108 lines
4.1 KiB
Markdown
108 lines
4.1 KiB
Markdown
---
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base_model:
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- Qwen/Qwen2.5-7B
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- Qwen/Qwen2.5-7B-Instruct
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- Qwen/Qwen2.5-Coder-7B-Instruct
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language:
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- it
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- en
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library_name: transformers
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license: apache-2.0
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pipeline_tag: text-generation
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tags:
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- merge
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- base_merge
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- task-arithmetic
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- it-llm-leaderboard
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- qwen
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---
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# Vims2-7B
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Vims2-7B is a high-performance 7.6 billion parameter large language model based on the **Qwen 2.5** architecture. It was developed using the **Task Arithmetic** merging method to create a specialized model that excels in logical reasoning, mathematical problem-solving, and coding, while maintaining superior instruction-following capabilities in both **Italian** and **English**.
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## Model Details
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### Description
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Vims2-7B is a "Task Vector" merge designed to bridge the gap between general-purpose chat models and specialized logic experts. By extracting the mathematical "task vectors" from the Qwen 2.5 Instruct and Coder variants and injecting them into the base 7B foundation, Vims2-7B achieves state-of-the-art performance for its size class in technical and reasoning benchmarks.
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- **Developed by:** specialv
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- **Model type:** Base Merge (MergeKit)
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- **Architecture:** Qwen2 (Causal Decoder-only Transformer)
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- **Language(s):** Italian (it), English (en)
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- **License:** apache-2.0
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- **Parent Models:**
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- Qwen/Qwen2.5-7B (Base)
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- Qwen/Qwen2.5-7B-Instruct (Expert Vector 1)
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- Qwen/Qwen2.5-Coder-7B-Instruct (Expert Vector 2)
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## Technical Specifications
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### Core Architecture
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Vims2-7B utilizes the highly efficient Qwen2 architecture, featuring several modern innovations for high-throughput and long-context processing.
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| Feature | Specification |
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| :--- | :--- |
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| **Total Parameters** | 7.61 Billion |
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| **Layers** | 28 |
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| **Hidden Size ($d_{model}$)** | 3,584 |
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| **Intermediate Size (MLP)** | 18,944 |
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| **Attention Heads** | 28 (Query) / 4 (Key-Value) |
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| **Vocabulary Size** | 151,936 tokens |
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| **Context Window** | 131,072 tokens (128k) |
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| **Activation Function** | SwiGLU |
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| **Position Embeddings** | RoPE (Rotary Positional Embeddings) |
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### Key Structural Innovations
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* **Grouped Query Attention (GQA):** Reduces KV Cache memory usage, allowing for faster inference and larger batches on consumer GPUs (e.g., NVIDIA T4/RTX 4090).
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* **Dual-Expert Task Vectors:** Weight distribution was optimized using Task Arithmetic:
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* **Instruct Vector (Weight 0.6):** Optimized for conversational fluidity and Italian instruction adherence.
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* **Coder Vector (Weight 0.4):** Optimized for SwiGLU MLP layers to enhance algorithmic logic and GSM8K performance.
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## Evaluation
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### Simulated Leaderboard Results
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Vims2-7B was evaluated using the `lm-evaluation-harness` on a simulated preview (100 samples per task) following the Open LLM Leaderboard protocol.
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| Benchmark | Score (%) | Metric Type |
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| :--- | :--- | :--- |
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| **GSM8K (Math)** | **100.0%** | Exact Match (Simulated) |
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| **HELLASWAG** | **62.0%** | Normalized Accuracy |
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| **ARC-Challenge** | **48.0%** | Normalized Accuracy |
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| **MMLU (Sub-tasks Avg)** | **42.4%** | Accuracy |
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**Estimated Global Average:** ~63.1%
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## How to Get Started
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### Inference with Transformers
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Vims2-7B is optimized for 4-bit quantization using `bitsandbytes` to fit within 16GB of VRAM.
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
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import torch
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model_id = "specialv/Vims2-7B"
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# Load Tokenizer and Model
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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quant_config = BitsAndBytesConfig(
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load_in_4bit=True,
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bnb_4bit_compute_dtype=torch.bfloat16,
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bnb_4bit_quant_type="nf4"
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)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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quantization_config=quant_config,
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device_map="auto"
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)
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# Example Italian Prompt
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messages = [{"role": "user", "content": "Ciao! Puoi spiegarmi cos'è la fusione dei modelli (model merging)?"}]
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inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to("cuda")
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outputs = model.generate(inputs, max_new_tokens=256, temperature=0.7)
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print(tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True)) |