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LFM2.5-1.2B-Instruct_boe_legal_pre_alpha

Name: LFM2.5-1.2B-Instruct_boe_legal_pre_alpha
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LFM2.5-1.2B-Instruct_boe_legal_pre_alpha

A Spanish legal assistant fine-tuned on the Boletín Oficial del Estado (BOE).

This is a pre-alpha specialist model derived from the general-purpose LiquidAI/LFM2.5-1.2B-Instruct base model, fine-tuned to produce question → answer legal pairs grounded in real Spanish legislation published in the Boletín Oficial del Estado (BOE). The original files has been extracted from the Legalize-es repo that has a complete tracking of every Spanish law and all its revisions.

It is intended as an RAG/retrieval-grounded legal content generator: given a legal passage from the BOE, it produces a realistic legal question and a structured, article-citing answer that is strictly anchored to the provided text.

⚠️ Pre-alpha status. This model is an early experimental checkpoint. It is provided for research and evaluation purposes only and must not be used for legal advice. Always verify any generated content against the authoritative BOE source (boe.es) and a qualified legal professional.

Quick Facts
Base Model
Model Description
Training Data
Dataset Statistics
Intended Uses
Limitations
How to Use
License
Citation
Acknowledgements
BOE Apppendix

Quick Facts

Property	Value
Model name	`jasonjimnz/LFM2.5-1.2B-Instruct_boe_legal_pre_alpha`
Base model	`LiquidAI/LFM2.5-1.2B-Instruct`
Parameters	1.17B
Architecture	LFM2 (`lfm2`) — hybrid double-gated convolution + GQA
Layers	16 (10 double-gated conv blocks + 6 GQA blocks)
Vocabulary size	65,536
Context length	32,768 tokens
Languages	Spanish (fine-tune); base supports ES/EN/AR/ZH/FR/DE/JA/KO
Format	GGUF (Q4_K_M, 4-bit)
File size	~731 MB
Fine-tuning framework	Unsloth
License	`lfm1.0`
Paper	LFM2 Technical Report — arXiv:2511.23404

Base Model

The model is fine-tuned from LiquidAI/LFM2.5-1.2B-Instruct, the general-purpose instruction-tuned member of the LFM2.5 family from Liquid AI, designed for on-device deployment.

Key characteristics of the base model (from the official model card):

Parameters: 1.17B
Layers: 16 (10 double-gated convolution blocks + 6 GQA blocks)
Training budget: 28T tokens (extended pre-training)
Context length: 32,768 tokens
Vocabulary size: 65,536
Knowledge cutoff: Mid-2024
Languages: English, Arabic, Chinese, French, German, Japanese, Korean, Spanish
Recommended generation params (base): temperature: 0.1, top_k: 50, repetition_penalty: 1.05
License: lfm1.0

Base model bibtex:

bibtex

@article{liquidai2025lfm2,
  title={LFM2 Technical Report},
  author={Liquid AI},
  journal={arXiv preprint arXiv:2511.23404},
  year={2025}
}

Model Description

This checkpoint applies supervised fine-tuning (SFT) to the LFM2.5-1.2B-Instruct base model using a curated RAFT-style synthetic dataset of Spanish legal Q&A pairs. Each training example consists of:

a contexto — a real chunk of Spanish legislation drawn from the BOE (including YAML-style front matter with identifier, rank, publication_date, status, department, subjects, etc. and the article text),
a pregunta — a complex, realistic legal question answerable exactly from that context,
a respuesta — a structured technical answer citing the relevant article(s) of the cited norm.

The fine-tuned weights were produced and exported to GGUF (Q4_K_M) using Unsloth.

Training Data

Dataset: Crafted with ChatML Studio, 1.5% of BOE content

A single-file JSONL dataset (one JSON object per line). The primary data source is the Boletín Oficial del Estado (BOE), the official gazette of Spain, containing a broad range of Spanish legal instruments.

