Hydrogen-1.0

MXFP4 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

Model details

This repository is dedicated to the MXFP4 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 MXFP4 checkpoint format and follow the deployment requirements for these weight files.

Quantization and deployment

Quantized with AMD Quark. Dense MLPs, shared and routed experts, and MTP MLPs use MXFP4 with dynamic activations; attention, router, embeddings, lm_head and eh_proj remain BF16. The repository's quantization configuration uses groups of 32 elements with E8M0 scales for weights and activations.

The team reports testing on AMD Instinct MI350X with ATOM at tensor parallel 4, FP8 KV cache, and speculative decoding off. The following example assumes a compatible ATOM installation and does not enable a speculative decoding method:

python -m atom.entrypoints.openai_server \
  --model deep-principle/Hydrogen-1.0-MXFP4 \
  --served-model-name hydrogen-1.0 \
  --trust-remote-code --kv_cache_dtype fp8 -tp 4 \
  --host 0.0.0.0 --server-port 8001

ATOM uses --server-port for HTTP; --port is internal engine communication. The API model ID is hydrogen-1.0. Available context and output budgets depend on runtime, service settings and memory; this command does not establish 1M-context operation on every setup.

Monitor KV cache utilization and eviction under load. In the team's reported TP4 test, 128 concurrent long reasoning requests filled the pool and triggered eviction, with a sharp drop in answer quality; at 48 concurrent requests the pool stayed below 75% and quality was normal. These are deployment-specific observations, not universal concurrency limits.

Keep the matching end-of-turn settings in generation_config.json, along with the tokenizer, chat template, model and quantization configurations, and weight index. The reported deployment relies on these settings for correct stopping.

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:
    rai