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Model: RinKana/Qwen2.5-3B-Deconstruct-V2.4-Merged-v2
Source: Original Platform

.gitattributes

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README.md

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+---
+base_model: unsloth/qwen2.5-3b-instruct-bnb-4bit
+tags:
+- text-generation-inference
+- transformers
+- unsloth
+- qwen2
+license: apache-2.0
+language:
+- en
+datasets:
+- RinKana/tetralemma-reasoning-dataset-v4
+---
+# Example Inference Code
+
+```python
+# --- INFERENCE TEST (Qwen 3B - Tetralemma V2) ---
+from unsloth import FastLanguageModel
+
+
+FastLanguageModel.for_inference(model)
+
+
+test_question = "You're Hedge fund manager, with AUM of $1000000000. how would you structure your financial portofolio?"
+
+
+v2_system_prompt = """You are a Deconstructionist Analyst.
+User Question: {}
+Deconstruction Analysis:"""
+
+prompt = v2_system_prompt.format(test_question)
+
+inputs = tokenizer([prompt], return_tensors = "pt").to("cuda")
+
+# Generate
+outputs = model.generate(
+    **inputs, 
+    max_new_tokens = 4096, 
+    use_cache = True,
+    temperature = 0.7, 
+    top_p = 0.9,
+)
+
+# Decode Result
+print("\n--- Qwen Thinking (Qwen 2.5-3B) ---")
+# Strip prompt for cleaness
+print(tokenizer.decode(outputs[0]).split("Deconstruction Analysis:")[-1].replace("<|endoftext|>", ""))
+```
+# Example Output:
+```
+--- Qwen Thinking (Qwen 2.5-3B) ---
+ <reason>
+Diversify across: Equities (Equity ETFs/PE Funds) 20%, Fixed Income (Bonds/Yields) 30%, Cash/Currency 10%, Private Equity/Liquidity Notes 20%, Real Estate 10%. Hedge Funds/Options are 'Unconventional' and risky; keep small.
+</reason>
+<exception>
+The 'Risk-Free Rate' is not zero. Bond prices are falling due to rising interest rates. Cash sits in negative interest. Private equity is expensive and illiquid. The 60-40 portfolio (Stocks vs Bonds) is obsolete in a floating rate world. Unconventional assets like Options/Crypto might offer the needed returns to outperform a boring vanilla portfolio.
+</exception>
+<tension>
+The tension is between *Traditional Diversification* (ETFs/Bonds) and *High-Return Asset Classes* (PE/Private).
+</tension>
+<categorization>
+Keep *Core* in *Assets That Pay* (Bonds/Cash). Invest *Aggressively* in *Liquidity* (PE/Private equity). Don't touch the 'Option Pricing' unless you have the math creds.
+</categorization>
+<deconstruction>
+The goal is 'Net Alpha.' Traditional pools are full. To get alpha, you must either find a hidden risk premium (Value/Equity) or find a productivity advantage (Talent/Process). The complex structures are often just spreadsheets trying to be hedge funds.
+</deconstruction>
+<conclusion>
+A modern hedge fund should prioritize liquid alternative assets like private equity and real estate over traditional bonds/cash, reallocating capital based on the evolving risk-free rate environment.
+</conclusion><|im_end|>
+```
+# Example Training
+```python
+from trl import SFTTrainer
+from transformers import TrainingArguments
+from unsloth import FastLanguageModel, is_bfloat16_supported
+from datasets import load_dataset
+import torch
+
+# --- 1. CONFIGURATION ---
+model_name = "RinKana/Qwen2.5-3B-Deconstruct-V2.4-Merged-v2"
+
+
+max_seq_length = 4096
+dtype = None 
+load_in_4bit = True 
+
+# --- 2. LOAD MODEL ---
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name = model_name,
+    max_seq_length = max_seq_length,
+    dtype = dtype,
+    load_in_4bit = load_in_4bit,
+)
+
+# LoRA config
+model = FastLanguageModel.get_peft_model(
+    model,
+    r = 16,
+    target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
+                      "gate_proj", "up_proj", "down_proj"],
+    lora_alpha = 16,
+    lora_dropout = 0,
+    bias = "none",
+    use_gradient_checkpointing = "unsloth",
+    random_state = 3407,
+)
+
+# --- 3. FORMATTING FUNCTION (V2 - DECONSTRUCTIONIST) ---
+v2_system_prompt = """You are a Deconstructionist Analyst.
+User Question: {}
+Deconstruction Analysis: {}"""
+
+EOS_TOKEN = tokenizer.eos_token
+
+def formatting_prompts_func(examples):
+    questions = examples["Question"]
+    reasonings = examples["Reasoning"] 
+    texts = []
+    for question, reasoning in zip(questions, reasonings):
+        text = v2_system_prompt.format(question, reasoning) + EOS_TOKEN
+        texts.append(text)
+    return { "text" : texts, }
+
+# Load Dataset V3 - 219 dataset
+dataset_file = "RinKana/tetralemma-reasoning-dataset-v4" 
+dataset = load_dataset(dataset_file, split="train")
+dataset = dataset.map(formatting_prompts_func, batched = True)
+
+# --- 4. TRAINING ---
+trainer = SFTTrainer(
+    model = model,
+    tokenizer = tokenizer,
+    train_dataset = dataset,
+    dataset_text_field = "text",
+    max_seq_length = max_seq_length,
+    dataset_num_proc = 2,
+    packing = False,
+    
+    args = TrainingArguments(
+        per_device_train_batch_size = 4,
+        gradient_accumulation_steps = 2,
+        warmup_steps = 5,
+        num_train_epochs = 10, 
+        learning_rate = 2e-4,
+        fp16 = not is_bfloat16_supported(),
+        bf16 = is_bfloat16_supported(),
+        logging_steps = 1,
+        optim = "adamw_8bit",
+        weight_decay = 0.01,
+        lr_scheduler_type = "linear",
+        seed = 3407,
+        output_dir = "outputs",
+        report_to = "wandb",
+        disable_tqdm = False,
+    ),
+)
+
+# --- 5. START TRAINING ---
+print(f"🚀 Starting Eksperimen V2 on {model_name}...")
+trainer_stats = trainer.train()
+
+print("✅ Training Done!!!")
+```
+# Uploaded finetuned  model
+
+- **Developed by:** RinKana
+- **License:** apache-2.0
+- **Finetuned from model :** unsloth/qwen2.5-3b-instruct-bnb-4bit
+
+This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
+
+[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

