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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 +1,104 @@
+---
+license: apache-2.0
+base_model: Qwen/Qwen2.5-0.5B-Instruct
+tags:
+- text-classification
+- lora
+- peft
+- ifeval
+- commoneval
+- wildvoice
+- voicebench
+---
+
+# Qwen2.5-0.5B-Instruct Text Classification Model
+
+This model is a fine-tuned version of [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct) using LoRA (Low-Rank Adaptation) for text classification tasks.
+
+## Model Description
+
+The model has been trained to classify text into three categories:
+- **ifeval**: Instruction-following tasks with specific formatting requirements
+- **commoneval**: Factual questions and knowledge-based queries
+- **wildvoice**: Conversational, informal language patterns
+
+## Performance
+
+- **Overall Accuracy**: 93.33% (28/30 correct)
+- **ifeval**: 100% (10/10)
+- **commoneval**: 80% (8/10)
+- **wildvoice**: 100% (10/10)
+
+## Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# Load the model and tokenizer
+model = AutoModelForCausalLM.from_pretrained("your-username/qwen2.5-0.5b-text-classification")
+tokenizer = AutoTokenizer.from_pretrained("your-username/qwen2.5-0.5b-text-classification")
+
+# Example usage
+def classify_text(text):
+    prompt = f"Text: {text}\nLabel:"
+    inputs = tokenizer(prompt, return_tensors="pt")
+    
+    with torch.no_grad():
+        generated = model.generate(
+            **inputs,
+            max_new_tokens=15,
+            do_sample=True,
+            temperature=0.1,
+            top_p=0.9,
+            pad_token_id=tokenizer.eos_token_id,
+            eos_token_id=tokenizer.eos_token_id,
+        )
+    
+    response = tokenizer.decode(generated[0], skip_special_tokens=True)
+    return response[len(prompt):].strip()
+
+# Test examples
+print(classify_text("Follow these instructions exactly: Write 3 sentences about cats."))
+# Output: ifeval
+
+print(classify_text("What is the capital of France?"))
+# Output: commoneval
+
+print(classify_text("Hey, how are you doing today?"))
+# Output: wildvoice
+```
+
+## Training Details
+
+- **Base Model**: Qwen/Qwen2.5-0.5B-Instruct
+- **Method**: LoRA (Low-Rank Adaptation)
+- **Trainable Parameters**: 0.88% of total parameters
+- **Learning Rate**: 5e-4
+- **Batch Size**: 2
+- **Training Steps**: 150
+- **Max Length**: 128
+
+## Dataset
+
+The model was trained on synthetic data representing three text categories:
+- Instruction-following tasks (ifeval)
+- Factual questions (commoneval)
+- Conversational text (wildvoice)
+
+## Limitations
+
+- The model was trained on synthetic data and may not generalize well to all real-world text
+- Performance may vary on text that doesn't clearly fit the three defined categories
+- The model is optimized for the specific prompt format used during training
+
+## Citation
+
+```bibtex
+@misc{qwen2.5-0.5b-text-classification,
+  title={Qwen2.5-0.5B Text Classification Model},
+  author={Your Name},
+  year={2024},
+  publisher={Hugging Face},
+  howpublished={\url{https://huggingface.co/your-username/qwen2.5-0.5b-text-classification}}
+}
+```

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 %}

config.json

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+{
+  "architectures": [
+    "Qwen2ForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "dtype": "float16",
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+  "hidden_act": "silu",
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+  "intermediate_size": 4864,
+  "layer_types": [
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+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
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+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 32768,
+  "max_window_layers": 21,
+  "model_type": "qwen2",
+  "num_attention_heads": 14,
+  "num_hidden_layers": 24,
+  "num_key_value_heads": 2,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 1000000.0,
+  "sliding_window": null,
+  "tie_word_embeddings": true,
+  "transformers_version": "4.56.1",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

generation_config.json

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+  "top_p": 0.8,
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+}

model.safetensors

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special_tokens_map.json

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+    "lstrip": false,
+    "normalized": false,
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+  }
+}

tokenizer.json

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tokenizer_config.json

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