Phase-Technologies/qwen2.5-3b-claude-distilled-reasoning-dpo

Qwen2.5-3B-Claude-Distilled-Reasoning-DPO

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Overview

`qwen2.5-3b-claude-distilled-reasoning-dpo` is a post-trained, reasoning-specialized 3.0B parameter causal language model.

This model represents a two-stage post-training alignment pipeline built on top of Qwen/Qwen2.5-3B-Instruct:

1. 1.Supervised Fine-Tuning (SFT): Fine-tuned on high-quality internal reasoning monologue traces distilled from Claude 3.5 Sonnet, imparting deep step-by-step mathematical, logical, and code-synthesis reasoning behavior.
2. 2.Direct Preference Optimization (DPO): Aligned using DPOTrainer on preference pairs (argilla/ultrafeedback-binarized-preferences-cleaned). This step eliminates scientific hallucinations (e.g., density vs. thermal conductivity), suppresses infinite token repetition loops, and anchors physical explanations to first-principles facts.

Model Capabilities & Highlights

• —Distilled Chain-of-Thought (CoT): Thinks through mathematical equations, coding challenges, and logic puzzles step-by-step prior to executing answers.
• —Factually Grounded: High performance on graduate/research-level physics and mathematics questions, reducing hallucination tendencies present in basic SFT models.
• —ChatML Ready: Fully compatible with standard Qwen2.5 ChatML chat templates and system prompt instructions.
• —Low Memory Footprint: Runs comfortably in FP16/SDPA on a single consumer GPU (e.g., NVIDIA T4 / RTX 3060) requiring ~6GB VRAM.

Alignment & Evaluation Pipeline

Integrated Real-Time Streaming & Safe Inference Script

The code below provides a production-ready, bulletproof inference script. It includes:

• —Token-by-Token Streaming using TextIteratorStreamer.
• —Dynamic Temperature Scaling (lowers temperature for simple greetings to prevent creative rambling; elevates it for math/reasoning tasks).
• —System Prompt Injection to prevent tool/interface hallucinations.
• —Custom Stopping Criteria to cut off any potential ASCII symbol artifacts or trailing conversational chatter.

import os
import sys
from threading import Thread
import torch
import time
from transformers import (
    AutoTokenizer, 
    AutoModelForCausalLM, 
    TextIteratorStreamer,
    StoppingCriteria,
    StoppingCriteriaList
)

# =========================================================================
# 1. CONFIGURATION & MODEL LOADING
# =========================================================================
REPO_ID = "Phase-Technologies/qwen2.5-3b-claude-distilled-reasoning-dpo"

print(f"[*] Hardware Status: CUDA Available: {torch.cuda.is_available()}")

tokenizer = AutoTokenizer.from_pretrained(REPO_ID)
model = AutoModelForCausalLM.from_pretrained(
    REPO_ID,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="sdpa"
)

# =========================================================================
# 2. ENHANCED INFERENCE ENGINE
# =========================================================================
def analyze_inference(prompt):
    messages = [
        {"role": "system", "content": "You are a reasoning assistant. Solve the problem step-by-step and provide a final answer in a box."},
        {"role": "user", "content": prompt}
    ]
    formatted_prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
    inputs = tokenizer(formatted_prompt, return_tensors="pt").to(model.device)
    
    streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
    
    # Increased repetition penalty to 1.3 to stop 'Implication:' loops
    # Added stop_strings for common hallucination patterns
    gen_kwargs = dict(
        **inputs,
        streamer=streamer,
        max_new_tokens=400,
        do_sample=True,
        temperature=0.4,
        top_p=0.9,
        repetition_penalty=1.3,
        stop_strings=["Implication:", "<|im_end|>", "###"],
        tokenizer=tokenizer,
        pad_token_id=tokenizer.eos_token_id
    )

    print(f"\n--- TESTING IMPROVED PARAMETERS ---")
    start_time = time.time()
    thread = Thread(target=model.generate, kwargs=gen_kwargs)
    thread.start()
    
    generated_text = ""
    for new_text in streamer:
        print(new_text, end="", flush=True)
        generated_text += new_text
    
    duration = time.time() - start_time
    print(f"\n\n[Metric] Speed: {len(tokenizer.encode(generated_text))/duration:.2f} tokens/sec")

analyze_inference("Sally has 3 brothers. Each of her brothers has 2 sisters. How many sisters does Sally have?")

Technical Specifications

• —Architecture: Causal LM (Qwen2.5 architecture)
• —Parameters: ~3.09 Billion
• —Context Window: 32,768 tokens (Recommended max inference: 2,048 tokens)
• —Precision: bfloat16 / float16
• —License: Apache-2.0

Citation & Acknowledgments

• —Base Model: Alibaba Qwen Team (Qwen/Qwen2.5-3B-Instruct)
• —Preference Dataset: Argilla (argilla/ultrafeedback-binarized-preferences-cleaned)
• —Distillation Framework: Fine-tuned and post-trained using Hugging Face TRL (DPOTrainer), PEFT, and Transformers.