deep-principle/Hydrogen-1.0-NVFP4

NVFP4 quantized version of Hydrogen-1.0

Tech blog · Usage · Versions · 中文说明

Overview

Hydrogen-1.0 is a scientific problem-solving and research assistance model developed by Deep Principle based on GLM-5.3. Through text interaction, it is intended to assist with scientific questions, explain derivations, and organize responses to open-ended research problems. Outputs should be assessed against the assumptions, constraints, and evidence relevant to each task.

Research in practice

The technical blog presents examples of scientific computing, reaction-network exploration, NMR structure analysis, and experimental planning. These demonstrations combine the model with external tools and specific research environments; outputs require scientific review.

Versions

Version	Repository	Hardware
BF16 (unquantized)	deep-principle/Hydrogen-1.0	Full unquantized weights; sufficient device/host memory and a compatible runtime required
NVFP4	deep-principle/Hydrogen-1.0-NVFP4	Compatible NVIDIA Blackwell runtime; team-tested on 8x B300
MXFP4	deep-principle/Hydrogen-1.0-MXFP4	Compatible AMD Instinct runtime; team-tested on MI350X with ATOM

Model details

Item	Description
Model name	Hydrogen-1.0
Publisher	Deep Principle
Base model	GLM-5.3
Repository	deep-principle/Hydrogen-1.0-NVFP4
Release artifact	NVFP4 quantized model weights
Context length	1M tokens
Maximum output length	128K tokens
Training overview	See the technical blog; full datasets and recipes are not released with the weights
Model license	MIT License
Contact	info@deepprinciple.com

This repository is dedicated to the NVFP4 quantized version of Hydrogen-1.0. Available context and output budgets depend on the inference framework, service configuration, and runtime resources. Use a runtime that supports the exported NVFP4 checkpoint format and follow the deployment requirements for these weight files.

Quantization and deployment

Quantized with NVIDIA ModelOpt. The repository's config.json specifies NVFP4 routed experts with calibrated activation scales and FP8 dense MLPs in layers 0–2; shared experts, attention, router, embeddings, lm_head and the MTP layer remain BF16. Its quantization configuration includes FP8 KV cache settings. Retain both hf_quant_config.json and config.json supplied with this checkpoint.

The team reports testing with SGLang on 8x NVIDIA B300. The following example assumes a SGLang installation compatible with this export format and explicitly selects FP8 E4M3 KV cache; ensure checkpoint cache scales and backend compatibility:

python -m sglang.launch_server \
  --model-path deep-principle/Hydrogen-1.0-NVFP4 \
  --served-model-name hydrogen-1.0 \
  --tp 8 --trust-remote-code \
  --quantization modelopt_fp4 --kv-cache-dtype fp8_e4m3 \
  --reasoning-parser glm45 --tool-call-parser glm47 \
  --context-length 262144 --mem-fraction-static 0.85 \
  --chunked-prefill-size 16384 \
  --host 0.0.0.0 --port 8001

This command caps the deployed context at 262144 tokens, rather than the model's stated 1M. Input and generation must fit the deployed context budget. Increasing the limit requires compatible runtime support and sufficient memory. The API model ID is hydrogen-1.0, matching the client below.

Keep the checkpoint's tokenizer, chat template, generation_config.json, model and quantization configurations, and weight index together so the runtime uses the matching end-of-turn and stopping settings.

Usage example

The following example calls an already running compatible inference service. The launch command above sets the API model ID to hydrogen-1.0; confirm it at /v1/models and set the base URL for your deployment. This example does not download or load weights, and does not imply that a hosted endpoint is available on either model hub.

python -m pip install openai
export MODEL_BASE_URL="http://localhost:8001/v1"
export SERVED_MODEL_NAME="hydrogen-1.0"
# Set MODEL_API_KEY separately if the service requires authentication.

import os
from openai import OpenAI

client = OpenAI(
    base_url=os.environ["MODEL_BASE_URL"],
    api_key=os.environ.get("MODEL_API_KEY", "not-required"),
)

stream = client.chat.completions.create(
    model=os.environ["SERVED_MODEL_NAME"],
    messages=[
        {"role": "system", "content": "You are an AI scientist. State assumptions and explain the scientific reasoning behind your answer."},
        {"role": "user", "content": "Derive the period of a simple pendulum under the small-angle approximation, and explain when the approximation breaks down."},
    ],
    max_tokens=4096,
    stream=True,
    extra_body={"chat_template_kwargs": {"reasoning_effort": "max"}},
)

finish_reason = None
has_answer = False
for chunk in stream:
    if not chunk.choices:
        continue
    choice = chunk.choices[0]
    if choice.finish_reason is not None:
        finish_reason = choice.finish_reason
    text = choice.delta.content or ""
    if text:
        has_answer = has_answer or bool(text.strip())
        print(text, end="", flush=True)
print()
if finish_reason == "length":
    raise RuntimeError('The response was truncated. Increase max_tokens within the service limits.')
if not has_answer:
    raise RuntimeError('No final answer was returned. Check the request status and output budget.')

The example uses an illustrative per-request budget of max_tokens=4096, rather than the model's maximum output length. Adjust it for complex reasoning tasks within the service limits. The code displays the final-answer content from the stream. See usage notes for additional request conventions.

Intended uses

• Research and educational exploration of scientific questions.
• Assistance with explaining equations, derivations, assumptions, and possible solution approaches.
• Drafting structured responses to open-ended scientific problems for expert review.
• Studying model behavior and developing downstream research tools within the applicable license terms.

Model outputs are supporting material for users to review. They should not be treated as verified experimental findings or as sufficient grounds for decisions affecting health, safety, or other consequential outcomes.

Limitations and biases

The model may produce incorrect facts, calculations, equations, references, or scientific explanations. A detailed or confident response does not establish correctness. Users should verify key claims with primary literature, independent calculations, or experiments as appropriate.

Performance may vary across disciplines, languages, task formats, and input lengths. Training data may introduce selection, cultural, or linguistic biases. Outputs should not be treated as a complete or unbiased representation of scientific consensus.

Long-context capacity does not guarantee reliable retrieval, understanding, or use of every input detail. NVFP4 quantization may affect outputs. Validate the quantized model for the intended task; this statement does not imply a measured accuracy change for this release.

Training data and procedure

The technical blog describes the public training overview, including supervised fine-tuning (SFT), reinforcement learning (RL), and data-source categories such as domain knowledge, research scenarios, laboratory records, and discovery trajectories. Full training datasets and complete training recipes are not released with the model weights in this repository.

Evaluation

Evaluation focuses on scientific multiple-choice questions and open-ended scientific research problems. Validate the model for the intended task, inputs, and inference configuration. Model answers do not replace independent verification of scientific conclusions.

License and attribution

Hydrogen-1.0 is developed by Deep Principle based on GLM-5.3 by Z.AI. The Hydrogen-1.0 NVFP4 quantized model weights in this repository are provided under the MIT License. Preserve the included copyright and permission notices when using or redistributing the weights.

Independently authored Deep Principle code and documentation are licensed under Apache License 2.0. Third-party files retain their respective licenses. See licensing scope and attribution notices for details.

Contact

Deep Principle: info@deepprinciple.com