Q3.5-9B-GLM-5.1-DA

Key Highlights

• GLM-5.1 Reasoning Distillation: Fine-tuned using high-quality reasoning traces derived from GLM-5.1 datasets with a strong focus on mathematical and long-context reasoning.
• Distilled-Abliterated (DA): Applies refusal direction analysis and ablation-based strategies to reduce internal refusal behaviors while maintaining reasoning quality.
• Qwen3.5 Backbone: Built on top of Qwen/Qwen3.5-9B via prithivMLmods/Qwen3.5-9B-Unredacted-MAX for strong instruction-following and reasoning performance.
• Long-Context Mathematical Reasoning: Optimized for multi-step mathematical problem solving, logical decomposition, and extended reasoning chains.
• Instruction + Reasoning Fusion: Handles instruction-following and complex reasoning tasks seamlessly.
• Efficient 9B Deployment: Suitable for local inference and quantized deployment setups with lower hardware requirements compared to larger-scale models.

Datasets Used and Training Details

Quick Start with Transformers

bash
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pip install transformers==5.8.0
# or latest
pip install git+https://github.com/huggingface/transformers.git

python
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from transformers import Qwen3_5ForConditionalGeneration, AutoProcessor
import torch

model = Qwen3_5ForConditionalGeneration.from_pretrained(
    "prithivMLmods/Q3.5-9B-GLM-5.1-DA",
    torch_dtype="auto",
    device_map="auto"
)

processor = AutoProcessor.from_pretrained(
    "prithivMLmods/Q3.5-9B-GLM-5.1-DA"
)

messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "text",
                "text": "Solve this step-by-step: If a train travels 240 km in 3 hours, what is its average speed?"
            }
        ],
    }
]

text = processor.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)

inputs = processor(
    text=[text],
    padding=True,
    return_tensors="pt"
).to("cuda")

generated_ids = model.generate(
    **inputs,
    max_new_tokens=512
)

generated_ids_trimmed = [
    out_ids[len(in_ids):]
    for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]

output_text = processor.batch_decode(
    generated_ids_trimmed,
    skip_special_tokens=True,
    clean_up_tokenization_spaces=False
)

print(output_text)

Intended Use

• Mathematical Reasoning: Multi-step arithmetic, algebraic reasoning, and logical problem solving
• Long-Context Tasks: Extended reasoning chains and context-heavy instruction following
• Instruction Following: Hybrid prompts combining reasoning and structured responses
• Research on Abliteration: Studying the impact of refusal reduction techniques on reasoning preservation
• Alignment & Red-Teaming Research: Evaluating reduced-refusal systems under complex reasoning scenarios

Limitations & Risks

Important Note: This model intentionally minimizes built-in safety refusals.

• Sensitive Content Risk: May produce unrestricted or controversial outputs
• User Responsibility: Requires careful and ethical usage
• Mathematical Hallucinations: Complex reasoning tasks may still contain logical or numerical inconsistencies
• Abliteration Trade-offs: Reduced refusal behaviors may impact safety alignment and output filtering
• High Compute Demand: Optimized inference or quantization may still be required for efficient deployment