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---
base_model:
- Qwen/Qwen2.5-7B
- Qwen/Qwen2.5-7B-Instruct
- Qwen/Qwen2.5-Coder-7B-Instruct
language:
- it
- en
library_name: transformers
license: apache-2.0
pipeline_tag: text-generation
tags:
- merge
- base_merge
- task-arithmetic
- it-llm-leaderboard
- qwen
---
# Vims2-7B
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**.
## Model Details
### Description
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.
- **Developed by:** specialv
- **Model type:** Base Merge (MergeKit)
- **Architecture:** Qwen2 (Causal Decoder-only Transformer)
- **Language(s):** Italian (it), English (en)
- **License:** apache-2.0
- **Parent Models:**
- Qwen/Qwen2.5-7B (Base)
- Qwen/Qwen2.5-7B-Instruct (Expert Vector 1)
- Qwen/Qwen2.5-Coder-7B-Instruct (Expert Vector 2)
## Technical Specifications
### Core Architecture
Vims2-7B utilizes the highly efficient Qwen2 architecture, featuring several modern innovations for high-throughput and long-context processing.
| Feature | Specification |
| :--- | :--- |
| **Total Parameters** | 7.61 Billion |
| **Layers** | 28 |
| **Hidden Size ($d_{model}$)** | 3,584 |
| **Intermediate Size (MLP)** | 18,944 |
| **Attention Heads** | 28 (Query) / 4 (Key-Value) |
| **Vocabulary Size** | 151,936 tokens |
| **Context Window** | 131,072 tokens (128k) |
| **Activation Function** | SwiGLU |
| **Position Embeddings** | RoPE (Rotary Positional Embeddings) |
### Key Structural Innovations
* **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).
* **Dual-Expert Task Vectors:** Weight distribution was optimized using Task Arithmetic:
* **Instruct Vector (Weight 0.6):** Optimized for conversational fluidity and Italian instruction adherence.
* **Coder Vector (Weight 0.4):** Optimized for SwiGLU MLP layers to enhance algorithmic logic and GSM8K performance.
## Evaluation
### Simulated Leaderboard Results
Vims2-7B was evaluated using the `lm-evaluation-harness` on a simulated preview (100 samples per task) following the Open LLM Leaderboard protocol.
| Benchmark | Score (%) | Metric Type |
| :--- | :--- | :--- |
| **GSM8K (Math)** | **100.0%** | Exact Match (Simulated) |
| **HELLASWAG** | **62.0%** | Normalized Accuracy |
| **ARC-Challenge** | **48.0%** | Normalized Accuracy |
| **MMLU (Sub-tasks Avg)** | **42.4%** | Accuracy |
**Estimated Global Average:** ~63.1%
![Vims2-7B Performance Comparison](vims2_comparison.png)
## How to Get Started
### Inference with Transformers
Vims2-7B is optimized for 4-bit quantization using `bitsandbytes` to fit within 16GB of VRAM.
```python
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
import torch
model_id = "specialv/Vims2-7B"
# Load Tokenizer and Model
tokenizer = AutoTokenizer.from_pretrained(model_id)
quant_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_compute_dtype=torch.bfloat16,
bnb_4bit_quant_type="nf4"
)
model = AutoModelForCausalLM.from_pretrained(
model_id,
quantization_config=quant_config,
device_map="auto"
)
# Example Italian Prompt
messages = [{"role": "user", "content": "Ciao! Puoi spiegarmi cos'è la fusione dei modelli (model merging)?"}]
inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to("cuda")
outputs = model.generate(inputs, max_new_tokens=256, temperature=0.7)
print(tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True))

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dtype: bfloat16
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models:
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"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"151646": {
"content": "<|object_ref_start|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"151647": {
"content": "<|object_ref_end|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"151648": {
"content": "<|box_start|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"151649": {
"content": "<|box_end|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
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"151650": {
"content": "<|quad_start|>",
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"151651": {
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"151652": {
"content": "<|vision_start|>",
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"rstrip": false,
"single_word": false,
"special": true
},
"151653": {
"content": "<|vision_end|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
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"151654": {
"content": "<|vision_pad|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"151655": {
"content": "<|image_pad|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"151656": {
"content": "<|video_pad|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"151657": {
"content": "<tool_call>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": false
},
"151658": {
"content": "</tool_call>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": false
},
"151659": {
"content": "<|fim_prefix|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": false
},
"151660": {
"content": "<|fim_middle|>",
"lstrip": false,
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"rstrip": false,
"single_word": false,
"special": false
},
"151661": {
"content": "<|fim_suffix|>",
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"single_word": false,
"special": false
},
"151662": {
"content": "<|fim_pad|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": false
},
"151663": {
"content": "<|repo_name|>",
"lstrip": false,
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"rstrip": false,
"single_word": false,
"special": false
},
"151664": {
"content": "<|file_sep|>",
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"normalized": false,
"rstrip": false,
"single_word": false,
"special": false
}
},
"additional_special_tokens": [
"<|im_start|>",
"<|im_end|>",
"<|object_ref_start|>",
"<|object_ref_end|>",
"<|box_start|>",
"<|box_end|>",
"<|quad_start|>",
"<|quad_end|>",
"<|vision_start|>",
"<|vision_end|>",
"<|vision_pad|>",
"<|image_pad|>",
"<|video_pad|>"
],
"bos_token": null,
"chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n {%- else %}\n {{- 'You are a helpful assistant.' }}\n {%- endif %}\n {{- \"\\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>\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\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\" }}\n{%- else %}\n {%- if messages[0]['role'] == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n {%- else %}\n {{- '<|im_start|>system\\nYou are a helpful assistant.<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {{- '<|im_start|>' + message.role }}\n {%- if message.content %}\n {{- '\\n' + message.content }}\n {%- endif %}\n {%- for tool_call in message.tool_calls %}\n {%- if tool_call.function is defined %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '\\n<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n<tool_response>\\n' }}\n {{- message.content }}\n {{- '\\n</tool_response>' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n",
"clean_up_tokenization_spaces": false,
"eos_token": "<|endoftext|>",
"errors": "replace",
"model_max_length": 131072,
"pad_token": "<|endoftext|>",
"split_special_tokens": false,
"tokenizer_class": "Qwen2Tokenizer",
"unk_token": null
}

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