Create README.md

README.md

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+---
+license: apache-2.0
+base_model: Qwen/Qwen2.5-1.5B-Instruct
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+  - qwen2
+  - quantized
+  - text-generation
+  - chat
+---
+
+# Qwen2.5-1.5B-Instruct-Quantized
+
+A quantized checkpoint created from **[Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct)**.
+
+This repository contains a quantized version of the original instruction-tuned Qwen2.5 1.5B model. The base model was developed by the Qwen team. This repository is a community quantization, not the original Qwen release. The base model is a causal language model designed for instruction following and conversational text generation. See the [official base model card](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) for its architecture and original documentation. citeturn953640search0
+
+## Model Details
+
+| Field | Details |
+|---|---|
+| Model name | Qwen2.5-1.5B-Instruct-Quantized |
+| Base model | [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) |
+| Model family | Qwen2.5 |
+| Model size | 1.5B-class model (base model: approximately 1.54B parameters) |
+| Task | Text generation, chat, instruction following |
+| Quantization | Quantized by the repository maintainer; method and bit-width not specified |
+| Maintainer | CodeDevX |
+
+## Intended Use
+
+This model may be used for:
+
+- Conversational assistance and instruction following
+- General text generation and question answering
+- Summarization, rewriting, and drafting
+- Experimentation with quantized language models and local inference, subject to runtime compatibility
+
+## Quick Start
+
+The exact loading method depends on the quantization format used for this checkpoint. Check the repository's **Files and versions** tab for the model file extension and configuration before choosing an inference runtime.
+
+### Transformers (for Transformers-compatible checkpoints)
+
+If the uploaded files are compatible with Transformers, you can try:
+
+```bash
+pip install -U transformers torch accelerate safetensors
+```
+
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+model_id = "CodeDevX/qwen2.5-1.5b-instruct-quantized"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype="auto",
+    device_map="auto",
+)
+
+messages = [
+    {"role": "user", "content": "Explain quantization in simple terms."}
+]
+
+prompt = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True,
+)
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+
+with torch.no_grad():
+    output = model.generate(
+        **inputs,
+        max_new_tokens=256,
+        do_sample=True,
+        temperature=0.7,
+        top_p=0.8,
+    )
+
+answer = tokenizer.decode(
+    output[0][inputs["input_ids"].shape[1]:],
+    skip_special_tokens=True,
+)
+print(answer)
+```
+
+**Important:** This example is for Transformers-compatible model files. It will not load a GGUF file directly. For GGUF, use a compatible runtime such as `llama.cpp` and follow the runtime's model-loading instructions.
+
+## Chat Template
+
+The original Qwen2.5-Instruct model uses a chat template. If the tokenizer is included and compatible, use `tokenizer.apply_chat_template()` to format messages.
+
+```python
+messages = [
+    {"role": "system", "content": "You are a helpful assistant."},
+    {"role": "user", "content": "Summarize the benefits of renewable energy."},
+]
+```
+
+## Quantization Information
+
+The checkpoint has been quantized from the base model listed above. The following technical details can be filled in to make the model card reproducible:
+
+- **Quantization method:** Not specified
+- **Bit-width / quantization type:** Not specified
+- **Quantization framework or tool:** Not specified
+- **Calibration dataset (if applicable):** Not specified
+- **Quantization settings:** Not specified
+- **Original and quantized file sizes:** Not specified
+- **Benchmark comparison with the original model:** Not provided
+
+Quantization can reduce model storage and memory requirements, but the effect on output quality, speed, and hardware compatibility depends on the quantization method and runtime.
+
+## Limitations
+
+- Responses may be inaccurate, incomplete, or biased.
+- Quantization may affect output quality and performance.
+- Hardware and runtime requirements depend on the checkpoint format and quantization method.
+- Do not rely on model outputs as the sole basis for high-stakes decisions.
+
+## Evaluation
+
+No benchmark or quality evaluation results are documented here. If available, add benchmark names, scores, hardware, inference settings, and a comparison against the original model.
+
+## License and Attribution
+
+The official base model is listed under the Apache-2.0 license. Review the [base model license and terms](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) and ensure this derived checkpoint includes any required license and attribution notices before redistribution or commercial use. citeturn953640search0
+
+## Credits
+
+- **Original model:** [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct)
+- **Quantized checkpoint:** [CodeDevX/qwen2.5-1.5b-instruct-quantized](https://huggingface.co/CodeDevX/qwen2.5-1.5b-instruct-quantized)
+
+---
+
+*Quantization and repository documentation by CodeDevX.*