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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,164 @@
+---
+base_model: unsloth/qwen2.5-1.5b-instruct-unsloth-bnb-4bit
+library_name: peft
+pipeline_tag: text-generation
+license: apache-2.0
+language:
+- en
+tags:
+- failed-experiment
+- qlora
+- qwen2
+- life-mentor
+- philosophical-ai
+- lessons-learned
+- lora
+- sft
+- transformers
+- trl
+- unsloth
+---
+
+# ClarityMentor LoRA (Failed Experiment) ❌
+
+> **⚠️ STATUS: FAILED** - This model demonstrates what NOT to do when fine-tuning a life mentor LLM.
+
+## What This Is
+
+A QLoRA fine-tune of `Qwen/Qwen2.5-1.5B-Instruct` intended to be a philosophical life mentor called "ClarityMentor".
+
+## Why It Failed
+
+The model **only asks questions** regardless of user input. Even when asked "what is apple" or "hello", it responds with philosophical clarifying questions.
+
+### Root Causes:
+
+1. **Overweighted question-asking data**: The `conversation_starters` dataset (2,999 samples) was designed to train Socratic questioning behavior
+2. **Imbalanced training mix**: Too many datasets emphasized asking clarifying questions
+3. **System prompt mismatch**: Training data used a question-heavy system prompt, but we wanted direct answers
+4. **Overfitting to patterns**: Model memorized a narrow set of question templates instead of learning diverse responses
+
+### Evidence of Failure:
+
+```
+User: hello
+Model: What does success look like to you personally?
+
+User: what is apple
+Model: What makes certain decisions feel harder than others for you?
+
+User: give me python code
+Model: What specifically draws you to this question right now?
+
+User: this world shall burn to ashes
+Model: What does success look like to you personally?
+```
+
+The model ignores user input entirely and outputs memorized question templates.
+
+## Training Details
+
+| Parameter | Value |
+|-----------|-------|
+| Base Model | Qwen/Qwen2.5-1.5B-Instruct |
+| Method | QLoRA (4-bit NF4 quantization) |
+| Hardware | NVIDIA RTX 4050 Laptop GPU (6GB VRAM) |
+| Training Time | ~3 hours |
+| Training Samples | 27,362 |
+| Eval Samples | 1,440 |
+| Epochs | 2 |
+| Final Train Loss | 0.789 |
+| Final Eval Loss | 0.68 |
+
+### Dataset Mix (the problem):
+
+| Dataset | Samples | Issue |
+|---------|---------|-------|
+| Philosophy QA | 11,613 | OK - direct Q&A |
+| Counseling | ~10,000 | Question-heavy responses |
+| Reddit | ~10,000 | OK - varied |
+| Quotes | 4,995 | OK - wisdom-based |
+| **Conversation Starters** | **2,999** | **🔴 Main culprit** - trained ONLY to ask questions |
+
+### LoRA Config:
+
+```yaml
+r: 16
+lora_alpha: 32
+lora_dropout: 0
+target_modules:
+  - q_proj
+  - k_proj
+  - v_proj
+  - o_proj
+  - gate_proj
+  - up_proj
+  - down_proj
+max_seq_length: 512
+```
+
+### Training Args:
+
+```yaml
+per_device_train_batch_size: 1
+gradient_accumulation_steps: 16
+learning_rate: 2e-4
+lr_scheduler_type: cosine
+warmup_ratio: 0.03
+bf16: true
+optim: paged_adamw_8bit
+```
+
+## Lessons Learned
+
+1. **Audit your training data** - The conversation_starters dataset was specifically designed to output clarifying questions, which dominated the learned behavior
+2. **Balance response types** - Need diverse outputs (questions, answers, advice) not just one pattern
+3. **System prompt matters during training** - Should match intended inference behavior
+4. **Test incrementally** - Should have validated on a small sample before full training
+5. **Watch for overfitting patterns** - Low loss doesn't mean good model behavior
+
+## How to Fix (for future attempts)
+
+1. ❌ Remove or significantly reduce conversation_starters dataset
+2. ✅ Add more datasets with direct, substantive answers
+3. ✅ Include the "no questions" system prompt in training data
+4. ✅ Better balance between question-asking and answer-giving examples
+5. ✅ Add response diversity validation before training
+
+## Usage (not recommended, but here's how)
+
+```python
+from unsloth import FastLanguageModel
+
+model, tokenizer = FastLanguageModel.from_pretrained(
+    "YOUR_USERNAME/claritymentor-lora-failed",
+    max_seq_length=512,
+    load_in_4bit=True,
+)
+FastLanguageModel.for_inference(model)
+
+messages = [
+    {"role": "system", "content": "You are ClarityMentor, a philosophical mentor."},
+    {"role": "user", "content": "What is the meaning of life?"}
+]
+
+inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True).to("cuda")
+outputs = model.generate(inputs, max_new_tokens=256, temperature=0.7)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+# Output: Will ask you questions instead of answering...
+```
+
+## Framework Versions
+
+- PEFT: 0.18.1
+- Transformers: 4.57.3
+- Unsloth: 2026.1.3
+- PyTorch: 2.9.1+cu128
+
+## License
+
+Apache 2.0
+
+---
+
+*This model is shared as a learning resource. Sometimes failures teach more than successes.*

adapter_config.json

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+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": {
+    "base_model_class": "Qwen2ForCausalLM",
+    "parent_library": "transformers.models.qwen2.modeling_qwen2",
+    "unsloth_fixed": true
+  },
+  "base_model_name_or_path": "unsloth/qwen2.5-1.5b-instruct-unsloth-bnb-4bit",
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "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": 32,
+  "lora_bias": false,
+  "lora_dropout": 0,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "peft_version": "0.18.1",
+  "qalora_group_size": 16,
+  "r": 16,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "up_proj",
+    "k_proj",
+    "o_proj",
+    "down_proj",
+    "gate_proj",
+    "q_proj",
+    "v_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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added_tokens.json

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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 %}

special_tokens_map.json

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+}

tokenizer.json

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+size 11421994

tokenizer_config.json

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