Instructions to use dusersad12/HelixLM-CheckpointHub with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dusersad12/HelixLM-CheckpointHub with Transformers:

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

pipe = pipeline("text-generation", model="dusersad12/HelixLM-CheckpointHub")

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("dusersad12/HelixLM-CheckpointHub", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use dusersad12/HelixLM-CheckpointHub with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "dusersad12/HelixLM-CheckpointHub"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dusersad12/HelixLM-CheckpointHub",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/dusersad12/HelixLM-CheckpointHub

SGLang

How to use dusersad12/HelixLM-CheckpointHub 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 "dusersad12/HelixLM-CheckpointHub" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dusersad12/HelixLM-CheckpointHub",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "dusersad12/HelixLM-CheckpointHub" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dusersad12/HelixLM-CheckpointHub",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use dusersad12/HelixLM-CheckpointHub with Docker Model Runner:
```
docker model run hf.co/dusersad12/HelixLM-CheckpointHub
```

HelixLM

1. Introduction

The HelixLM model family, maintained by the Nimbus AI Lab, has just gone through a major version upgrade. In the latest update, HelixLM has significantly improved its depth of reasoning and inference capabilities by leveraging increased computational resources and introducing algorithmic optimization mechanisms during post-training. The model has demonstrated outstanding performance across various benchmark evaluations, including mathematics, programming, and general logic. Its overall performance is now approaching that of other leading models.

Compared to the previous version, the upgraded model shows significant improvements in handling complex reasoning tasks. For instance, in the GPQA-Diamond 2025 evaluation, the model's accuracy has increased from 58% in the previous version to 76.5% in the current version. This advancement stems from longer reasoning chains during the evaluation process: in the GPQA test set, the previous model used an average of 9K tokens per question, whereas the new version averages 21K tokens per question.

Beyond its improved reasoning capabilities, this version also offers a reduced hallucination rate and enhanced support for function calling.

2. Evaluation Results

Comprehensive Benchmark Results

	Benchmark	Model1	Model2	Model1-v2	HelixLM
Core Reasoning Tasks	Math Reasoning	0.510	0.535	0.521	0.506
	Logical Reasoning	0.789	0.801	0.810	0.731
	Common Sense	0.716	0.702	0.725	0.705
Language Understanding	Reading Comprehension	0.671	0.685	0.690	0.663
	Question Answering	0.582	0.599	0.601	0.584
	Text Classification	0.803	0.811	0.820	0.795
	Sentiment Analysis	0.777	0.781	0.790	0.772
Generation Tasks	Code Generation	0.615	0.631	0.640	0.600
	Creative Writing	0.588	0.579	0.601	0.557
	Dialogue Generation	0.621	0.635	0.639	0.611
	Summarization	0.745	0.755	0.760	0.739
Specialized Capabilities	Translation	0.782	0.799	0.801	0.788
	Knowledge Retrieval	0.651	0.668	0.670	0.653
	Instruction Following	0.733	0.749	0.751	0.730
	Safety Evaluation	0.718	0.701	0.725	0.717

Overall Performance Summary

The HelixLM demonstrates strong performance across all evaluated benchmark categories, with particularly notable results in reasoning and generation tasks.

3. Chat Website & API Platform

We offer a chat interface and API for you to interact with HelixLM. Please check our official website for more details.

4. How to Run Locally

Please refer to our code repository for more information about running HelixLM locally.

Compared to previous versions, the usage recommendations for HelixLM have the following changes:

System prompt is supported.
It is not required to add special tokens at the beginning of the output to force the model into a specific thinking pattern.

The model architecture of HelixLM-Small is identical to its base model, but it shares the same tokenizer configuration as the main HelixLM. This model can be run in the same manner as its base model.

System Prompt

We recommend using the following system prompt with a specific date.

You are HelixLM, a helpful AI assistant.
Today is {current date}.

For example,

You are HelixLM, a helpful AI assistant.
Today is Mar 09, 2026, Monday.

Temperature

We recommend setting the temperature parameter $T_{{model}}$ to 0.6.

Prompts for File Uploading and Web Search

For file uploading, please follow the template below to create prompts, where {{file_name}}, {{file_content}} and {{question}} are arguments.

file_template = \
"""[file name]: {{file_name}}
[file content begin]
{{file_content}}
[file content end]
{{question}}"""

For web search enhanced generation, we recommend the following prompt template where {{search_results}}, {{cur_date}}, and {{question}} are arguments.

search_answer_en_template = \
'''# The following contents are the search results related to the user's message:
{{search_results}}
In the search results I provide to you, each result is formatted as [webpage X begin]...[webpage X end], where X represents the numerical index of each article. Please cite the context at the end of the relevant sentence when appropriate. Use the citation format [citation:X] in the corresponding part of your answer. If a sentence is derived from multiple contexts, list all relevant citation numbers, such as [citation:3][citation:5]. Be sure not to cluster all citations at the end; instead, include them in the corresponding parts of the answer.
When responding, please keep the following points in mind:
- Today is {{cur_date}}.
- Not all content in the search results is closely related to the user's question. You need to evaluate and filter the search results based on the question.
- For listing-type questions (e.g., listing all flight information), try to limit the answer to 10 key points and inform the user that they can refer to the search sources for complete information. Prioritize providing the most complete and relevant items in the list. Avoid mentioning content not provided in the search results unless necessary.
- For creative tasks (e.g., writing an essay), ensure that references are cited within the body of the text, such as [citation:3][citation:5], rather than only at the end of the text. You need to interpret and summarize the user's requirements, choose an appropriate format, fully utilize the search results, extract key information, and generate an answer that is insightful, creative, and professional. Extend the length of your response as much as possible, addressing each point in detail and from multiple perspectives, ensuring the content is rich and thorough.
- If the response is lengthy, structure it well and summarize it in paragraphs. If a point-by-point format is needed, try to limit the answer to 5 points and merge related items.
- For objective Q&A, if the answer is very brief, you may add one or two related sentences to enrich the content.
- Choose an appropriate and visually appealing format for your response based on the user's requirements and the content of the answer, ensuring strong readability.
- Your answer should synthesize information from multiple relevant webpages and avoid repeatedly citing the same webpage.
- Unless the user requests otherwise, your response should be in the same language as the user's question.
# The user's message is:
{{question}}'''

5. License

This code repository is licensed under the MIT License. The use of HelixLM models is also subject to the MIT License. The model series supports commercial use and distillation.

6. Contact

If you have any questions, please raise an issue on our GitHub repository or contact us at contact@helixlm.ai.

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