Instructions to use phenixace/Chem-R-Faithful with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use phenixace/Chem-R-Faithful with Transformers:

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

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

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("phenixace/Chem-R-Faithful")
model = AutoModelForCausalLM.from_pretrained("phenixace/Chem-R-Faithful", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use phenixace/Chem-R-Faithful with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/phenixace/Chem-R-Faithful

SGLang

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

Docker Model Runner
How to use phenixace/Chem-R-Faithful with Docker Model Runner:
```
docker model run hf.co/phenixace/Chem-R-Faithful
```

Chem-R-Faithful / README.md

phenixace

Align model-card metrics with paper task-averaged results

a6b1392 verified 26 days ago

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	---
	license: cc-by-4.0
	base_model: weidawang/Chem-R-8B
	tags:
	- chemistry
	- chain-of-thought
	- reasoning-faithfulness
	- grpo
	library_name: transformers
	---

	# Chem-R-Faithful

	Chem-R-8B continued with GRPO under a verification-grounded process reward: the accuracy term
	is paid only when the reasoning trace is clean, i.e. when every functional-group claim it makes is
	supported by the input, the predicted molecule, or the reference.

	The point is not accuracy. An answer-level reward cannot distinguish a correct answer reached
	through a faithful trace from the same answer reached through a fabricating one, because the
	reward does not depend on the trace at all. Gating it on a structural check of the trace makes
	those two cases score differently.

	## What it changes

	Measured over twelve generative task variants (ChEBI-20 caption↔molecule, USPTO-50k
	retrosynthesis, nine S²-Bench subtasks), against the Chem-R checkpoint it was trained from.
	All four rows below are unweighted means over the twelve task variants (each variant has
	weight 1/12), calculated from the per-task values in sheet `Diagnosis_model_task` of the
	repository's [Source Data](https://github.com/phenixace/MolReHallu/blob/0c951962d69f79c92b759b52c28d0144f6cb3ab3/data/source_data.xlsx).
	This uses the same task-averaging convention as the paper's Fig. 1d and its mean ER across tasks;
	the task sizes do not determine their weights in this table.

	Specifically, task performance, mean ER and clean-trace rate average the `perf`, `ER` and
	`pct_er0` columns, respectively. Per-claim fabrication rate averages `100 - cp` over tasks,
	where `cp` is the percentage of verified claims among verified plus fabricated specific
	functional-group claims within each task; the six generic group names excluded by the
	evaluation code are excluded from both counts. These are means of per-task claim rates,
	not a single claim rate pooled across tasks.

	\| \| Chem-R \| Chem-R-Faithful \|
	\|---\|---\|---\|
	\| per-claim fabrication rate \| 22.56% \| 4.35% \|
	\| mean ER (fabrication score, 0–100) \| 10.63 \| 2.39 \|
	\| clean-trace rate (ER = 0) \| 45.45% \| 87.58% \|
	\| task performance (0–100) \| 58.91 \| 61.48 \|

	Values here are shown to two decimal places. Rounded directly from the underlying values to
	the paper's display precision, performance is 59 / 61 (Fig. 1d), and Chem-R-Faithful has mean
	ER 2.4 and per-claim fabrication rate 4.3%.

	Aggregation update: Earlier versions of this card took performance, mean ER and
	clean-trace rate from `R1_stage_ladder`, which weights these metrics by the number of responses
	(16,107 per model: 3,300 for each ChEBI-20 task, 5,007 for retrosynthesis and 500 for each of
	the nine S²-Bench subtasks). Those response-weighted summaries gave performance 50.09 / 51.45
	and Chem-R-Faithful mean ER 1.73. The table above now uses equal task weights to align with the
	paper; the model checkpoints, evaluation records and per-task results are unchanged.
	Per-claim fabrication rate was already task-averaged and is unchanged.

	Fabrication drops roughly five-fold and the clean-trace rate nearly doubles, with task performance
	slightly up rather than traded away.

	## Usage

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer
	m = AutoModelForCausalLM.from_pretrained("phenixace/Chem-R-Faithful", torch_dtype="bfloat16")
	t = AutoTokenizer.from_pretrained("phenixace/Chem-R-Faithful")
	```

	It answers in `<think>…</think><answer>…</answer>` form. The answer span is what should be parsed;
	the trace span is what the detector audits.

	## Training

	GRPO, 936 steps on 4×H200 (~26.6 h). Reward
	`0.1·format + 0.4·accuracy + 0.4·(1 − hallucination) + 0.2·grounded`, with the accuracy term gated
	on ER = 0. Config, launcher, the exact training parquets, and the EasyR1/verl patch the
	per-task reward dispatch requires are in the code repository.

	## Code, data, and the detector

	https://github.com/phenixace/MolReHallu — the structural claim detector, the evaluation pipeline,
	the released model responses and per-claim diagnosis records, and the training recipe. The
	detector and the reward gate can both be exercised on a CPU in under a minute.

	## Limitations

	The verifier decides functional groups, ring systems and molecular classes from the molecular
	graph. It does not certify a complete chemical argument, and ER is a fabrication rate over the
	explicit, structurally decidable claims it recovers rather than a recall-complete audit of the
	reasoning. Fabrication is reduced, not eliminated.

	## License

	CC BY 4.0. Derived from `weidawang/Chem-R-8B`, which remains subject to its own license.