lfm2-hybridai-perls-sft-merged/README.md

---
license: apache-2.0
base_model: LiquidAI/LFM2-1.2B
tags:
  - sft
  - gestalt
library_name: transformers
pipeline_tag: text-generation
---

# Factor168/lfm2-hybridai-perls-sft-merged

Full merged checkpoint of `LiquidAI/LFM2-1.2B` fine-tuned (LoRA, merged) on Gestalt dialogue
(Bry=user, Fritz=assistant) using 1–10 turn sliding windows from `perls/perls_sft.jsonl`
(combined `perls.txt` + `perls_synth.txt`).

## Model Details
- Base: `LiquidAI/LFM2-1.2B`
- Method: LoRA SFT, merged into base (`merge_and_unload`)
- Epochs: 2
- Precision during training: bf16 if available, else fp16/float32
- Repo type: full merged model (no external adapter needed)

## Data
- Source: `perls/perls_sft.jsonl`
- Format: chat `messages` with user/assistant roles
- Preprocessing: 1–10 turn sliding windows over each conversation

## Training (script)
- Script: `perls/train_perls_sft_lora.py`
- LR: 2e-4 (default in script)
- Batch: see script defaults (per_device_train_batch_size=4, grad_accum=2)
- Warmup ratio: 0.03
- Weight decay: 0.01

## Inference (Transformers)
Recommended sampling (matches training demo):
- temperature=0.3
- min_p=0.15
- repetition_penalty=1.05

Example outputs after fine-tuning (using the same sampling parameters):

- User: Ich bin gestresst.
  Assistant: Richtig! Was machen Sie mit dem Stress?
- User: Ich kann mich nicht motivieren.
  Assistant: Das ist eine Stimmung, nicht ein Problem.
- User: Ich habe Angst.
  Assistant: Natürlich! Angst ist eine gute Begleiterin. Was ist Ihre Hauptangst?
- User: Wer sind Sie?
  Assistant: Sie sind alles, was ich bin.
- User: Dr. Perls, was machen Sie?
  Assistant: Ich beobachte Sie!
- User: Was ist Gestalttherapie?
  Assistant: Ich mag sie nicht!

## Tokenized prompts (input_ids)
- `Ich bin gestresst.` → [1, 21221, 11572, 6658, 1276, 820, 523]
- `Ich kann mich nicht motivieren.` → [1, 21221, 6417, 16169, 3355, 19044, 6110, 523]
- `Ich habe Angst.` → [1, 21221, 12845, 63888, 523]
- `Wer sind Sie?` → [1, 34633, 3987, 3612, 540]
- `Dr. Perls, was machen Sie?` → [1, 9549, 523, 3563, 4442, 521, 953, 18532, 3612, 540]
- `Was ist Gestalttherapie?` → [1, 28519, 2168, 22185, 2369, 1118, 39134, 540]

```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

repo = "Factor168/lfm2-hybridai-perls-sft-merged"
tok = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(repo, torch_dtype="auto").eval()

prompt = "Ich bin gestresst."
text = tok.apply_chat_template(
    [{"role": "user", "content": prompt}],
    tokenize=False,
    add_generation_prompt=True,
)
inputs = tok(text, return_tensors="pt").to(model.device)
out = model.generate(
    **inputs,
    max_new_tokens=256,
    do_sample=True,
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    pad_token_id=tok.pad_token_id,
)
print(tok.decode(out[0, inputs['input_ids'].shape[1]:], skip_special_tokens=True))
```

## Inference (vLLM)
vLLM can load this merged checkpoint directly:
```
vllm serve Factor168/lfm2-hybridai-perls-sft-merged               --tensor-parallel-size 1               --max-model-len 2048
```

## Notes
- This is a small, domain-specific SFT; outputs may be terse or stylistically like the source dialogues.
- Safety/quality: no safety tuning; review outputs before production use.