Instructions to use phenixace/Chem-R-Faithful with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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)# 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=40) 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
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.
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
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.
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meta-llama/Llama-3.1-8B