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
```

phenixace commited on Sep 9

Commit

a6b1392

verified ·

1 Parent(s): 078f432

Align model-card metrics with paper task-averaged results

Browse files

Use unweighted means over the twelve task variants from Diagnosis_model_task for performance, mean ER and clean-trace rate. Preserve the already task-averaged per-claim fabrication rate. Document the earlier response-weighted summaries and independent rounding to the paper's display precision. No checkpoint or evaluation record changes.

Files changed (1) hide show

README.md +29 -7

README.md CHANGED Viewed

@@ -24,17 +24,39 @@ those two cases score differently.
 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 are the `R1_stage_ladder` sheet of the code repository's Source Data, so they can be
-checked against one shipped row:
 | | Chem-R | Chem-R-Faithful |
 |---|---|---|
 | per-claim fabrication rate | 22.56% | **4.35%** |
-| mean ER (fabrication score, 0–100) | 10.60 | **1.73** |
-| clean-trace rate (ER = 0) | 40.63% | **88.05%** |
-| task performance | 50.09 | **51.45** |
-Fabrication drops roughly five-fold and the clean-trace rate doubles, with task performance
 slightly up rather than traded away.
 ## Usage

 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