101 lines
3.2 KiB
Markdown
101 lines
3.2 KiB
Markdown
---
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license: apache-2.0
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base_model: LiquidAI/LFM2-1.2B
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tags:
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- sft
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- gestalt
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library_name: transformers
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pipeline_tag: text-generation
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---
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# Factor168/lfm2-hybridai-perls-sft-merged
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Full merged checkpoint of `LiquidAI/LFM2-1.2B` fine-tuned (LoRA, merged) on Gestalt dialogue
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(Bry=user, Fritz=assistant) using 1–10 turn sliding windows from `perls/perls_sft.jsonl`
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(combined `perls.txt` + `perls_synth.txt`).
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## Model Details
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- Base: `LiquidAI/LFM2-1.2B`
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- Method: LoRA SFT, merged into base (`merge_and_unload`)
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- Epochs: 2
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- Precision during training: bf16 if available, else fp16/float32
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- Repo type: full merged model (no external adapter needed)
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## Data
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- Source: `perls/perls_sft.jsonl`
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- Format: chat `messages` with user/assistant roles
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- Preprocessing: 1–10 turn sliding windows over each conversation
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## Training (script)
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- Script: `perls/train_perls_sft_lora.py`
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- LR: 2e-4 (default in script)
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- Batch: see script defaults (per_device_train_batch_size=4, grad_accum=2)
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- Warmup ratio: 0.03
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- Weight decay: 0.01
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## Inference (Transformers)
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Recommended sampling (matches training demo):
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- temperature=0.3
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- min_p=0.15
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- repetition_penalty=1.05
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Example outputs after fine-tuning (using the same sampling parameters):
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- User: Ich bin gestresst.
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Assistant: Richtig! Was machen Sie mit dem Stress?
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- User: Ich kann mich nicht motivieren.
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Assistant: Das ist eine Stimmung, nicht ein Problem.
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- User: Ich habe Angst.
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Assistant: Natürlich! Angst ist eine gute Begleiterin. Was ist Ihre Hauptangst?
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- User: Wer sind Sie?
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Assistant: Sie sind alles, was ich bin.
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- User: Dr. Perls, was machen Sie?
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Assistant: Ich beobachte Sie!
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- User: Was ist Gestalttherapie?
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Assistant: Ich mag sie nicht!
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## Tokenized prompts (input_ids)
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- `Ich bin gestresst.` → [1, 21221, 11572, 6658, 1276, 820, 523]
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- `Ich kann mich nicht motivieren.` → [1, 21221, 6417, 16169, 3355, 19044, 6110, 523]
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- `Ich habe Angst.` → [1, 21221, 12845, 63888, 523]
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- `Wer sind Sie?` → [1, 34633, 3987, 3612, 540]
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- `Dr. Perls, was machen Sie?` → [1, 9549, 523, 3563, 4442, 521, 953, 18532, 3612, 540]
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- `Was ist Gestalttherapie?` → [1, 28519, 2168, 22185, 2369, 1118, 39134, 540]
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```python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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repo = "Factor168/lfm2-hybridai-perls-sft-merged"
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tok = AutoTokenizer.from_pretrained(repo)
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model = AutoModelForCausalLM.from_pretrained(repo, torch_dtype="auto").eval()
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prompt = "Ich bin gestresst."
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text = tok.apply_chat_template(
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[{"role": "user", "content": prompt}],
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tokenize=False,
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add_generation_prompt=True,
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)
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inputs = tok(text, return_tensors="pt").to(model.device)
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out = model.generate(
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**inputs,
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max_new_tokens=256,
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do_sample=True,
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temperature=0.3,
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min_p=0.15,
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repetition_penalty=1.05,
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pad_token_id=tok.pad_token_id,
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)
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print(tok.decode(out[0, inputs['input_ids'].shape[1]:], skip_special_tokens=True))
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```
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## Inference (vLLM)
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vLLM can load this merged checkpoint directly:
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```
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vllm serve Factor168/lfm2-hybridai-perls-sft-merged --tensor-parallel-size 1 --max-model-len 2048
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```
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## Notes
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- This is a small, domain-specific SFT; outputs may be terse or stylistically like the source dialogues.
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- Safety/quality: no safety tuning; review outputs before production use.
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