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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 +1,158 @@
+---
+base_model: Qwen/Qwen2.5-7B-Instruct
+datasets:
+  - FinLang/investopedia-instruction-tuning-dataset
+language:
+  - en
+library_name: peft
+pipeline_tag: text-generation
+tags:
+  - finance
+  - lora
+  - qwen2.5
+  - instruction-tuning
+  - investopedia
+license: apache-2.0
+model-index:
+  - name: Qwen2.5-7B Financial LoRA
+    results:
+      - task:
+          type: text-generation
+          name: Text Generation
+        dataset:
+          name: investopedia-instruction-tuning-dataset
+          type: FinLang/investopedia-instruction-tuning-dataset
+          split: test
+        metrics:
+          - type: perplexity
+            value: 3.33
+            name: Perplexity
+          - type: bertscore
+            value: 0.8986
+            name: BERTScore F1
+---
+
+# Qwen2.5-7B Financial LoRA Adapter
+
+A LoRA adapter fine-tuned on top of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) for financial question answering and explanation tasks. Trained on the full [Investopedia instruction-tuning dataset](https://huggingface.co/datasets/FinLang/investopedia-instruction-tuning-dataset) (206K examples, 1 epoch).
+
+## Model Details
+
+- **Base model:** Qwen/Qwen2.5-7B-Instruct
+- **Fine-tuning method:** LoRA (PEFT)
+- **Training dataset:** FinLang/investopedia-instruction-tuning-dataset (206,000 examples)
+- **Task:** Financial Q&A and instruction following
+- **Language:** English
+- **License:** Apache 2.0
+
+### LoRA Configuration
+
+| Parameter | Value |
+|-----------|-------|
+| Rank (r) | 16 |
+| Alpha | 32 |
+| Dropout | 0 |
+| Target modules | q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj |
+| Bias | none |
+
+### Training Hyperparameters
+
+| Parameter | Value |
+|-----------|-------|
+| Learning rate | 2e-4 |
+| Batch size (per device) | 1 |
+| Gradient accumulation steps | 8 |
+| Effective batch size | 8 |
+| Epochs | 1 |
+| Optimizer | adamw_8bit |
+| LR scheduler | linear |
+| Precision | bf16 |
+| Quantization | 4-bit (NF4) |
+| Hardware | Kaggle T4 GPU (x1) |
+| Training time | ~24 hours |
+
+## Usage
+
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
+from peft import PeftModel
+
+base_model_id = "Qwen/Qwen2.5-7B-Instruct"
+adapter_id = "xczou/qwen2.5-7b-financial-lora"
+
+bnb_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_compute_dtype=torch.float16,
+    bnb_4bit_use_double_quant=True,
+    bnb_4bit_quant_type="nf4",
+)
+
+tokenizer = AutoTokenizer.from_pretrained(base_model_id, trust_remote_code=True)
+base_model = AutoModelForCausalLM.from_pretrained(
+    base_model_id,
+    quantization_config=bnb_config,
+    device_map="auto",
+    trust_remote_code=True,
+)
+
+model = PeftModel.from_pretrained(base_model, adapter_id)
+model.eval()
+
+def ask(question, max_new_tokens=300):
+    prompt = (
+        f"<|im_start|>system\nYou are a financial expert.<|im_end|>\n"
+        f"<|im_start|>user\n{question}<|im_end|>\n"
+        f"<|im_start|>assistant\n"
+    )
+    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+    with torch.no_grad():
+        outputs = model.generate(
+            **inputs,
+            max_new_tokens=max_new_tokens,
+            temperature=0.7,
+            top_p=0.9,
+            do_sample=True,
+            pad_token_id=tokenizer.eos_token_id,
+        )
+    return tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)
+
+print(ask("What is the difference between a Roth IRA and a Traditional IRA?"))
+```
+
+## Evaluation Results
+
+Evaluated on the test split of `FinLang/investopedia-instruction-tuning-dataset` (100 held-out examples, never seen during training).
+
+### Perplexity (lower is better)
+
+| Model | Perplexity |
+|-------|-----------|
+| Qwen2.5-7B-Instruct (base) | 84.80 |
+| + Financial LoRA adapter | 3.33 |
+| **Improvement** | **96.1%** |
+
+### BERTScore F1 (higher is better)
+
+| Model | BERTScore F1 |
+|-------|-------------|
+| Qwen2.5-7B-Instruct (base) | 0.8373 |
+| + Financial LoRA adapter | 0.8986 |
+| **Improvement** | **7.3%** |
+
+The large perplexity improvement reflects strong domain language adaptation. The BERTScore improvement reflects that generated answers are semantically closer to expert Investopedia reference answers. The base Qwen2.5-7B-Instruct model already has a high BERTScore baseline (0.8373) due to its broad pre-training, so the 7.3% gain represents meaningful domain specialization on top of an already capable model.
+
+## Scripts
+
+- **Training script:** [qwenadaptertraining on Kaggle](https://www.kaggle.com/code/xczouxiaocheng/qwenadaptertraining)
+- **Evaluation script:** [evaluate-qwen-financial-adapter on Kaggle](https://www.kaggle.com/code/xczouxiaocheng/evaluate-qwen-financial-adapter)
+
+## Training Data
+
+Trained on [FinLang/investopedia-instruction-tuning-dataset](https://huggingface.co/datasets/FinLang/investopedia-instruction-tuning-dataset), a dataset of 206,000 financial Q&A pairs sourced from Investopedia, covering topics including stocks, bonds, ETFs, retirement accounts, derivatives, personal finance, and macroeconomics.
+
+## Limitations
+
+- Responses reflect Investopedia's editorial style and may not cover all financial topics equally
+- Not suitable for real-time financial data or market predictions
+- Should not be used as a substitute for professional financial advice

adapter_config.json

@@ -0,0 +1,50 @@
+{
+  "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/qwen2.5-7b-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": [
+    "q_proj",
+    "k_proj",
+    "o_proj",
+    "down_proj",
+    "gate_proj",
+    "up_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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+version https://git-lfs.github.com/spec/v1
+oid sha256:16cea635bcea002bb89dc4ff9af218c04e0c8a78bdf003e202c1cfdce18e7a40
+size 161533192

chat_template.jinja

@@ -0,0 +1,54 @@
+{%- 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 %}

dataset-metadata.json

@@ -0,0 +1,9 @@
+{
+  "title": "Financial Qwen Adapter",
+  "id": "xczouxiaocheng/financial-qwen-adapter",
+  "licenses": [
+    {
+      "name": "CC0-1.0"
+    }
+  ]
+}

tokenizer.json

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+version https://git-lfs.github.com/spec/v1
+oid sha256:bd5948af71b4f56cf697f7580814c7ce8b80595ef985544efcacf716126a2e31
+size 11422356

tokenizer_config.json

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+{
+  "add_prefix_space": false,
+  "backend": "tokenizers",
+  "bos_token": null,
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "is_local": false,
+  "model_max_length": 32768,
+  "pad_token": "<|PAD_TOKEN|>",
+  "padding_side": "left",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null,
+  "added_tokens_decoder": {
+    "151643": {
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+}