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Model: Factor168/lfm2-hybridai-perls-sft-merged
Source: Original Platform

README.md

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+---
+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.