---
base_model: LiquidAI/LFM2.5-350M
library_name: peft
license: other
license_name: lfm1.0
license_link: https://huggingface.co/LiquidAI/LFM2.5-350M/blob/main/LICENSE
pipeline_tag: text-generation
tags:
  - lora
  - grpo
  - reinforcement-learning
  - structured-output
  - json
  - yaml
  - ifstruct
  - lfm2
---

# LFM2.5-350M · IFStruct LoRA adapter

A LoRA adapter (rank 8, alpha 16; attention `q/k/v_proj`, `out_proj`, short-conv `in_proj`/`out_proj`,
MLP `w1/w2/w3`) for [LiquidAI/LFM2.5-350M](https://huggingface.co/LiquidAI/LFM2.5-350M), trained with
reinforcement learning (GRPO) to follow structured-output instructions: emit valid JSON/YAML that
matches a requested schema, wrapper key, item count, code-block and no-commentary constraints.

The base model's license applies (see [LiquidAI/LFM2.5-350M](https://huggingface.co/LiquidAI/LFM2.5-350M)).

## IFStruct v1.0 result

Scored on the full 2,000-row test set of [LiquidAI/ifstruct-v1.0](https://huggingface.co/datasets/LiquidAI/ifstruct-v1.0)
with the benchmark's own validator (byte-identical to `Liquid4All/ifstruct`). Both rows below were run on the
same machine and vLLM version.

| Model | pass@1 | JSON | YAML |
|---|---|---|---|
| **this adapter** | **52.55** | 53.0 | 52.1 |
| LFM2.5-350M (base) | 22.85 | 18.7 | 27.0 |

Eval setup: greedy decoding (temperature 0), 1 sample per prompt, `max_tokens` 16000, no system prompt,
the model's chat template, no constrained decoding, vLLM 0.24. No response exceeded 2,859 tokens.
Full per-row outputs (prompt, response, validator errors) for both runs are in [`eval/`](./eval/).

Largest remaining failure kinds: missing required fields and extraneous fields — the model often does not
produce exactly the requested set of keys. Enum, code-block, value-range and type errors dropped 3-4x vs base.

## Training

- Method: GRPO (verl 0.9.0), CISPO policy loss, KL loss (low_var_kl, 0.01) to the base model,
  zero-variance group filtering, 16 prompts × 16 rollouts per step, rollout temperature 1.0, lr 1e-4,
  max response 4096 tokens. Hybrid conv layers: trained without sequence packing (`use_remove_padding: false`).
- Reward: binary pass/fail from the IFStruct validator. No judge model.
- Data: 4,293 synthetic prompts, 3 epochs; this is the step-270 checkpoint (of ~276).
- None of the 2,000 benchmark prompts or their entity types appear in the training data
  (checked by exact prompt and prompt-prefix match).

## Disclosure: how the benchmark was used

This is **not a blind held-out score**:

1. **Data targeting.** The synthetic training set was generated with its failure-mode mix steered toward
   failure types observed on this benchmark (for a different model). No benchmark rows were copied.
2. **Checkpoint selection.** The checkpoint was chosen using validation slices drawn from this benchmark
   (≈288 of the 2,000 rows).

Expect a lower number on genuinely unseen structured-output distributions.

## Usage

```python
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base = "LiquidAI/LFM2.5-350M"
tok = AutoTokenizer.from_pretrained(base)
model = AutoModelForCausalLM.from_pretrained(base, torch_dtype="auto", device_map="auto")
model = PeftModel.from_pretrained(model, "ichetandhembre/lfm2.5-350m-ifstruct-lora-adaptor")
```

With vLLM: `vllm serve LiquidAI/LFM2.5-350M --enable-lora --max-lora-rank 8 --lora-modules ifstruct=ichetandhembre/lfm2.5-350m-ifstruct-lora-adaptor`.