Instructions to use ullahhafeez321/Qwen-NER-Decoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ullahhafeez321/Qwen-NER-Decoder with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="ullahhafeez321/Qwen-NER-Decoder")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("ullahhafeez321/Qwen-NER-Decoder", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ullahhafeez321/Qwen-NER-Decoder with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ullahhafeez321/Qwen-NER-Decoder"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ullahhafeez321/Qwen-NER-Decoder",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ullahhafeez321/Qwen-NER-Decoder

SGLang

How to use ullahhafeez321/Qwen-NER-Decoder with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "ullahhafeez321/Qwen-NER-Decoder" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ullahhafeez321/Qwen-NER-Decoder",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "ullahhafeez321/Qwen-NER-Decoder" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ullahhafeez321/Qwen-NER-Decoder",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ullahhafeez321/Qwen-NER-Decoder with Docker Model Runner:
```
docker model run hf.co/ullahhafeez321/Qwen-NER-Decoder
```

ullahhafeez321 commited on 6 days ago

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@@ -1,199 +1,217 @@
----
-library_name: transformers
-tags: []
----
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-- **Developed by:** [More Information Needed]
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-### Model Sources [optional]
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-## Uses
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-### Direct Use
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-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-## How to Get Started with the Model
-Use the code below to get started with the model.
-[More Information Needed]
-## Training Details
-### Training Data
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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+# English Named Entity Recognition (NER) – Qwen2.5-0.5B-Instruct + LoRA
+## Model Description
+This project is a fine-tuned **decoder-only Named Entity Recognition (NER) model** based on [`Qwen/Qwen2.5-0.5B-Instruct`](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct).
+The model is trained to identify and extract named entities from English text and return them in structured JSON format.
+It recognizes four standard CoNLL-2003 entity categories:
+- **PER** – Person
+- **ORG** – Organization
+- **LOC** – Location
+- **MISC** – Miscellaneous
+The model uses **Low-Rank Adaptation (LoRA)** for parameter-efficient fine-tuning.
+## Model Details
+| Property | Details |
+|---|---|
+| Model Name | English NER Decoder |
+| Base Model | Qwen/Qwen2.5-0.5B-Instruct |
+| Architecture | Decoder-only Transformer |
+| Fine-tuning Method | LoRA |
+| LoRA Rank | 8 |
+| LoRA Alpha | 16 |
+| LoRA Dropout | 0.05 |
+| Training Epochs | 1 |
+| Language | English |
+| Task | Named Entity Recognition |
+| Output Format | JSON |
+## Dataset
+The model was fine-tuned using the **CoNLL-2003 English Named Entity Recognition dataset**.
+Dataset: [`lhoestq/conll2003`](https://huggingface.co/datasets/lhoestq/conll2003)
+The dataset contains English sentences annotated with BIO-style named entity labels.
+The four entity categories are PER, ORG, LOC, and MISC.
+## Training Configuration
+The model was fine-tuned using LoRA with the following configuration:
+- Base model: Qwen2.5-0.5B-Instruct
+- LoRA rank: 8
+- LoRA alpha: 16
+- LoRA dropout: 0.05
+- Target modules: q_proj, k_proj, v_proj, o_proj
+- Training epochs: 1
+- Training samples: Up to 8,000
+- Validation samples: 1,000
+- Training precision: FP16
+- Optimizer: AdamW
+The training was performed using a Kaggle GPU environment.
+## Evaluation Results
+The model was evaluated on a quick test subset of 100 English sentences using exact entity-level matching.
+| Metric | Score |
+|---|---:|
+| Precision | 90.40% |
+| Recall | 81.63% |
+| F1-score | 85.79% |
+| True Positives | 160 |
+| False Positives | 17 |
+| False Negatives | 36 |
+**Note:** These results represent the 100-sample evaluation subset, not the complete test dataset. They should not be interpreted as a full-dataset benchmark.
+Additional manually selected English sentences were used to inspect entity extraction behavior.
+## Example
+**Input:**
+```text
+Elon Musk founded SpaceX.
+```
+**Output:**
+```json
+{
+  "entities": [
+    {
+      "text": "Elon Musk",
+      "type": "PER"
+    },
+    {
+      "text": "SpaceX",
+      "type": "ORG"
+    }
+  ]
+}
+```
+Another example:
+**Input:**
+```text
+Barack Obama visited Microsoft headquarters in Seattle.
+```
+**Output:**
+```json
+{
+  "entities": [
+    {
+      "text": "Microsoft",
+      "type": "ORG"
+    },
+    {
+      "text": "Seattle",
+      "type": "LOC"
+    }
+  ]
+}
+```
+## Intended Use
+This model is intended for:
+- English named entity extraction.
+- Natural Language Processing (NLP) applications.
+- Information extraction systems.
+- Text analysis and document processing.
+- Educational and research projects.
+- Integration with sentiment classification systems.
+## Limitations
+- The model may occasionally miss named entities.
+- Some entities may receive incorrect classifications.
+- Entity boundaries and spelling may occasionally be incorrect.
+- Performance can vary across different domains and writing styles.
+- The reported evaluation score is based on a limited 100-sample test subset.
+- The model is not intended to replace human verification in critical applications.
+## How to Use
+Install the required libraries:
+```bash
+pip install transformers peft accelerate torch
+```
+Load the base model and LoRA adapter:
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from peft import PeftModel
+BASE_MODEL = "Qwen/Qwen2.5-0.5B-Instruct"
+# Replace with your Hugging Face repository ID
+ADAPTER_MODEL = "YOUR_USERNAME/YOUR_MODEL_NAME"
+tokenizer = AutoTokenizer.from_pretrained(ADAPTER_MODEL)
+base_model = AutoModelForCausalLM.from_pretrained(
+    BASE_MODEL,
+    torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
+    device_map="auto"
+)
+model = PeftModel.from_pretrained(
+    base_model,
+    ADAPTER_MODEL
+)
+model.eval()
+```
+The adapter must be loaded together with its original base model.
+## Model Architecture
+This project uses a decoder-only Transformer architecture.
+The model generates structured entity extraction results from an English input sentence.
+LoRA fine-tuning adapts selected attention projection layers without updating all the original base-model parameters.
+## Future Improvements
+- Increase training data and training epochs.
+- Improve recall and entity coverage.
+- Evaluate performance on the complete test dataset.
+- Improve entity boundary detection.
+- Expand evaluation across different English text domains.
+- Integrate with an encoder-only BERT sentiment classification model.
+## Acknowledgements
+- [Qwen Team](https://huggingface.co/Qwen) for the Qwen2.5 base model.
+- [CoNLL-2003](https://huggingface.co/datasets/lhoestq/conll2003) dataset contributors.
+- [Hugging Face](https://huggingface.co/) for the Transformers, Datasets, and PEFT ecosystem.
+## License
+Please refer to the base model and dataset licenses before redistributing or using this fine-tuned adapter.
+---
+**Project:** English Named Entity Recognition using a Decoder-only Transformer with LoRA Fine-tuning.
+**Frameworks:** PyTorch, Transformers, PEFT, Hugging Face Datasets.