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agurung/colar-qwen3-4b-ff-rl
colar-qwen3-4b-ff-rl is a reinforcement learning model from agurung. Use it for the reinforcement learning task on the model card, and read the license before you ship it in a product. The card lists the license as apache-2.0.
This repository stores CoLAR exports in a Hugging Face-compatible layout. The repo root works for standard Transformers loading, and extrastate.pt preserves the latent head for latent decoding.
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From the Hugging Face model README
This repository stores CoLAR exports in a Hugging Face-compatible layout. The repo root works for standard Transformers loading, and extra_state.pt preserves the latent head for latent decoding.
exact-epoch16-step9856-val_reward=0.6875qwen3_colar_rl_exact_epoch16_step9856| Tag | Local reference | Status |
|---|---|---|
exact-epoch16-step9856-val_reward=0.6875 | exact epoch16 export | current commit |
best-epoch16-step9856-val-reward-0.6562best-epoch24-val_reward=0.6719last-epoch28-val_reward=0.5781second-epoch08-val_reward=0.6406extra_state.pt for CoLAR latent decodingexport_meta.json from the local exportlatent_metadata.json with archival provenancefrom transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained('agurung/colar-qwen3-4b-ff-rl', revision='exact-epoch16-step9856-val_reward=0.6875', torch_dtype='auto', device_map='auto')
tokenizer = AutoTokenizer.from_pretrained('agurung/colar-qwen3-4b-ff-rl', revision='exact-epoch16-step9856-val_reward=0.6875')
For latent decoding, download the same revision and use extra_state.pt together with the repo root model files.