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Model: Phase-Technologies/qwen2.5-3b-claude-distilled-reasoning-dpo Source: Original Platform
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README.md
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README.md
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---
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language:
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- en
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license: apache-2.0
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base_model: Phase-Technologies/qwen2.5-3b-claude-distilled-reasoning
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tags:
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- dpo
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- rlhf
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- reasoning
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- chain-of-thought
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- distillation
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- claude
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- qwen
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- text-generation
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pipeline_tag: text-generation
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library_name: transformers
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datasets:
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- Phase-Technologies/claude-merged-traces
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- argilla/ultrafeedback-binarized-preferences-cleaned
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---
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# Qwen2.5-3B-Claude-Distilled-Reasoning-DPO
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<p align="center">
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<img src="https://raw.githubusercontent.com/QwenLM/Qwen/main/assets/qwen2.5_logo.png" width="30%" alt="Qwen2.5 Logo"/>
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</p>
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## Overview
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**`qwen2.5-3b-claude-distilled-reasoning-dpo`** is a post-trained, reasoning-specialized 3.0B parameter causal language model.
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This model represents a two-stage post-training alignment pipeline built on top of `Qwen/Qwen2.5-3B-Instruct`:
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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.
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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.
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---
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## Model Capabilities & Highlights
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* **Distilled Chain-of-Thought (CoT):** Thinks through mathematical equations, coding challenges, and logic puzzles step-by-step prior to executing answers.
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* **Factually Grounded:** High performance on graduate/research-level physics and mathematics questions, reducing hallucination tendencies present in basic SFT models.
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* **ChatML Ready:** Fully compatible with standard Qwen2.5 ChatML chat templates and system prompt instructions.
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* **Low Memory Footprint:** Runs comfortably in FP16/SDPA on a single consumer GPU (e.g., NVIDIA T4 / RTX 3060) requiring ~6GB VRAM.
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---
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## Alignment & Evaluation Pipeline
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| Feature | Base Model (`Qwen2.5-3B-Instruct`) | SFT Stage (`...-reasoning`) | DPO Stage (`...-reasoning-dpo`) |
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| :--- | :--- | :--- | :--- |
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| **Reasoning Engine** | Static response generation | Claude CoT monologue traces | **Refined CoT monologue traces** |
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| **Physics/Math Accuracy** | Standard textbook baseline | Prone to reasoning hallucinations | **First-principles verified** |
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| **Degeneracy / Loops** | Standard EOS handling | Prone to trailing follow-up loops | **Suppressed via preference rewards** |
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---
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## Integrated Real-Time Streaming & Safe Inference Script
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The code below provides a production-ready, bulletproof inference script. It includes:
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* **Token-by-Token Streaming** using `TextIteratorStreamer`.
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* **Dynamic Temperature Scaling** (lowers temperature for simple greetings to prevent creative rambling; elevates it for math/reasoning tasks).
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* **System Prompt Injection** to prevent tool/interface hallucinations.
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* **Custom Stopping Criteria** to cut off any potential ASCII symbol artifacts or trailing conversational chatter.
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```python
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import os
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import sys
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from threading import Thread
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import torch
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import time
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from transformers import (
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AutoTokenizer,
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AutoModelForCausalLM,
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TextIteratorStreamer,
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StoppingCriteria,
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StoppingCriteriaList
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)
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# =========================================================================
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# 1. CONFIGURATION & MODEL LOADING
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# =========================================================================
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REPO_ID = "Phase-Technologies/qwen2.5-3b-claude-distilled-reasoning-dpo"
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print(f"[*] Hardware Status: CUDA Available: {torch.cuda.is_available()}")
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tokenizer = AutoTokenizer.from_pretrained(REPO_ID)
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model = AutoModelForCausalLM.from_pretrained(
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REPO_ID,
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torch_dtype=torch.float16,
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device_map="auto",
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attn_implementation="sdpa"
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)
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# =========================================================================
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# 2. ENHANCED INFERENCE ENGINE
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# =========================================================================
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def analyze_inference(prompt):
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messages = [
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{"role": "system", "content": "You are a reasoning assistant. Solve the problem step-by-step and provide a final answer in a box."},
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{"role": "user", "content": prompt}
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]
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formatted_prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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inputs = tokenizer(formatted_prompt, return_tensors="pt").to(model.device)
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streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
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# Increased repetition penalty to 1.3 to stop 'Implication:' loops
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# Added stop_strings for common hallucination patterns
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gen_kwargs = dict(
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**inputs,
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streamer=streamer,
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max_new_tokens=400,
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do_sample=True,
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temperature=0.4,
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top_p=0.9,
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repetition_penalty=1.3,
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stop_strings=["Implication:", "<|im_end|>", "###"],
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tokenizer=tokenizer,
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pad_token_id=tokenizer.eos_token_id
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)
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print(f"\n--- TESTING IMPROVED PARAMETERS ---")
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start_time = time.time()
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thread = Thread(target=model.generate, kwargs=gen_kwargs)
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thread.start()
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generated_text = ""
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for new_text in streamer:
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print(new_text, end="", flush=True)
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generated_text += new_text
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duration = time.time() - start_time
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print(f"\n\n[Metric] Speed: {len(tokenizer.encode(generated_text))/duration:.2f} tokens/sec")
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analyze_inference("Sally has 3 brothers. Each of her brothers has 2 sisters. How many sisters does Sally have?")
