Agents-K1

Knowledge extraction model in Agents-K1 is a 4B-parameter language model fine-tuned from

Qwen/Qwen3-4B-Instruct-2507

with GRPO (Group Relative Policy Optimization) on the information-extraction

corpus, targeting Named Entity Recognition (NER) and Relation Extraction (RE)

in English scientific and general-domain text.

The model produces structured JSON extractions with explicit step-by-step

reasoning, enabling its use as a building block in downstream knowledge-graph

construction, citation linking, and multi-hop QA pipelines.

Highlights

+3.3 absolute F1 averaged over 10 NER/RE benchmarks vs. the

Qwen3-4B-Instruct base model, with gains on every dataset evaluated

(including held-out CrossNER domains).

Trained with rule-based rewards (format + JSON validity + entity/relation F1),

no human preference data required.

Outputs follow a strict …… schema, making

reasoning auditable and JSON parsing reliable.

Intended use

Designed as an extraction backbone for:

Scientific-literature mining (entities/relations in biomedicine, chemistry,

CS, etc.)

Knowledge-graph construction
Pre-processing for retrieval / multi-hop QA systems

Not intended for general-purpose chat — it has been specialized for structured

extraction.

Usage

The model uses the same chat template as Qwen3-4B-Instruct and expects a

schema-driven user prompt. The reply will contain a `` block followed by

an `` block with a JSON object.

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "InternScience/Agents-K1"
tok   = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="bfloat16", device_map="auto")

system = (
    "You are an expert in information extraction. Given a task instruction "
    "with schema definitions and input text, extract the required information.\n\n"
    "You should think step by step about the extraction task, then provide "
    "your answer in JSON format.\n\n"
    "Format your response as:\n"
    "\nYour step-by-step reasoning...\n\n"
    "\nYour JSON extraction result here\n"
)

user = (
    "You are an expert in named entity recognition. Please extract entities "
    "that match the schema definition from the input. Return an empty list if "
    "the entity type does not exist. Please respond in the format of a JSON "
    "dictionary.\n\n"
    'Entity types to extract: ["person", "organization", "location"]\n\n'
    "Input text: Marie Curie worked at the University of Paris.\n\n"
    "Please think step by step and respond in the following format:\n"
    "\nYour reasoning process...\n\n"
    "\nYour JSON extraction result\n"
)

messages = [{"role": "system", "content": system},
            {"role": "user",   "content": user}]
inputs = tok.apply_chat_template(messages, add_generation_prompt=True,
                                 return_tensors="pt").to(model.device)
out = model.generate(inputs, max_new_tokens=512, do_sample=False)
print(tok.decode(out[0][inputs.shape[-1]:], skip_special_tokens=True))

For RE, replace the user template with Relation types to extract: [...]

and a relation-extraction instruction; the output schema is a JSON dict mapping

relation types to lists of {head, tail} pairs.

Training data

Training data comes from IEPile, restricted to:

English NER and RE tasks
22 source datasets, mixing scientific (SciERC, GENIA_NER, BC5CDR, BC2GM,

BC4CHEMD, AnatEM, NCBI) and general-domain (CoNLL2003, conll04, FabNER,

MultiNERD, NYT11, kbp37, …) corpora

| Split | Size | Notes |

|-----------:|-------:|-------|

| Train | 14,400 | 90/10 split, seed=42; each source capped to balance the mix |

| Validation | 1,600 | |

70% of samples have non-empty gold labels; 30% are empty-label cases (to prevent

the model from defaulting to non-empty outputs).

Training procedure

Algorithm: GRPO (PPO without a critic), implemented in

veRL.

Reward ∈ \[0, 1\]:
format reward: 0.1 · 𝟙[has ] + 0.1 · 𝟙[has ]
JSON validity: 0.1 · 𝟙[valid JSON dict] (or 0.05 for non-dict valid JSON)
task F1: 0.7 · F1(pred, gold) — entity-set F1 for NER, triple-set F1 for RE

Evaluation

Reported numbers are micro-F1 on each benchmark's official test split, using

the same prompt template as training. Gains are base → Agents-K1 (GRPO).

|---------------------------------|:----:|------:|--------:|--------------:|------:|

| CoNLL2003 | NER | 3,184 | 0.6547 | 0.7007 | +0.046 |

| NCBI-Disease | NER | 937 | 0.6737 | 0.7340 | +0.060 |

| BC5CDR | NER | 4,788 | 0.7126 | 0.7494 | +0.037 |

| CrossNER — AI (held-out) | NER | 430 | 0.4862 | 0.5400 | +0.054 |

| CrossNER — Literature (held) | NER | 416 | 0.5462 | 0.5736 | +0.027 |

| CrossNER — Music (held) | NER | 457 | 0.5791 | 0.6050 | +0.026 |

| CrossNER — Politics (held) | NER | 650 | 0.6611 | 0.6855 | +0.024 |

| CrossNER — Science (held) | NER | 532 | 0.5928 | 0.6132 | +0.020 |

| SciERC | NER | 397 | 0.1166 | 0.1270 | +0.010 |

| conll04 | RE | 287 | 0.2933 | 0.3181 | +0.025 |

| Average | | | 0.5317 | 0.5647 | +0.033 |

All 10/10 benchmarks improve, including the 5 CrossNER domains that are

not in the training mix — evidence of generalization rather than mere

fitting to in-distribution sources.

Limitations

Schema-driven prompting required. Free-form questions will likely

return malformed JSON; always supply explicit entity / relation type lists.

License

Released under the Apache-2.0 license, following the upstream

Qwen3-4B-Instruct-2507

license. Users must also comply with the licenses of the IEPile component

datasets when using this model in derivative works.