added_tokens.json

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+{
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+  "<tool_call>": 151657,
+  "<|box_end|>": 151649,
+  "<|box_start|>": 151648,
+  "<|endoftext|>": 151643,
+  "<|file_sep|>": 151664,
+  "<|fim_middle|>": 151660,
+  "<|fim_pad|>": 151662,
+  "<|fim_prefix|>": 151659,
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+  "<|image_pad|>": 151655,
+  "<|object_ref_end|>": 151647,
+  "<|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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+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- messages[0]['content'] }}
+    {%- else %}
+        {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
+    {%- endif %}
+    {{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
+    {%- else %}
+        {{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- for message in messages %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {{- '<|im_start|>' + message.role }}
+        {%- if message.content %}
+            {{- '\n' + message.content }}
+        {%- endif %}
+        {%- for tool_call in message.tool_calls %}
+            {%- if tool_call.function is defined %}
+                {%- set tool_call = tool_call.function %}
+            {%- endif %}
+            {{- '\n<tool_call>\n{"name": "' }}
+            {{- tool_call.name }}
+            {{- '", "arguments": ' }}
+            {{- tool_call.arguments | tojson }}
+            {{- '}\n</tool_call>' }}
+        {%- endfor %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- message.content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+{%- endif %}

config.json

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+{
+    "architectures": [
+        "Qwen2ForCausalLM"
+    ],
+    "attention_dropout": 0.0,
+    "torch_dtype": "float16",
+    "eos_token_id": 151645,
+    "hidden_act": "silu",
+    "hidden_size": 2048,
+    "initializer_range": 0.02,
+    "intermediate_size": 11008,
+    "layer_types": [
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention"
+    ],
+    "max_position_embeddings": 32768,
+    "max_window_layers": 70,
+    "model_type": "qwen2",
+    "num_attention_heads": 16,
+    "num_hidden_layers": 36,
+    "num_key_value_heads": 2,
+    "pad_token_id": 151654,
+    "rms_norm_eps": 1e-06,
+    "rope_scaling": null,
+    "rope_theta": 1000000.0,
+    "sliding_window": null,
+    "tie_word_embeddings": true,
+    "transformers_version": "4.56.2",
+    "unsloth_fixed": true,
+    "unsloth_version": "2025.12.9",
+    "use_cache": true,
+    "use_sliding_window": false,
+    "vocab_size": 151936
+}

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+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null,
+  "chat_template": "{%- if tools %}\n    {{- '<|im_start|>system\\n' }}\n    {%- if messages[0]['role'] == 'system' %}\n        {{- messages[0]['content'] }}\n    {%- else %}\n        {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}\n    {%- endif %}\n    {{- \"\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n    {%- for tool in tools %}\n        {{- \"\\n\" }}\n        {{- tool | tojson }}\n    {%- endfor %}\n    {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n    {%- if messages[0]['role'] == 'system' %}\n        {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n    {%- else %}\n        {{- '<|im_start|>system\\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\\n' }}\n    {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n    {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n        {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n    {%- elif message.role == \"assistant\" %}\n        {{- '<|im_start|>' + message.role }}\n        {%- if message.content %}\n            {{- '\\n' + message.content }}\n        {%- endif %}\n        {%- for tool_call in message.tool_calls %}\n            {%- if tool_call.function is defined %}\n                {%- set tool_call = tool_call.function %}\n            {%- endif %}\n            {{- '\\n<tool_call>\\n{\"name\": \"' }}\n            {{- tool_call.name }}\n            {{- '\", \"arguments\": ' }}\n            {{- tool_call.arguments | tojson }}\n            {{- '}\\n</tool_call>' }}\n        {%- endfor %}\n        {{- '<|im_end|>\\n' }}\n    {%- elif message.role == \"tool\" %}\n        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n            {{- '<|im_start|>user' }}\n        {%- endif %}\n        {{- '\\n<tool_response>\\n' }}\n        {{- message.content }}\n        {{- '\\n</tool_response>' }}\n        {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n            {{- '<|im_end|>\\n' }}\n        {%- endif %}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n"
+}