Each record has the following structure:

json

{
  "metadata_origen": {
    "file_path": "legalize-es/es/BOE-A-1864-7880.md",
    "file_name": "BOE-A-1864-7880.md",
    "doc_total_words": 157,
    "doc_total_lines": 49,
    "chunk_index": 0
  },
  "contexto": "---\ntitle: \"Real Decreto...\"\nidentifier: \"BOE-A-1864-7880\"\n...\n# Real Decreto...\n###### Artículo 1.\n...",
  "pregunta": "¿Cuál es el alcance normativo del Real Decreto...?",
  "respuesta": "De conformidad con lo dispuesto en el Artículo... "
}

metadata_origen — source document and chunk metadata.
contexto — [SYSTEM] the legal passage (BOE metadata front matter + article text). This is the "column with the article from BOE".
pregunta — [USER] the model-generated (RAFT) legal question.
respuesta — [ASSISTANT] the model-generated structured legal answer.

Dataset Statistics

Aggregated statistics computed directly from dataset_boe_raft_20260820_133225.jsonl:

Metric	Value
Total entries	9,825
Unique law references (identifiers)	1,713
Unique source files	1,826
Chronological coverage	years 1835 – 2026
Avg. context length	~1,386 chars
Avg. question length	~298 chars
Avg. answer length	~893 chars
Malformed lines	0

Source distribution of unique legal references (dominant source is BOE):

Prefix	Bulletin	Count
`BOE`	Boletín Oficial del Estado (national)	1,706
`BOCL`	Boletín Oficial Castilla y León	2
`BOIB`	Boletín Oficial Illes Balears	2
`BOCT`	Boletín Oficial Castilla-La Mancha	1
`DOGV`	Diari Oficial GVA (Valencia)	1
`BORM`	Boletín Oficial Región de Murcia	1

Top norm types (rank):

rank	Count
orden	512
ley	397
real_decreto	355
resolucion	203
instruccion	70
ley_organica	34
decreto	33
acuerdo_internacional	28
ley_foral	25
real_decreto_ley	17

Legal statuses represented: in_force, repealed, annulled, expired, Finalizado, L.

Top subject areas (subjects): Epidemias, Comunidades Autónomas, Colegios Profesionales, Organización de la Administración del Estado, Estado de alarma, Sanidad, Enfermedades, Procedimiento administrativo, Elecciones, Seguridad Social, Registro Civil, Formularios administrativos, Administración de Justicia, Acuerdos internacionales, Libre circulación de personas.

The user-reported count of "~1800 different references" matches the 1,826 unique source files (and 1,713 unique law identifiers) in the corpus.

Intended Uses

The model is designed for retrieval-augmented generation (RAG) and legal content generation over Spanish law, specifically:

RLHF/RAFT-style data distillation — generating new synthetic question/answer pairs for distillation or as a teacher for smaller models.
Retrieval-grounded legal QA — given a BOE passage (retrieved by a vector search like ChromaDB), produce a question and an answer anchored to that passage.
Legal document understanding — summarising/extracting the operative provisions (articles) of a given Spanish legal instrument.

Given the small 1.2B size and Q4_K_M quantization, it is well suited to on-device and edge deployments (sub-1 GB memory footprint) with llama.cpp/vLLM.

Limitations

Pre-alpha / experimental quality; not benchmarked for legal accuracy.
Not a replacement for a lawyer. Outputs may contain hallucinations, omit amendments, or misinterpret consolidated text.
Knowledge cutoff mid-2024 (inherited from the base model); only the legislative text present in the contexto is authoritative for this model.
Grounding-dependent. Answers are only reliable when a relevant BOE passage is supplied in the context. Free-form (non-grounded) legal questions will degrade in quality and may hallucinate.
Trained predominantly on BOE content; coverage of a small number of regional bulletin types is minimal.
Spanish-centric fine-tune; other languages are outside scope.

