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README.md

@@ -1,3 +1,77 @@
----
-license: apache-2.0
----
+# K2-Inhale (LoRA Adapter)
+
+**Base model:** `LLM360/K2-Think`  
+**Author fine-tune:** `SutanRifkyt`  
+**Method:** QLoRA (4-bit)  
+**Domain:** Lung CT & PET/CT findings (nodule, consolidation, FDG uptake, staging hints)  
+**Use case:** Explain findings in plain language for patients, say how concerning for lung cancer, and suggest next step (follow-up CT, PET-CT, biopsy, urgent oncologist etc).
+
+## How to use
+
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from peft import PeftModel
+
+base_model_id = "LLM360/K2-Think"
+adapter_id    = "SutanRifkyt/K2-Inhale"
+
+tokenizer = AutoTokenizer.from_pretrained(
+    base_model_id,
+    use_fast=False,
+    trust_remote_code=False
+)
+
+model = AutoModelForCausalLM.from_pretrained(
+    base_model_id,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+    trust_remote_code=False
+)
+
+model = PeftModel.from_pretrained(model, adapter_id)
+
+prompt = """<|user|>:
+Explain this chest CT finding in simple language for the patient, assess how concerning it is for lung cancer, and say what should happen next.
+
+Clinical findings:
+Spiculated 1.8 cm nodule in the right upper lobe with irregular margins and increased FDG uptake on PET.
+<|assistant|>:
+"""
+
+inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
+
+with torch.no_grad():
+    output = model.generate(
+        **inputs,
+        max_new_tokens=300,
+        do_sample=False,
+        temperature=0.0,
+    )
+
+print(tokenizer.decode(output[0], skip_special_tokens=True))
+Training data
+
+Merged ~8k instruction-style samples from:
+
+ReXGroundingCT style CT findings (free-text localized abnormalities)
+
+Lung-PET-CT-Dx (TCIA) PET/CT cases with histopathology labels and staging clues
+
+Each sample is turned into:
+
+instruction: ask model to explain for patient, assess cancer concern, propose next step
+
+input: actual radiology-style finding text
+
+output: chain-of-thought style reasoning + final recommendation
+
+Safety
+
+This model is not a doctor. It's a triage / education assistant.
+It should not replace radiologist, oncologist, or clinical decision-making.
+
+License
+
+LoRA weights are provided for research use.
+Source data includes CC BY 4.0 material from The Cancer Imaging Archive (TCIA) and academic datasets.

adapter_config.json

@@ -0,0 +1,42 @@
+{
+  "alpha_pattern": {},
+  "auto_mapping": null,
+  "base_model_name_or_path": "LLM360/K2-Think",
+  "bias": "none",
+  "corda_config": null,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 128,
+  "lora_bias": false,
+  "lora_dropout": 0.05,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "qalora_group_size": 16,
+  "r": 64,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "k_proj",
+    "v_proj",
+    "o_proj",
+    "down_proj",
+    "gate_proj",
+    "up_proj",
+    "q_proj"
+  ],
+  "target_parameters": null,
+  "task_type": "CAUSAL_LM",
+  "trainable_token_indices": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false
+}

adapter_model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:83bf7c8551ece9fcbbb4a348f3efc886c0717bfb9e18a18e88ea840a31c061d7
+size 2147605960

added_tokens.json

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+{
+  "</tool_call>": 151658,
+  "<tool_call>": 151657,
+  "<|box_end|>": 151649,
+  "<|box_start|>": 151648,
+  "<|endoftext|>": 151643,
+  "<|file_sep|>": 151664,
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+  "<|object_ref_start|>": 151646,
+  "<|quad_end|>": 151651,
+  "<|quad_start|>": 151650,
+  "<|repo_name|>": 151663,
+  "<|video_pad|>": 151656,
+  "<|vision_end|>": 151653,
+  "<|vision_pad|>": 151654,
+  "<|vision_start|>": 151652
+}

chat_template.jinja

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+{% set system_message = 'You are K2-Think, a helpful assistant trained by MBZUAI. To answer the user\'s question, you first think about the reasoning process and then provide the user with the answer. The reasoning process and answer are enclosed within <think> </think> and <answer> </answer> tags, respectively, i.e., <think> reasoning process here </think> <answer> answer here </answer>.' %}{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}{% set system_message = messages[0]['content'] %}{% else %}{% set loop_messages = messages %}{% endif %}{% if system_message is defined %}{{ '<|im_start|>system
+' + system_message + '<|im_end|>
+' }}{% endif %}{% for message in loop_messages %}{% set content = message['content'] %}{% if message['role'] == 'user' %}{{ '<|im_start|>user
+' + content + '<|im_end|>
+<|im_start|>assistant
+' }}{% elif message['role'] == 'assistant' %}{{ content + '<|im_end|>
+' }}{% endif %}{% endfor %}

special_tokens_map.json

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+{
+  "additional_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
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+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
+  "eos_token": {
+    "content": "<|im_end|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
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+  "pad_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

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+  "tokenizer_class": "Qwen2Tokenizer",
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+}