, added DISC section

Browse files

Files changed (1) hide show

README.md +15 -0

README.md CHANGED Viewed

@@ -14,6 +14,7 @@ tags:
 - generated_from_trainer
 - finetune
 - symbioticai
 language:
 - en
 license: apache-2.0
@@ -125,6 +126,20 @@ Limitations and Bias
  * Inherited Bias: This model inherits any biases present in the base model (SmolLM2-CoT-360M) and the training datasets.
 ### Acknowledgements
 You're doing great!
 ## Citations
 If you use TRL in your work, please cite the library:
 @misc{vonwerra2022trl,

 - generated_from_trainer
 - finetune
 - symbioticai
+- convergentintel
 language:
 - en
 license: apache-2.0
  * Inherited Bias: This model inherits any biases present in the base model (SmolLM2-CoT-360M) and the training datasets.
 ### Acknowledgements
 You're doing great!
+## Discrepancy Calculus Foundation
+This model is part of the [Convergent Intelligence LLC: Research Division](https://huggingface.co/reaperdoesntknow) portfolio. All models in this portfolio are developed under the Discrepancy Calculus (DISC) framework — a measure-theoretic approach to understanding and controlling the gap between what a model *should* produce and what it *actually* produces.
+DISC treats training singularities (loss plateaus, mode collapse, catastrophic forgetting) not as failures to be smoothed over, but as **structural signals** that reveal the geometry of the learning problem. Key concepts:
+- **Discrepancy Operator (D):** Measures the gap between expected and observed behavior at each training step
+- **Jump Sets:** Boundaries where model behavior changes discontinuously — these are *features*, not bugs
+- **Ghost Imprinting:** Teacher knowledge that transfers to student models through weight-space topology rather than explicit distillation signal
+For the full mathematical treatment, see [Discrepancy Calculus: Foundations and Core Theory](https://huggingface.co/reaperdoesntknow/Discrepancy_Calculus) (DOI: 10.57967/hf/8194).
+**Citation chain:** [Structure Over Scale](https://huggingface.co/reaperdoesntknow/Structure-Over-Scale) (DOI: 10.57967/hf/8165) → [Three Teachers to Dual Cognition](https://huggingface.co/reaperdoesntknow/DualMind_Methodolgy) (DOI: 10.57967/hf/8184) → [Discrepancy Calculus](https://huggingface.co/reaperdoesntknow/Discrepancy_Calculus) (DOI: 10.57967/hf/8194)
 ## Citations
 If you use TRL in your work, please cite the library:
 @misc{vonwerra2022trl,