How to Use

Prompt Format / Chat Template

LFM2.5 uses a ChatML-like template. The recommended system prompt for legal Q&A (matching the training setup) is:

<|startoftext|><|im_start|>system
Eres un jurista experto espanol especializado en el BOE.
Dada una seccion del BOE, genera UNA pregunta compleja y realista y su respuesta,
ambas estrictamente basadas en el texto. Responde en JSON con las claves
"pregunta" y "respuesta".<|im_end|>
<|im_start|>user
Contexto legal (BOE):
{contexto}

Genera el JSON:<|im_end|>
<|im_start|>assistant

In code, prefer tokenizer.apply_chat_template() (Transformers) or the chat_format="chatml" handling provided by the runtime to format messages correctly.

llama-cpp-python

pip install llama-cpp-python

Download the GGUF and load it locally:

python

from llama_cpp import Llama

MODEL = "LFM2.5-1.2B-Instruct.Q4_K_M.gguf"  # or a local path

llm = Llama(
    model_path=MODEL,
    n_ctx=32768,          # base context length
    n_threads=8,          # CPU threads
    chat_format="chatml", # ChatML-style template used by LFM2.5
    verbose=False,
)

# Minimal example
out = llm.create_chat_completion(
    messages=[
        {
            "role": "system",
            "content": (
                "Eres un jurista experto espanol especializado en el BOE. "
                "Responde en JSON con las claves 'pregunta' y 'respuesta'."
            ),
        },
        {
            "role": "user",
            "content": "Dame un ejemplo de uso legal de este modelo.",
        },
    ],
    temperature=0.3,
    max_tokens=1024,
)
print(out["choices"][0]["message"]["content"])

RAG-style example (context-grounded):

python

contexto = """
# Real Decreto de 10 de octubre de 1864, suprimiendo las Grandezas de Espana honorarios
###### Articulo 1.
Queda suprimida la clase de Grandes de Espana honorarios.
###### Articulo 2.
Los que hoy corresponden a esta clase quedan declarados Grandes de Espana en propiedad.
"""

out = llm.create_chat_completion(
    messages=[
        {
            "role": "system",
            "content": (
                "Eres un jurista experto espanol especializado en el BOE. "
                "Genera UNA pregunta compleja y su respuesta, ambas basadas "
                "exclusivamente en el contexto. Responde en JSON con las claves "
                "'pregunta' y 'respuesta'. No inventes datos."
            ),
        },
        {"role": "user", "content": f"Contexto legal (BOE):\n{contexto}\n\nGenera el JSON:"},
    ],
    temperature=0.3,
    max_tokens=1024,
)
print(out["choices"][0]["message"]["content"])

💡 The official GGUF distribution also exposes a built-in OpenAI-compatible server: llama-server -hf jasonjimnz/LFM2.5-1.2B-Instruct_boe_legal_pre_alpha:Q4_K_M.

vLLM

Note: this checkpoint is distributed as GGUF. vLLM can serve GGUF models directly via its llama.cpp backend (--load-format gguf). For the full vLLM feature set (tensor parallelism, better throughput, flash-attention), convert to HF safetensors format first.

Option A — serve the GGUF directly (OpenAI-compatible server):

bash

pip install vllm

VLLM_USE_V1=1 vllm serve "jasonjimnz/LFM2.5-1.2B-Instruct_boe_legal_pre_alpha" \
    --load-format gguf \
    --max-model-len 32768 \
    --dtype float16

Then call it over HTTP:

bash

curl -X POST "http://localhost:8000/v1/chat/completions" \
  -H "Content-Type: application/json" \
  --data '{
    "model": "jasonjimnz/LFM2.5-1.2B-Instruct_boe_legal_pre_alpha",
    "max_tokens": 1024,
    "temperature": 0.3,
    "messages": [
      {"role": "system", "content": "Eres un jurista experto espanol especializado en el BOE. Responde en JSON con las claves pregunta y respuesta."},
      {"role": "user", "content": "Contexto legal (BOE):\n<inserte texto del BOE aqui>\n\nGenera el JSON:"}
    ]
  }'

Option B — use vLLM via the Python OpenAI client:

python

from openai import OpenAI

client = OpenAI(base_url="http://localhost:8000/v1", api_key="EMPTY")

resp = client.chat.completions.create(
    model="jasonjimnz/LFM2.5-1.2B-Instruct_boe_legal_pre_alpha",
    messages=[
        {
            "role": "system",
            "content": "Eres un jurista experto espanol especializado en el BOE. "
                       "Responde en JSON con las claves 'pregunta' y 'respuesta'.",
        },
        {
            "role": "user",
            "content": "Contexto legal (BOE):\n<inserte texto del BOE aqui>\n\nGenera el JSON:",
        },
    ],
    temperature=0.3,
    max_tokens=1024,
)
print(resp.choices[0].message.content)

