Instructions to use dennisonb/reversible-circuit-coder-1.5b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dennisonb/reversible-circuit-coder-1.5b with Transformers:

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

pipe = pipeline("text-generation", model="dennisonb/reversible-circuit-coder-1.5b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("dennisonb/reversible-circuit-coder-1.5b")
model = AutoModelForCausalLM.from_pretrained("dennisonb/reversible-circuit-coder-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 dennisonb/reversible-circuit-coder-1.5b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "dennisonb/reversible-circuit-coder-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": "dennisonb/reversible-circuit-coder-1.5b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/dennisonb/reversible-circuit-coder-1.5b

SGLang

How to use dennisonb/reversible-circuit-coder-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 "dennisonb/reversible-circuit-coder-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": "dennisonb/reversible-circuit-coder-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 "dennisonb/reversible-circuit-coder-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": "dennisonb/reversible-circuit-coder-1.5b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use dennisonb/reversible-circuit-coder-1.5b with Docker Model Runner:
```
docker model run hf.co/dennisonb/reversible-circuit-coder-1.5b
```

reversible-circuit-coder-1.5b / README.md

dennisonb

Polished model card + description

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	---
	license: apache-2.0
	base_model: Qwen/Qwen2.5-Coder-1.5B-Instruct
	library_name: transformers
	pipeline_tag: text-generation
	language:
	- en
	tags:
	- reversible-circuit
	- quantum-circuit-synthesis
	- code
	- reasoning
	- ecdsa-fail
	- verifier-guided
	---

	# reversible-circuit-coder-1.5b

	**A 1.5B model fine-tuned to synthesize cheap, correct reversible quantum circuits — and an honest case
	study in where small-model imitation/RL/reasoning hits a wall on algorithmic tasks.**

	This model designs reversible (quantum) circuits for the [ECDSA.fail](https://ecdsa.fail) secp256k1
	point-addition challenge and the broader task of **verifier-guided, cost-minimizing reversible-circuit
	optimization**: given a target reversible function, emit a circuit that is correct on every input,
	reversible, phase-clean, and ancilla-clean, at the lowest cost (Toffoli count × peak qubit width).

	- Developed by: Dennison Bertram (built autonomously with Claude Code)
	- Base model: `Qwen/Qwen2.5-Coder-1.5B-Instruct` (Apache-2.0)
	- License: Apache-2.0
	- Repository (full pipeline, verifier, data factory, eval, honest writeup):
	[github.com/dennisonbertram/reversible-circuit-llm](https://github.com/dennisonbertram/reversible-circuit-llm)

	## Model description
	The training signal comes from a microsecond-exact verifier (bit-identical to the challenge's Rust
	simulator). Rather than fine-tuning on textbook examples, a verifier-gated search engine produces
	near-optimal circuits (~0.54× the Toffoli cost of textbook references), and the model is SFT'd
	(LoRA) on 24,545 such optimal targets across a 7-family curriculum. The model emits an op-stream in
	the harness DSL: `X qT`, `CX qC qT`, `CCX qC1 qC2 qT` (Toffoli — the cost lever), `SWAP qA qB`.

	## Intended uses & limitations
	Intended: a proof-of-concept / research artifact for verifier-grounded circuit synthesis; a
	generator of small reversible arithmetic/boolean circuits (use best-of-N with the open-source verifier
	as an inference oracle); a teaching example for neuro-symbolic / tool-use research.

	Not intended: a production solver. It reliably solves only the easiest tasks.

	## Evaluation (honest)
	Held-out reversible-circuit synthesis, `valid_rate` = fraction solved with best-of-16:

	\| model \| held-out valid_rate \|
	\|---\|---\|
	\| base Qwen2.5-Coder-1.5B \| 0% (emits Python, not circuits) \|
	\| this model (optimal-target SFT) \| 4.8% (solves the easiest band) \|

	Key research finding: a 7B trained identically, plus reinforcement learning (GRPO) and
	reasoning chain-of-thought, all plateau at the same ~4%. The bottleneck is not data, capacity,
	RL, or reasoning — it is the small model's inability to reliably execute multi-step symbolic
	procedures (Gaussian elimination, ripple-carry) for unseen instances. It can narrate the algorithm
	but makes execution errors. Even a state-externalizing tool (single gate at a time) didn't break this
	zero-shot — the remaining gap is sequential planning. The honest next directions are tool-use with
	training, frontier-scale reasoning models, and neuro-symbolic methods.

	## How to use
	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer
	tok = AutoTokenizer.from_pretrained("dennisonb/reversible-circuit-coder-1.5b")
	model = AutoModelForCausalLM.from_pretrained("dennisonb/reversible-circuit-coder-1.5b")
	```
	Use the system prompt + task format from the repo (`proxy/system_prompt.txt`, `proxy/sample_task.txt`),
	sample best-of-N, and verify each candidate with the open-source proxy verifier (`proxy/proxy_env.py`).

	## Training data
	24,545 near-optimal circuit targets generated by the verifier-gated search engine over a procedurally
	generated curriculum (modular adders/multipliers/inverse, controlled add/sub, GF(2) linear maps,
	S-boxes; widths 2–7). Move/reasoning corpora mined from 275 accepted ECDSA.fail submissions are also in
	the repo. Datasets are regenerable via the repo's scripts.

	🤖 Built autonomously with Claude Code.