---
library_name: transformers
tags:
- code
- reward-model
- multilingual
license: apache-2.0
datasets:
- project-themis/Themis-CodePreference
base_model:
- project-themis/Themis-RM-0.6B-PMP
pipeline_tag: text-classification
language:
- en
---
# Themis-RM-0.6B
[](https://arxiv.org/abs/2605.00754)
[](https://huggingface.co/collections/project-themis/themis-reward-model-collection)
[](https://huggingface.co/collections/project-themis/themis-preference-datasets-and-benchmarks)
[](https://github.com/iNeil77/Themis)
[](https://hub.docker.com/repository/docker/ineil77/themis/general)
## Overview
**Themis-RM-0.6B** is a 600M-parameter multilingual code reward model for flexible multi-criteria scoring. It is the smallest model in the [Themis-RM](https://huggingface.co/collections/project-themis/themis-reward-model-collection) suite, trained using the Bradley-Terry preference framework on [Themis-CodePreference](https://huggingface.co/collections/project-themis/themis-preference-datasets-and-benchmarks), the largest open-source collection of code preferences to date (more than 350k preference pairs).
Themis-RM models evaluate code across five quality dimensions — Functional Correctness, Runtime Efficiency, Memory Efficiency, Security Hardness, and Readability & Maintainability — and support eight programming languages. Our experiments demonstrate positive scaling trends, strong cross-lingual transfer when training on diverse preferences, and the importance of multi-criteria training for reliable code reward modelling.
## Model Family
The Themis-RM suite ranges from 600M to 32B parameters, all built on the Qwen3 backbone.
## Results
Themis-RM models achieve best-in-class accuracy on [Themis-CodeRewardBench](https://huggingface.co/datasets/project-themis/Themis-CodeRewardBench), a code-specific reward model benchmark, while also matching or exceeding much larger models on established general-domain benchmarks ([RewardBench V1](https://huggingface.co/datasets/allenai/reward-bench), [RewardBench V2](https://huggingface.co/datasets/allenai/reward-bench-v2), [JudgeBench](https://huggingface.co/datasets/ScalerLab/JudgeBench)). Models are grouped by parameter class; **bold** marks the best in each group.
| Model | [Themis-CodeRewardBench](https://huggingface.co/datasets/project-themis/Themis-CodeRewardBench) | [RewardBench V1](https://huggingface.co/datasets/allenai/reward-bench) | [RewardBench V2](https://huggingface.co/datasets/allenai/reward-bench-v2) | [JudgeBench](https://huggingface.co/datasets/ScalerLab/JudgeBench) |
|---|---|---|---|---|
| | | | | |
| **32B - 72B Class** | | | | |
| [WorldPM-72B](https://huggingface.co/Qwen/WorldPM-72B-RLHFLow) | 76.96 | 90.88 | 67.92 | 55.21 |
| [Athene-RM-70B](https://huggingface.co/Nexusflow/Athene-RM-70B) | 78.39 | 91.22 | 68.76 | 63.45 |
| [Nemotron-70B-Reward](https://huggingface.co/nvidia/Llama-3.3-Nemotron-70B-Reward) | 81.19 | 93.88 | 70.49 | **73.47** |
| [**Themis-RM-32B**](https://huggingface.co/project-themis/Themis-RM-32B) | **91.82** | **94.89** | **72.34** | 71.65 |
| [AceCodeRM-32B](https://huggingface.co/TIGER-Lab/AceCodeRM-32B) | 62.95 | 23.58 | 67.98 | 66.77 |
| | | | | |
| **7B - 14B Class** | | | | |
| [**Themis-RM-14B**](https://huggingface.co/project-themis/Themis-RM-14B) | **91.19** | 94.11 | 71.44 | **70.85** |
| [**Themis-RM-8B**](https://huggingface.co/project-themis/Themis-RM-8B) | 89.78 | 93.69 | 65.87 | 69.97 |
| [Athene-RM-8B](https://huggingface.co/Nexusflow/Athene-RM-8B) | 76.58 | 87.48 | 62.96 | 61.12 |
| [CodeScaler-8B](https://huggingface.co/LARK-Lab/CodeScaler-8B) | 79.12 | 94.66 | 76.51 | 70.05 |
| [Skywork-Reward-V2-8B](https://huggingface.co/Skywork/Skywork-Reward-V2-Qwen3-8B) | 79.97 | **94.76** | **76.93** | 67.90 |
