prithivMLmods

Q3.5-4B-OpusGLM-MAX-0731-ablated

README

License: apache-2.0

Key Highlights

Qwen 3.5 Foundation: Built directly on top of Qwen/Qwen3.5-4B.
Multi-Stage Training: Trained through multiple stages to progressively improve reasoning performance.
General Purpose Reasoning: Trained using General Purpose GLM and Opus reasoning traces spanning mathematics, science, coding, and analytical tasks.
General Reasoning Enhancement: Further trained on additional high-quality reasoning traces to strengthen instruction following and multi-step reasoning.
Research-Focused Release: Designed for reasoning research, experimentation, and evaluation.
Efficient 4B Deployment: Suitable for local inference and research environments.

Quick Start with Transformers

bash
pip install transformers
pip install accelerate

python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model = AutoModelForCausalLM.from_pretrained(
    "prithivMLmods/Q3.5-4B-OpusGLM-MAX-0731-ablated",
    torch_dtype="auto",
    device_map="auto"
)

tokenizer = AutoTokenizer.from_pretrained(
    "prithivMLmods/Q3.5-4B-OpusGLM-MAX-0731-ablated"
)

messages = [
    {
        "role": "user",
        "content": "Explain how a transformer model processes text."
    }
]

inputs = tokenizer.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_tensors="pt"
).to(model.device)

outputs = model.generate(
    inputs,
    max_new_tokens=512
)

print(
    tokenizer.decode(
        outputs[0][inputs.shape[-1]:],
        skip_special_tokens=True
    )
)

Training Details

Table with columns: Setting, Value
Setting	Value
Base Model	Qwen/Qwen3.5-4B
Training Method	Multi-stage Supervised Fine-Tuning (SFT)
Maximum Sequence Length	32,768 tokens (Long Context)
Training Precision	BF16 (Full Precision)
Training & Alignment Framework	TRL
Training Data	General Purpose GLM reasoning traces, Opus reasoning traces, and additional high-quality reasoning datasets

Model Files

Table with columns: Resource, Link
Resource	Link
Transformers Model	`prithivMLmods/Q3.5-4B-OpusGLM-MAX-0731-ablated`
GGUF (llama.cpp Quantizations)	`prithivMLmods/Q3.5-4B-OpusGLM-MAX-0731-ablated-GGUF`

Intended Use

Reasoning Research: Studying long-context reasoning and multi-stage training techniques.
Mathematical Reasoning: Solving complex mathematical problems with multi-step reasoning.
Scientific Reasoning: Performing structured scientific analysis and problem solving.
Coding Assistance: Assisting with code generation, debugging, and technical reasoning.
Instruction Following: Evaluating and improving instruction-following capabilities.
Local Deployment: Running efficient 4B reasoning models in research and experimentation environments.

Limitations

Experimental Model: Behavior may differ from the base model in certain scenarios.
Reasoning Artifacts: Complex reasoning chains may occasionally produce incorrect intermediate steps or conclusions.
Training Biases: Performance reflects the characteristics and coverage of the reasoning data used during training.

Acknowledgements

Qwen/Qwen3.5-4B: Base model used for this project.
TRL - Transformers Reinforcement Learning: TRL is a full stack library providing tools to train transformer language models with methods including Supervised Fine-Tuning (SFT), Group Relative Policy Optimization (GRPO), Direct Preference Optimization (DPO), Reward Modeling, and more.
Transformers: Transformers provides state-of-the-art machine learning models for text, computer vision, audio, video, and multimodal tasks, supporting both inference and training.

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Model Details

Model Provider

prithivMLmods

Model Tree

Base

Qwen/Qwen3.5-4B

Fine-tuned

this model

Input Modalities

TextImageVideo

Output Modalities

Text

Supported Functionality