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@@ -6,6 +6,9 @@ base_model:
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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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  ---
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  # Model Card for DeepSeek-R1-Distill-Qwen-1.5B-4bit
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@@ -17,21 +20,11 @@ This is a 4-bit quantized version of the `deepseek-ai/DeepSeek-R1-Distill-Qwen-1
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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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-
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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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-
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- ## Uses
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  ### Direct Use
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@@ -41,7 +34,7 @@ This model is intended for research and practical applications where memory effi
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  - Language understanding tasks
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  - Chatbots and conversational AI
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- ### Downstream Use [optional]
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  This model can be fine-tuned for specific tasks such as:
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@@ -81,9 +74,9 @@ quantization_config = BitsAndBytesConfig(
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  )
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  # Load the model and tokenizer
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- tokenizer = AutoTokenizer.from_pretrained("your-username/DeepSeek-R1-Distill-Qwen-1.5B-4bit", trust_remote_code=True)
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  model = AutoModelForCausalLM.from_pretrained(
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- "your-username/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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  - 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