sambal

k2-v2-think-dpo-checkpoint-200

README

License: apache-2.0

Details

Base model: LLM360/K2-Think
Architecture: Qwen2ForCausalLM (32B class)
Training objective: sigmoid DPO (beta=0.1) with RPO (alpha=0.1)
Chat template: K2 v2 high, included as chat_template.jinja
Format: merged Transformers checkpoint, 14 BF16 safetensors shards
Training state: intermediate checkpoint at iteration 200/496

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python
from transformers import AutoModelForCausalLM, AutoTokenizer

repo_id = "sambal/k2-v2-think-dpo-checkpoint-200"
tokenizer = AutoTokenizer.from_pretrained(repo_id)
model = AutoModelForCausalLM.from_pretrained(
    repo_id,
    torch_dtype="auto",
    device_map="auto",
)

This is an intermediate preference-training checkpoint. Evaluate it on the intended downstream tasks before deployment.

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Model Details

Model Provider

sambal

Model Tree

Base

IFM/K2-Think

Fine-tuned

this model

Input Modalities

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Output Modalities

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Supported Functionality

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README

License: apache-2.0

Details

Base model: LLM360/K2-Think
Architecture: Qwen2ForCausalLM (32B class)
Training objective: sigmoid DPO (beta=0.1) with RPO (alpha=0.1)
Chat template: K2 v2 high, included as chat_template.jinja
Format: merged Transformers checkpoint, 14 BF16 safetensors shards
Training state: intermediate checkpoint at iteration 200/496

Loading

python
from transformers import AutoModelForCausalLM, AutoTokenizer

repo_id = "sambal/k2-v2-think-dpo-checkpoint-200"
tokenizer = AutoTokenizer.from_pretrained(repo_id)
model = AutoModelForCausalLM.from_pretrained(
    repo_id,
    torch_dtype="auto",
    device_map="auto",
)

This is an intermediate preference-training checkpoint. Evaluate it on the intended downstream tasks before deployment.