prithivMLmods
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QwQ-R1-Distill-7B-CoT

Text Generation

text-generation-inference

Inference Endpoints

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prithivMLmods commited on 12 days ago

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@@ -65,4 +65,5 @@ response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
 4. **Performance on Non-Reasoning Tasks:** The model is optimized for chain-of-thought reasoning and may underperform on tasks that require simpler, less structured responses.
 5. **Resource-Intensive:** Running the model efficiently requires significant computational resources, which may limit accessibility for smaller-scale deployments.
 6. **Dependence on Input Quality:** The model’s performance heavily depends on the clarity and quality of the input provided. Ambiguous or poorly structured prompts may yield suboptimal results.
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 4. **Performance on Non-Reasoning Tasks:** The model is optimized for chain-of-thought reasoning and may underperform on tasks that require simpler, less structured responses.
 5. **Resource-Intensive:** Running the model efficiently requires significant computational resources, which may limit accessibility for smaller-scale deployments.
 6. **Dependence on Input Quality:** The model’s performance heavily depends on the clarity and quality of the input provided. Ambiguous or poorly structured prompts may yield suboptimal results.