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 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.
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-### Model Sources [optional]
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-## Uses
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-### Direct Use
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-## Bias, Risks, and Limitations
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-### Recommendations
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-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.
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-## Training Details
-### Training Data
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-### Training Procedure
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-#### Training Hyperparameters
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-## Evaluation
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-### Testing Data, Factors & Metrics
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-#### Factors
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-### Results
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-#### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
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-## Environmental Impact
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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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-## Technical Specifications [optional]
-### Model Architecture and Objective
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-## Citation [optional]
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 ---
+language:
+  - en
+license: apache-2.0
+tags:
+  - text-generation
+  - instruction-tuning
+  - multi-task
+  - reasoning
+  - email
+  - summarization
+  - chat
+  - peft
+  - lora
+  - qwen
+  - deepseek
+base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
+datasets:
+  - HuggingFaceTB/smoltalk
+  - snoop2head/enron_aeslc_emails
+  - lucadiliello/STORIES
+  - abisee/cnn_dailymail
+  - wiki40b
+model_type: causal-lm
+inference: true
 library_name: transformers
+pipeline_tag: text-generation
 ---
+# 🧠 DeepSeek-Qwen-1.5B-Multitask-LoRA
+**Author:** [Your Name or HF Username]
+**License:** Apache-2.0
+**Base model:** [`deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B`](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B)
+**Adapter type:** LoRA (PEFT)
+**Capabilities:** Multi-task generalization & reasoning
+---
+## 🌟 Overview
+This model is a **LoRA-tuned variant of DeepSeek-R1-Distill-Qwen-1.5B**, trained on a **multi-task mixture** designed to teach the model to:
+- write **professional emails**
+- **continue stories** coherently
+- **hold conversations** and reason (from *SmolTalk*)
+- **summarize** long articles (CNN/DailyMail)
+- **answer technical questions**
+- **generate reports and structured text**
+It demonstrates **strong reasoning**, **clarity**, and **context retention** for small-scale compute deployment (4-bit quantization compatible).
+---
+## 🧩 Training Details
+| Parameter | Value |
+|------------|-------|
+| Base model | `deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B` |
+| Adapter | LoRA (r=8, alpha=32, dropout=0.1) |
+| Max sequence length | 1024 |
+| Learning rate | 3e-5 (cosine decay) |
+| Optimizer | `adamw_8bit` |
+| Grad Accumulation | 4 |
+| Precision | 4-bit quantized, FP16 compute |
+| Steps | 12k total (best @ ~8.2k) |
+| Training time | ~2.5h on A4000 |
+| Frameworks | 🤗 Transformers, PEFT, TRL, BitsAndBytes |
+---
+## 🧠 Reasoning Capability
+Thanks to integration of **SmolTalk** and diverse multi-task prompts, the model learns:
+- **Chain-of-thought style reasoning**
+- **Conversational grounding**
+- **Multi-step logical inferences**
+- **Instruction following** across domains
+Example:
+```text
+### Task: Explain reasoning
+### Input:
+If a train leaves City A at 3 PM and arrives at City B at 6 PM, covering 180 km, what is its average speed?
+### Output:
+The train travels 180 km in 3 hours.
+Average speed = 180 ÷ 3 = 60 km/h.