Option C — full performance (recommended for production): convert the GGUF to HF safetensors, then serve with plain vLLM:

bash

pip install "huggingface_hub[cli]"
# 1. Download the safetensors/convert from GGUF (e.g. with llama.cpp convert, or the
#    repo's source safetensors if a non-GGUF revision is published)
# 2. Then serve:
vllm serve jasonjimnz/LFM2.5-1.2B-Instruct_boe_legal_pre_alpha

Transformers (optional)

If a non-GGUF (safe) checkpoint is available, the base-style Transformers recipe applies:

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "jasonjimnz/LFM2.5-1.2B-Instruct_boe_legal_pre_alpha",
    device_map="auto",
    dtype="bfloat16",
)
tokenizer = AutoTokenizer.from_pretrained(
    "jasonjimnz/LFM2.5-1.2B-Instruct_boe_legal_pre_alpha"
)

messages = [{"role": "user", "content": "¿Qué es una ley orgánica en España?"}]
inputs = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True, tokenize=True, return_tensors="pt"
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.3)
print(tokenizer.decode(outputs[0][inputs.shape[-1]:], skip_special_tokens=True))

License

This model is released under the lfm1.0 license (inherited from the base model LiquidAI/LFM2.5-1.2B-Instruct). Please review the base model license for the full terms and any redistribution/attribution requirements.

Citation

If you use this model in research, please cite the base model:

bibtex

@article{liquidai2025lfm2,
  title={LFM2 Technical Report},
  author={Liquid AI},
  journal={arXiv preprint arXiv:2511.23404},
  year={2025}
}

Acknowledgements

Base model: Liquid AI — LFM2.5-1.2B-Instruct
Fine-tuning tooling: Unsloth
Data source: Legalize-es

Appendix

Articles inclduded (total or partially)

