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--- |
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language: |
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- en |
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license: apache-2.0 |
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tags: |
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- text-generation |
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- instruction-tuning |
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- multi-task |
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- reasoning |
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- email |
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- summarization |
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- chat |
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- peft |
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- lora |
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- qwen |
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- deepseek |
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base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B |
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datasets: |
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- HuggingFaceTB/smoltalk |
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- snoop2head/enron_aeslc_emails |
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- lucadiliello/STORIES |
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- abisee/cnn_dailymail |
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- wiki40b |
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model_type: causal-lm |
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inference: true |
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library_name: peft |
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pipeline_tag: text-generation |
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--- |
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# 🧠 Deepseek-R1-multitask-lora |
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**Author:** Gilbert Akham |
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**License:** Apache-2.0 |
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**Base model:** [`deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B`](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B) |
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**Adapter type:** LoRA (PEFT) |
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**Capabilities:** Multi-task generalization & reasoning |
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--- |
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# 🚀 What It Can Do |
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This multitask fine-tuned model handles a broad set of natural language and reasoning-based tasks, such as: |
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✉️ Email & message writing — generate clear, friendly, or professional communications. |
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📖 Story & creative writing — craft imaginative narratives, poems, and dialogues. |
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💬 Conversational chat — maintain coherent, context-aware conversations. |
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💡 Explanations & tutoring — explain technical or abstract topics simply. |
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🧩 Reasoning & logic tasks — provide step-by-step answers for analytical questions. |
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💻 Code generation & explanation — write and explain Python or general programming code. |
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🌍 Translation & summarization — translate between multiple languages or condense information. |
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The model’s multi-domain training (based on datasets like SmolTalk, Everyday Conversations, and reasoning-rich samples) makes it suitable for assistants, chatbots, content generators, or educational tools. |
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--- |
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## 🧩 Training Details |
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| Parameter | Value | |
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|------------|-------| |
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| Base model | `deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B` | |
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| Adapter | LoRA (r=8, alpha=32, dropout=0.1) | |
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| Max sequence length | 1024 | |
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| Learning rate | 3e-5 (cosine decay) | |
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| Optimizer | `adamw_8bit` | |
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| Grad Accumulation | 4 | |
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| Precision | 4-bit quantized, FP16 compute | |
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| Steps | 12k total (best @ ~8.2k) | |
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| Training time | ~2.5h on A4000 | |
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| Frameworks | 🤗 Transformers, PEFT, TRL, BitsAndBytes | |
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--- |
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## 🧠 Reasoning Capability |
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Thanks to integration of **SmolTalk** and diverse multi-task prompts, the model learns: |
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- **Chain-of-thought style reasoning** |
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- **Conversational grounding** |
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- **Multi-step logical inferences** |
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- **Instruction following** across domains |
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Example: |
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```text |
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### Task: Explain reasoning |
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### Input: |
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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? |
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### Output: |
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The train travels 180 km in 3 hours. |
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Average speed = 180 ÷ 3 = 60 km/h. |
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