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agurung/
coconut-qwen2.5-7b

Name: coconut-qwen2.5-7b
Availability: InStock

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qwen2

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Coconut-Enhanced Qwen2.5-7B-Instruct

This model was trained using the Coconut method for continuous latent space reasoning.

Base Model

Base: Qwen/Qwen2.5-7B-Instruct
Method: Coconut (Continuous Latent Space Reasoning)
Training: Custom reasoning dataset with spacy-segmented reasoning steps

Usage

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("agurung/coconut-qwen2.5-7b")
tokenizer = AutoTokenizer.from_pretrained("agurung/coconut-qwen2.5-7b")

# Use like any other Qwen model
inputs = tokenizer("Your question here", return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Training Details

Dataset: Reasoning traces with "In summary:" endings
Method: Progressive latent token replacement during training
Latent Tokens: 2 per reasoning step (c_thought)
Max Reasoning Stages: 2 (max_latent_stage)

Extracted from checkpoint: checkpoint_2