Upload 4-bit int quantized model via script

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README.md

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+---
+library_name: transformers
+tags:
+- llmcompressor
+- quantization
+- wint4
+---
+
+# Phi-4-mini-instruct-WINT4
+
+This model is a 4-bit quantized version of [microsoft/Phi-4-mini-instruct](https://huggingface.co/microsoft/Phi-4-mini-instruct) "using the [llmcompressor](https://github.com/neuralmagic/llmcompressor) library.
+
+## Quantization Details
+
+*   **Base Model:** [microsoft/Phi-4-mini-instruct](https://huggingface.co/microsoft/Phi-4-mini-instruct)
+*   **Quantization Library:** `llmcompressor`
+*   **Quantization Method:** Weight-only 4-bit int (WINT4)
+*   **Quantization Recipe:**
+```yaml
+quant_stage:
+      quant_modifiers:
+        QuantizationModifier:
+          ignore: [lm_head]
+          config_groups:
+            group_0:
+              weights: {num_bits: 4, type: int, symmetric: true, strategy: channel, dynamic: false}
+              targets: [Linear]
+```
+
+## Evaluation Results
+
+The following table shows the evaluation results on various benchmarks compared to the baseline (non-quantized) model.
+
+| Task             | Baseline Metric (10.0% Threshold) | Quantized Metric | Metric Type         |
+|------------------|-------------------------------------------------------|------------------|---------------------|
+| winogrande       | 0.7545                                              | 0.6985           | acc,none            |
+
+## How to Use
+
+You can load the quantized model and tokenizer using the `transformers` library:
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "NoorNizar/Phi-4-mini-instruct-WINT4"
+
+model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+
+# Example usage (replace with your specific task)
+prompt = "Hello, world!"
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+outputs = model.generate(**inputs, max_new_tokens=50)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+## Disclaimer
+
+This model was quantized automatically using a script. Performance and behavior might differ slightly from the original base model.

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config.json

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+  ],
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+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": false
+    },
+    "200025": {
+      "content": "<|tool_call|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": false
+    },
+    "200026": {
+      "content": "<|/tool_call|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": false
+    },
+    "200027": {
+      "content": "<|tool_response|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": false
+    },
+    "200028": {
+      "content": "<|tag|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<|endoftext|>",
+  "chat_template": "{% for message in messages %}{% if message['role'] == 'system' and 'tools' in message and message['tools'] is not none %}{{ '<|' + message['role'] + '|>' + message['content'] + '<|tool|>' + message['tools'] + '<|/tool|>' + '<|end|>' }}{% else %}{{ '<|' + message['role'] + '|>' + message['content'] + '<|end|>' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<|assistant|>' }}{% else %}{{ eos_token }}{% endif %}",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|endoftext|>",
+  "extra_special_tokens": {},
+  "model_max_length": 131072,
+  "pad_token": "<|endoftext|>",
+  "tokenizer_class": "GPT2Tokenizer",
+  "unk_token": "<|endoftext|>"
+}