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

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
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+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
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+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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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.

chat_template.jinja

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+{{- bos_token -}}
+{%- set system_prompt = "" -%}
+{%- set ns = namespace(system_prompt="") -%}
+{%- if messages[0]["role"] == "system" -%}
+	{%- set ns.system_prompt = messages[0]["content"] -%}
+	{%- set messages = messages[1:] -%}
+{%- endif -%}
+{%- if tools -%}
+	{%- set ns.system_prompt = ns.system_prompt + ("\n" if ns.system_prompt else "") + "List of tools: <|tool_list_start|>[" -%}
+	{%- for tool in tools -%}
+		{%- if tool is not string -%}
+            {%- set tool = tool | tojson -%}
+		{%- endif -%}
+		{%- set ns.system_prompt = ns.system_prompt + tool -%}
+        {%- if not loop.last -%}
+            {%- set ns.system_prompt = ns.system_prompt + ", " -%}
+        {%- endif -%}
+	{%- endfor -%}
+	{%- set ns.system_prompt = ns.system_prompt + "]<|tool_list_end|>" -%}
+{%- endif -%}
+{%- if ns.system_prompt -%}
+	{{- "<|im_start|>system\n" + ns.system_prompt + "<|im_end|>\n" -}}
+{%- endif -%}
+{%- for message in messages -%}
+	{{- "<|im_start|>" + message["role"] + "\n" -}}
+	{%- set content = message["content"] -%}
+	{%- if content is not string -%}
+		{%- set content = content | tojson -%}
+	{%- endif -%}
+	{%- if message["role"] == "tool" -%}
+		{%- set content = "<|tool_response_start|>" + content + "<|tool_response_end|>" -%}
+	{%- endif -%}
+	{{- content + "<|im_end|>\n" -}}
+{%- endfor -%}
+{%- if add_generation_prompt -%}
+	{{- "<|im_start|>assistant\n" -}}
+{%- endif -%}

config.json

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+{
+  "architectures": [
+    "Lfm2ForCausalLM"
+  ],
+  "block_auto_adjust_ff_dim": true,
+  "block_dim": 2048,
+  "block_ff_dim": 12288,
+  "block_ffn_dim_multiplier": 1.0,
+  "block_mlp_init_scale": 1.0,
+  "block_multiple_of": 256,
+  "block_norm_eps": 1e-05,
+  "block_out_init_scale": 1.0,
+  "block_use_swiglu": true,
+  "block_use_xavier_init": true,
+  "bos_token_id": 1,
+  "conv_L_cache": 3,
+  "conv_bias": false,
+  "conv_dim": 2048,
+  "conv_dim_out": 2048,
+  "conv_use_xavier_init": true,
+  "dtype": "bfloat16",
+  "eos_token_id": 7,
+  "hidden_size": 2048,
+  "initializer_range": 0.02,
+  "intermediate_size": 12288,
+  "layer_types": [
+    "conv",
+    "conv",
+    "full_attention",
+    "conv",
+    "conv",
+    "full_attention",
+    "conv",
+    "conv",
+    "full_attention",
+    "conv",
+    "full_attention",
+    "conv",
+    "full_attention",
+    "conv",
+    "full_attention",
+    "conv"
+  ],
+  "max_position_embeddings": 128000,
+  "model_type": "lfm2",
+  "norm_eps": 1e-05,
+  "num_attention_heads": 32,
+  "num_heads": 32,
+  "num_hidden_layers": 16,
+  "num_key_value_heads": 8,
+  "pad_token_id": 0,
+  "rope_theta": 1000000.0,
+  "transformers_version": "4.57.1",
+  "use_cache": true,
+  "use_pos_enc": true,
+  "vocab_size": 65536
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 7,
+  "pad_token_id": 0,
+  "transformers_version": "4.57.1"
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:07f7f8834d87b6b06dea5726a796b425701898d60bc3aa5354f1b214df394aef
+size 2340697936

special_tokens_map.json

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+{
+  "bos_token": {
+    "content": "<|startoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|im_end|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<|pad|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}