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```
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---
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## Technical Specifications
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* **Architecture:** Causal LM (`Qwen2.5` architecture)
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* **Parameters:** ~3.09 Billion
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* **Context Window:** 32,768 tokens (Recommended max inference: 2,048 tokens)
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* **Precision:** `bfloat16` / `float16`
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* **License:** Apache-2.0
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---
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## Citation & Acknowledgments
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* **Base Model:** Alibaba Qwen Team (`Qwen/Qwen2.5-3B-Instruct`)
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* **Preference Dataset:** Argilla (`argilla/ultrafeedback-binarized-preferences-cleaned`)
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* **Distillation Framework:** Fine-tuned and post-trained using Hugging Face `TRL` (`DPOTrainer`), `PEFT`, and `Transformers`.
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54
chat_template.jinja
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{%- if tools %}
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{{- '<|im_start|>system\n' }}
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{%- if messages[0]['role'] == 'system' %}
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{{- messages[0]['content'] }}
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{%- else %}
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{{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
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{%- endif %}
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{{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
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{%- for tool in tools %}
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{{- "\n" }}
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{{- tool | tojson }}
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{%- endfor %}
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{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
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{%- else %}
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{%- if messages[0]['role'] == 'system' %}
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{{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
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{%- else %}
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{{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- for message in messages %}
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{%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
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{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
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{%- elif message.role == "assistant" %}
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{{- '<|im_start|>' + message.role }}
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{%- if message.content %}
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{{- '\n' + message.content }}
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{%- endif %}
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{%- for tool_call in message.tool_calls %}
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{%- if tool_call.function is defined %}
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{%- set tool_call = tool_call.function %}
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{%- endif %}
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{{- '\n<tool_call>\n{"name": "' }}
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{{- tool_call.name }}
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{{- '", "arguments": ' }}
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{{- tool_call.arguments | tojson }}
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{{- '}\n</tool_call>' }}
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{%- endfor %}
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{{- '<|im_end|>\n' }}
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{%- elif message.role == "tool" %}
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{%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
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{{- '<|im_start|>user' }}
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{%- endif %}
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{{- '\n<tool_response>\n' }}
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{{- message.content }}
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{{- '\n</tool_response>' }}
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{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
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{{- '<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- endfor %}
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{%- if add_generation_prompt %}
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{{- '<|im_start|>assistant\n' }}
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{%- endif %}
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config.json
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config.json
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{
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"architectures": [
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"Qwen2ForCausalLM"
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],
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"attention_dropout": 0.0,
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"bos_token_id": 151643,
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"dtype": "float16",
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"eos_token_id": 151645,
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"hidden_act": "silu",
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"hidden_size": 2048,
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"initializer_range": 0.02,
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"intermediate_size": 11008,
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"layer_types": [
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention"
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],
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"max_position_embeddings": 32768,
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"max_window_layers": 70,
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"model_type": "qwen2",
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"num_attention_heads": 16,
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"num_hidden_layers": 36,
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"num_key_value_heads": 2,
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"pad_token_id": null,
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"rms_norm_eps": 1e-06,
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"rope_parameters": {
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"rope_theta": 1000000.0,
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"rope_type": "default"
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},
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"sliding_window": null,
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"tie_word_embeddings": true,
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"transformers_version": "5.14.1",
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"use_cache": true,
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"use_sliding_window": false,
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"vocab_size": 151936
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}
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generation_config.json
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{
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"bos_token_id": 151643,
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"do_sample": true,
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"eos_token_id": [
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151645,
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151643
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],
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"pad_token_id": 151643,
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"repetition_penalty": 1.05,
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"temperature": 0.7,
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"top_k": 20,
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"top_p": 0.8,
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"transformers_version": "5.14.1"
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}
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3
model.safetensors
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:250076c6c14f57c153e91bb4cbf5702a38c16de38861cd41a452063b48563025
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size 6171926680
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3
tokenizer.json
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3
tokenizer.json
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version https://git-lfs.github.com/spec/v1
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oid sha256:3fd169731d2cbde95e10bf356d66d5997fd885dd8dbb6fb4684da3f23b2585d8
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size 11421892
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30
tokenizer_config.json
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{
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"add_prefix_space": false,
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"backend": "tokenizers",
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"bos_token": null,
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"clean_up_tokenization_spaces": false,
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"eos_token": "<|im_end|>",
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"errors": "replace",
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"extra_special_tokens": [
|
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"<|im_start|>",
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"<|im_end|>",
|
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"<|object_ref_start|>",
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"<|object_ref_end|>",
|
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"<|box_start|>",
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"<|box_end|>",
|
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"<|quad_start|>",
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"<|quad_end|>",
|
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"<|vision_start|>",
|
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"<|vision_end|>",
|
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"<|vision_pad|>",
|
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"<|image_pad|>",
|
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"<|video_pad|>"
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],
|
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"is_local": false,
|
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"local_files_only": false,
|
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"model_max_length": 131072,
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"pad_token": "<|im_end|>",
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"split_special_tokens": false,
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"tokenizer_class": "Qwen2Tokenizer",
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"unk_token": null
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}
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