| [AceCodeRM-7B](https://huggingface.co/TIGER-Lab/AceCodeRM-7B) | 71.11 | 22.74 | 63.16 | 61.09 |
| | | | | |
| **0.6B - 4B Class** | | | | |
| [**Themis-RM-4B**](https://huggingface.co/project-themis/Themis-RM-4B) | **88.39** | 92.46 | 63.81 | 68.02 |
| [CodeScaler-4B](https://huggingface.co/LARK-Lab/CodeScaler-4B) | 77.97 | **94.32** | **75.13** | **68.44** |
| [Skywork-Reward-V2-4B](https://huggingface.co/Skywork/Skywork-Reward-V2-Qwen3-4B) | 79.27 | 94.06 | 74.26 | 65.43 |
| [**Themis-RM-1.7B**](https://huggingface.co/project-themis/Themis-RM-1.7B) | 83.04 | 89.17 | 56.22 | 63.29 |
| [CodeScaler-1.7B](https://huggingface.co/LARK-Lab/CodeScaler-1.7B) | 73.75 | 91.13 | 68.44 | 66.17 |
| [Skywork-Reward-V2-1.7B](https://huggingface.co/Skywork/Skywork-Reward-V2-Qwen3-1.7B) | 75.60 | 91.64 | 67.71 | 66.48 |
| **Themis-RM-0.6B (this model)** | 79.26 | 83.41 | 49.61 | 63.84 |
| [Skywork-Reward-V2-0.6B](https://huggingface.co/Skywork/Skywork-Reward-V2-Qwen3-0.6B) | 72.77 | 86.32 | 60.83 | 63.65 |
## Usage
```python
import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model_name = "project-themis/Themis-RM-0.6B"
device = "cuda:0"
model = AutoModelForSequenceClassification.from_pretrained(
model_name,
torch_dtype=torch.bfloat16,
device_map=device,
attn_implementation="flash_attention_2",
num_labels=1,
)
tokenizer = AutoTokenizer.from_pretrained(model_name)
prompt = "Write a Python function that checks if a string is a palindrome."
response_chosen = """def is_palindrome(s: str) -> bool:
s = s.lower().strip()
return s == s[::-1]"""
response_rejected = """def is_palindrome(s: str) -> bool:
for i in range(len(s)):
if s[i] != s[len(s) - i]:
return False
return True"""
conv_chosen = [
{"role": "user", "content": prompt},
{"role": "assistant", "content": response_chosen},
]
conv_rejected = [
{"role": "user", "content": prompt},
{"role": "assistant", "content": response_rejected},
]
chosen_text = tokenizer.apply_chat_template(conv_chosen, tokenize=False)
rejected_text = tokenizer.apply_chat_template(conv_rejected, tokenize=False)
inputs_chosen = tokenizer(chosen_text, return_tensors="pt", truncation=True, max_length=4096).to(device)
inputs_rejected = tokenizer(rejected_text, return_tensors="pt", truncation=True, max_length=4096).to(device)
with torch.no_grad():
score_chosen = model(**inputs_chosen).logits[0][0].item()
score_rejected = model(**inputs_rejected).logits[0][0].item()
print(f"Chosen response score: {score_chosen}")
print(f"Rejected response score: {score_rejected}")
```
### Multi-Criteria Scoring with System Prompts
Themis-RM models are trained with stochastic criteria-conditioned system prompts, allowing you to steer scoring toward a specific quality dimension at inference time. Prepend a system message that specifies the evaluation criteria before the user/assistant turns. The model supports the following criteria:
| Criterion | Key |
|:---|:---|
| Functional Correctness | `Functional_Correctness` |
| Runtime Efficiency | `Runtime_Efficiency` |
| Memory Efficiency | `Memory_Efficiency` |
| Security Hardness | `Security_Hardness` |
| Readability & Maintainability | `Readability_Maintainability` |
| All criteria (multi-criteria) | `Full` |
Each criterion-specific system prompt includes a base preamble (Helpfulness + Harmlessness) followed by the targeted criterion. The `Full` prompt includes all five code criteria together. When no system prompt is provided, the model scores based on general quality — the model was trained to handle all three modes (no system prompt, single-criterion, and full multi-criteria). For the full set of system prompts used during training and evaluation, see the [Evaluation](https://github.com/iNeil77/Themis/tree/main/Evaluation) folder in the GitHub repository.
