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valendra
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Model Details
Model Provider
valendra
Model Tree
Qwen/Qwen3.6-35B-A3B
Input Modalities
Output Modalities
Supported Functionality
qwen36-reasoning-effort-grpo-0.1-qlora-200-12k/checkpoint-200.q_proj, k_proj, v_proj, o_proj, shared_expert.gate_proj, shared_expert.up_proj, shared_expert.down_proj.merge_and_unload().The original adapter artifacts are preserved separately from this merged release. The published safetensors use the canonical Qwen 3.6 packed-MoE layout required by current inference runtimes.
The tokenizer includes three project-specific special tokens:
<|reasoning_effort_low|> (token ID 248077)<|reasoning_effort_medium|> (token ID 248078)<|reasoning_effort_high|> (token ID 248079)The published chat template accepts reasoning_effort="low", "medium", or "high" and emits the corresponding token before the thinking block. No xhigh token is provided.
Example:
python
from transformers import AutoModelForImageTextToText, AutoTokenizermodel_id = "valendra/sherry-35b-a3b-0.1-grpo-preview"tokenizer = AutoTokenizer.from_pretrained(model_id)model = AutoModelForImageTextToText.from_pretrained(model_id,device_map="auto",dtype="auto",)inputs = tokenizer.apply_chat_template([{"role": "user", "content": "Solve: 12 * 13"}],add_generation_prompt=True,enable_thinking=True,reasoning_effort="medium",return_tensors="pt",).to(model.device)outputs = model.generate(inputs, max_new_tokens=2048)print(tokenizer.decode(outputs[0, inputs.shape[-1]:], skip_special_tokens=False))
For non-thinking answers, call the template with enable_thinking=False. The model is multimodal-compatible through the Qwen3.6 architecture, but this release was trained and smoke-tested primarily through its text-generation path.
Research and experimentation with controllable reasoning effort. Validate outputs for your use case before deployment.