text

BOCL-h-1996-90001
BOCL-h-2016-90367
BOCT-c-1983-90029
BOE-A-1835-2348
BOE-A-1851-4969
BOE-A-1855-3318
BOE-A-1858-8874
BOE-A-1864-7880
BOE-A-1866-9441
BOE-A-1887-4896
BOE-A-1902-3758
BOE-A-1908-5579
BOE-A-1912-1123
BOE-A-1913-59
BOE-A-1915-143
BOE-A-1915-3087
BOE-A-1915-660
BOE-A-1918-1359
BOE-A-1918-3943
BOE-A-1918-4414
BOE-A-1922-7310
BOE-A-1938-11758
BOE-A-1938-12791
BOE-A-1945-7401
BOE-A-1948-11095
BOE-A-1948-40366
BOE-A-1951-4876
BOE-A-1951-5153
BOE-A-1952-7152
BOE-A-1954-18708
BOE-A-1955-15936
BOE-A-1955-2687
BOE-A-1957-17299
BOE-A-1957-7538
BOE-A-1958-17089
BOE-A-1959-11603
BOE-A-1960-1741
BOE-A-1960-5625
BOE-A-1960-5626
BOE-A-1960-8510
BOE-A-1961-943
BOE-A-1962-14073
BOE-A-1962-4442
BOE-A-1962-8684
BOE-A-1963-8151
BOE-A-1964-5448
BOE-A-1964-6622
BOE-A-1965-1
BOE-A-1965-10709
BOE-A-1965-8952
BOE-A-1966-10136
BOE-A-1966-6898
BOE-A-1967-16640
BOE-A-1967-19938
BOE-A-1967-5595
BOE-A-1967-6804
BOE-A-1967-7305
BOE-A-1968-1316
BOE-A-1968-542
BOE-A-1968-909
BOE-A-1969-1162
BOE-A-1969-168
BOE-A-1969-617
BOE-A-1969-643
BOE-A-1969-817
BOE-A-1969-915
BOE-A-1970-541
BOE-A-1971-10230
BOE-A-1971-10239
BOE-A-1971-1196
BOE-A-1971-1247
BOE-A-1971-1616
BOE-A-1971-309
BOE-A-1971-453
BOE-A-1971-516
BOE-A-1971-581
BOE-A-1971-754
BOE-A-1972-1176
BOE-A-1972-1438
BOE-A-1972-1567
BOE-A-1972-1582
BOE-A-1972-303
BOE-A-1972-351
BOE-A-1972-845
BOE-A-1973-126
BOE-A-1973-1430
BOE-A-1973-1722
BOE-A-1973-92
BOE-A-1974-1048
BOE-A-1974-1154
BOE-A-1974-1349
BOE-A-1974-1448
BOE-A-1974-1482
BOE-A-1974-1515
BOE-A-1974-1516
BOE-A-1974-1520
BOE-A-1974-1845
BOE-A-1974-2058
BOE-A-1974-315
BOE-A-1974-661
BOE-A-1974-787
BOE-A-1975-11040
BOE-A-1975-11042
BOE-A-1975-14173
BOE-A-1975-16431
BOE-A-1975-16964
BOE-A-1975-18620
BOE-A-1975-22434
BOE-A-1975-23876
BOE-A-1975-23877
BOE-A-1975-24066
BOE-A-1975-24703
BOE-A-1976-11503
BOE-A-1976-11919
BOE-A-1976-14457
BOE-A-1976-15228
BOE-A-1976-15545
BOE-A-1976-18035
BOE-A-1976-18294
BOE-A-1976-18818
BOE-A-1976-24045
BOE-A-1976-24774
BOE-A-1976-24912
BOE-A-1977-12123
BOE-A-1977-12678
BOE-A-1977-12737
BOE-A-1977-13678
BOE-A-1977-15037
BOE-A-1977-15607
BOE-A-1977-1909
BOE-A-1977-2059
BOE-A-1977-21581
BOE-A-1977-23050
BOE-A-1977-24937
BOE-A-1977-29877
BOE-A-1977-31585
BOE-A-1977-3981
BOE-A-1977-458
BOE-A-1977-465
BOE-A-1977-7066
BOE-A-1977-8602
BOE-A-1977-8603
BOE-A-1977-8607
BOE-A-1978-11099
BOE-A-1978-11314
BOE-A-1978-11879
BOE-A-1978-17687
BOE-A-1978-20758
BOE-A-1978-20837
BOE-A-1978-21522
BOE-A-1978-23134
BOE-A-1978-24714
BOE-A-1978-25111
BOE-A-1978-25805
BOE-A-1978-27612
BOE-A-1978-5340
BOE-A-1978-6473
BOE-A-1978-6997
BOE-A-1978-7584
BOE-A-1979-15303
BOE-A-1979-1873
BOE-A-1979-21590
BOE-A-1979-23711
BOE-A-1979-25084
BOE-A-1979-26445
BOE-A-1979-26727
BOE-A-1979-27191
BOE-A-1979-28529
BOE-A-1979-29498
BOE-A-1979-30490
BOE-A-1979-3280
BOE-A-1979-3281
BOE-A-1979-3393
BOE-A-1979-473
BOE-A-1979-474
BOE-A-1979-4741
BOE-A-1979-696
BOE-A-1979-867
BOE-A-1979-88
BOE-A-1979-9453
BOE-A-1980-10312
BOE-A-1980-11021
BOE-A-1980-1206
BOE-A-1980-13662
BOE-A-1980-13664
