emredeveloper
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README.md
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- deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
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tags:
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---
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# Model Card for DeepSeek-R1-Distill-Qwen-1.5B-4bit
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### Model Description
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- **Developed by:** [Your Name or Organization]
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- **Funded by [optional]:** [Your Funding Source, if applicable]
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- **Shared by:** [Your Name or Organization]
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- **Model type:** Transformer-based Language Model
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- **Language(s) (NLP):** English
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- **License:** MIT
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- **Finetuned from model:** `deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B`
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### Model Sources [optional]
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- **Repository:** [Link to your GitHub repository, if applicable]
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- **Paper [optional]:** [Link to the paper, if applicable]
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- **Demo [optional]:** [Link to a live demo, if applicable]
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## Uses
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### Direct Use
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- Language understanding tasks
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- Chatbots and conversational AI
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### Downstream Use
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This model can be fine-tuned for specific tasks such as:
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# Load the model and tokenizer
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tokenizer = AutoTokenizer.from_pretrained("
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model = AutoModelForCausalLM.from_pretrained(
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quantization_config=quantization_config,
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device_map="auto",
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trust_remote_code=True
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- deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
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tags:
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- cot
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- r1
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- deepseek
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- text
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---
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# Model Card for DeepSeek-R1-Distill-Qwen-1.5B-4bit
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### Model Description
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- **Model type:** Transformer-based Language Model
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- **Language(s) (NLP):** English
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- **License:** MIT
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- **Finetuned from model:** `deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B`
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### Direct Use
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- Language understanding tasks
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- Chatbots and conversational AI
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### Downstream Use
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This model can be fine-tuned for specific tasks such as:
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)
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# Load the model and tokenizer
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tokenizer = AutoTokenizer.from_pretrained("emredeveloper/DeepSeek-R1-Distill-Qwen-1.5B-4bit", trust_remote_code=True)
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model = AutoModelForCausalLM.from_pretrained(
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"emredeveloper/DeepSeek-R1-Distill-Qwen-1.5B-4bit",
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quantization_config=quantization_config,
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device_map="auto",
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trust_remote_code=True
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