```python
import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model_name = "project-themis/Themis-RM-0.6B"
device = "cuda:0"
model = AutoModelForSequenceClassification.from_pretrained(
model_name,
torch_dtype=torch.bfloat16,
device_map=device,
attn_implementation="flash_attention_2",
num_labels=1,
)
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Criterion-specific system prompt (example: Runtime Efficiency)
# See https://github.com/iNeil77/Themis/tree/main/Evaluation for all prompts
system_prompt = (
"You are an experienced AI judge and evaluator, specifically "
"designed and trained to assess the overall quality of responses "
"to human prompts. The prompts can be of various types, including "
"but not limited to open-ended questions, coding tasks, creative "
"writing, requests for information, and more. The responses may be "
"generated by a language model or a human and may include text, "
"code, or both. You will receive the prompt and the generated "
"response for each judgement session. Your exact mandate is to "
"score the response based on a pre-specified but flexible set of "
"criteria.\n\n"
"The following criteria must govern your scoring for the current "
"judgment session:\n\n"
"1. **Helpfulness**: How well does the response address the "
"prompt? Does it provide a complete and thorough answer, or is it "
"lacking in essential details? This is a tenet that applies to "
"evaluating all types of responses. You must score helpful "
"responses higher than less unhelpful ones.\n\n"
"2. **Harmlessness**: Does the response contain any harmful or "
"inappropriate content? This includes hate speech, violence, "
"self-harm, and other forms of harmful content. This is a tenet "
"that applies to evaluating all types of responses. You must "
"score harmless responses higher than harmful ones.\n\n"
"3. **Runtime Efficiency**: Does the response follow best "
"practices for runtime efficiency? Examples include using "
"efficient algorithms and data structures, minimizing time "
"complexity, avoiding unnecessary computations, caching results, "
"and leveraging parallel processing or asynchronous programming "
"techniques where appropriate, among others. This is a tenet "
"that applies to evaluating code responses. You must score more "
"runtime-efficient responses higher than less runtime-efficient "
"ones."
)
prompt = "Write a Python function that returns the n-th Fibonacci number."
response = """def fibonacci(n: int) -> int:
if n <= 1:
return n
a, b = 0, 1
for _ in range(2, n + 1):
a, b = b, a + b
return b"""
conversation = [
{"role": "system", "content": system_prompt},
{"role": "user", "content": prompt},
{"role": "assistant", "content": response},
]
text = tokenizer.apply_chat_template(conversation, tokenize=False)
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=4096).to(device)
with torch.no_grad():
score = model(**inputs).logits[0][0].item()
print(f"Runtime Efficiency score: {score}")
```
## License
This model is released under the [Apache 2.0 License](https://www.apache.org/licenses/LICENSE-2.0). The base model, [Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B), is also licensed under Apache 2.0.
## Citation
```bibtex
@article{themis2025,
title={Themis: Training Robust Multilingual Code Reward Models for Flexible Multi-Criteria Scoring},
author={},
journal={arXiv preprint arXiv:2605.00754},
year={2025}
}
``` 
Qwen/Qwen3-0.6B | — |
| Themis-RM-1.7B |