BOE-A-1980-15890
BOE-A-1980-15955
BOE-A-1980-15958
BOE-A-1980-16732
BOE-A-1980-18583
BOE-A-1980-21392
BOE-A-1980-22029
BOE-A-1980-23701
BOE-A-1980-24747
BOE-A-1980-25359
BOE-A-1980-25868
BOE-A-1980-26006
BOE-A-1980-27705
BOE-A-1980-3213
BOE-A-1980-405
BOE-A-1980-4214
BOE-A-1980-4552
BOE-A-1980-530
BOE-A-1980-5856
BOE-A-1980-6530
BOE-A-1980-723
BOE-A-1980-7574
BOE-A-1980-9427
BOE-A-1981-12775
BOE-A-1981-14916
BOE-A-1981-15417
BOE-A-1981-15424
BOE-A-1981-16145
BOE-A-1981-22578
BOE-A-1981-23039
BOE-A-1981-2368
BOE-A-1981-24155
BOE-A-1981-25101
BOE-A-1981-25913
BOE-A-1981-25915
BOE-A-1981-25918
BOE-A-1981-26082
BOE-A-1981-26452
BOE-A-1981-28045
BOE-A-1981-28140
BOE-A-1981-28756
BOE-A-1981-29162
BOE-A-1981-29376
BOE-A-1981-29556
BOE-A-1981-4585
BOE-A-1981-6317
BOE-A-1981-6683
BOE-A-1981-9716
BOE-A-1982-1
BOE-A-1982-10861
BOE-A-1982-11138
BOE-A-1982-1407
BOE-A-1982-16122
BOE-A-1982-16666
BOE-A-1982-16853
BOE-A-1982-17501
BOE-A-1982-17675
BOE-A-1982-17775
BOE-A-1982-18283
BOE-A-1982-19813
BOE-A-1982-20822
BOE-A-1982-21182
BOE-A-1982-25035
BOE-A-1982-26295
BOE-A-1982-27483
BOE-A-1982-28597
BOE-A-1982-34268
BOE-A-1982-7584
BOE-A-1982-8394
BOE-A-1982-9141
BOE-A-1982-9446
BOE-A-1983-10145
BOE-A-1983-10528
BOE-A-1983-11942
BOE-A-1983-15100
BOE-A-1983-17040
BOE-A-1983-17371
BOE-A-1983-18199
BOE-A-1983-19655
BOE-A-1983-20070
BOE-A-1983-20354
BOE-A-1983-20358
BOE-A-1983-21329
BOE-A-1983-21541
BOE-A-1983-22149
BOE-A-1983-23964
BOE-A-1983-24068
BOE-A-1983-25248
BOE-A-1983-26187
BOE-A-1983-27335
BOE-A-1983-27800
BOE-A-1983-28131
BOE-A-1983-28352
BOE-A-1983-28429
BOE-A-1983-31093
BOE-A-1983-31438
BOE-A-1983-32415
BOE-A-1983-33957
BOE-A-1983-34168
BOE-A-1983-5313
BOE-A-1984-1
BOE-A-1984-10264
BOE-A-1984-10265
BOE-A-1984-11449
BOE-A-1984-11619
BOE-A-1984-12099
BOE-A-1984-12186
BOE-A-1984-12187
BOE-A-1984-12724
BOE-A-1984-12729
BOE-A-1984-14761
BOE-A-1984-14883
BOE-A-1984-14938
BOE-A-1984-16655
BOE-A-1984-17054
BOE-A-1984-17575
BOE-A-1984-1856
BOE-A-1984-21683
BOE-A-1984-22625
BOE-A-1984-25654
BOE-A-1984-27820
BOE-A-1984-3020
BOE-A-1984-3139
BOE-A-1984-5916
BOE-A-1984-7248
BOE-A-1984-7388
BOE-A-1984-7391
BOE-A-1984-9043
BOE-A-1984-9190
BOE-A-1985-10245
BOE-A-1985-10478
BOE-A-1985-12305
BOE-A-1985-12676
BOE-A-1985-12771
BOE-A-1985-14922
BOE-A-1985-15029
BOE-A-1985-1604
BOE-A-1985-16448
BOE-A-1985-16532
BOE-A-1985-18027
BOE-A-1985-1807
BOE-A-1985-18351
BOE-A-1985-18485
BOE-A-1985-18558
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BOIB-i-1984-90002
BOIB-i-2013-90020
BORM-s-2020-90315
DOGV-r-1985-90017

jasonjimnz/LFM2.5-1.2B-Instruct_boe_legal_pre_alpha

LFM2.5-1.2B-Instruct_boe_legal_pre_alpha

Table of Contents

Quick Facts

Base Model

Model Description

The fine-tuned weights were produced and exported to GGUF (Q4_K_M) using Unsloth.

Training Data

Dataset Statistics

Intended Uses

Limitations

How to Use

Prompt Format / Chat Template

llama-cpp-python

vLLM

Transformers (optional)

License

Citation

Acknowledgements

Appendix

jasonjimnz/
LFM2.5-1.2B-Instruct_boe_legal_pre_alpha