Instructions to use simonts/genre2-grm-sft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use simonts/genre2-grm-sft with Transformers:

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

pipe = pipeline("text-generation", model="simonts/genre2-grm-sft")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("simonts/genre2-grm-sft")
model = AutoModelForCausalLM.from_pretrained("simonts/genre2-grm-sft")
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 simonts/genre2-grm-sft with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "simonts/genre2-grm-sft"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "simonts/genre2-grm-sft",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/simonts/genre2-grm-sft

SGLang

How to use simonts/genre2-grm-sft 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 "simonts/genre2-grm-sft" \
    --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": "simonts/genre2-grm-sft",
		"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 "simonts/genre2-grm-sft" \
        --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": "simonts/genre2-grm-sft",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use simonts/genre2-grm-sft with Docker Model Runner:
```
docker model run hf.co/simonts/genre2-grm-sft
```

GenRe2 GRM SFT Checkpoint

This repository contains the supervised fine-tuned generative reward model used as a local GRM checkpoint candidate for the GenRe2 experiments.

Source

Local source path used for this release:

/home/trx/rlm-code/ICLR26/output/mistralai/Mistral-7B-Instruct-v0.2-sft_newdataset_fuxian_final

Expected uploaded files:

config.json
generation_config.json
model-00001-of-00003.safetensors
model-00002-of-00003.safetensors
model-00003-of-00003.safetensors
model.safetensors.index.json
tokenizer.json
tokenizer.model
tokenizer_config.json
special_tokens_map.json
chat_template.jinja

Result Status

The closest local result row found during release preparation is:

Local result	FB Bench	FLASK	MT Bench	Vicuna	Avg	Paper row
`Mistral-7B-Instruct-v0.2-sft_epoch2`	0.7822	0.3726	0.2908	0.3429	0.4471	CE avg 0.4494

This is close to the paper CE average, but it is not a complete five-seed reproduction artifact.

Notes

Released under Apache-2.0. The base model metadata for mistralai/Mistral-7B-Instruct-v0.2 also lists Apache-2.0.
This checkpoint is large, about 14 GB locally.
The code release and checkpoint audit notes are in /home/trx/rlm-code/genre2_main_experiments.

Downloads last month: -

Safetensors

Model size

7B params

Tensor type

BF16

Model tree for simonts/genre2-grm-sft

Base model

mistralai/Mistral-7B-Instruct-v0.2

Finetuned

(1096)

this model

Adapters

1 model