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Model: arcee-ai/Virtuoso-Lite Source: Original Platform
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README.md
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README.md
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---
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base_model:
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- tiiuae/Falcon3-10B-Base
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library_name: transformers
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license: other
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tags:
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- mergekit
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- merge
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---
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<img src="https://huggingface.co/arcee-train/Virtuoso-Lite/resolve/main/virtuoso-lite.jpg" alt="Virtuoso-Lite Logo" style="display: block; margin: 0 auto;" />
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**Virtuoso-Lite (10B)** is our next-generation, 10-billion-parameter language model based on the Llama-3 architecture. It is distilled from Deepseek-v3 using ~1.1B tokens/logits, allowing it to achieve robust performance at a significantly reduced parameter count compared to larger models. Despite its compact size, Virtuoso-Lite excels in a variety of tasks, demonstrating advanced reasoning, code generation, and mathematical problem-solving capabilities.
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### GGUF
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Quantizations available [here](https://huggingface.co/arcee-ai/Virtuoso-Lite-GGUF)
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### Model Details
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- **Architecture Base:** Falcon-10B (based on Llama-3)
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- **Parameter Count:** 10B
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- **Tokenizer:**
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- Initially integrated with Deepseek-v3 tokenizer for logit extraction.
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- Final alignment uses the Llama-3 tokenizer, with specialized “tokenizer surgery” for cross-architecture compatibility.
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- **Distillation Data:**
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- ~1.1B tokens/logits from Deepseek-v3’s training data.
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- Logit-level distillation using a proprietary “fusion merging” approach for maximum fidelity.
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- **License:** [falcon-llm-license](https://falconllm.tii.ae/falcon-terms-and-conditions.html)
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### Background on Deepseek Distillation
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Deepseek-v3 serves as the teacher model, from which we capture logits across billions of tokens. Rather than standard supervised fine-tuning, Virtuoso-Lite applies a full logit-level replication to preserve the most crucial insights from the teacher. This approach enables:
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- Strong performance on technical/scientific queries
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- Enhanced code generation and debugging
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- Improved consistency in math-intensive tasks
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### Intended Use Cases
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- **Chatbots & Virtual Assistants**
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- **Lightweight Enterprise Data Analysis**
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- **Research Prototypes & Proofs of Concept**
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- **STEM Educational Tools (where smaller footprint is advantageous)**
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### Evaluations
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<img src="https://huggingface.co/arcee-train/Virtuoso-Lite/resolve/main/Benchmarks.png" alt="Virtuoso-Lite Logo" style="display: block; margin: 0 auto;" />
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### How to Use
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Below is a sample code snippet using `transformers`:
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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model_name = "arcee-ai/virtuoso-lite"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForCausalLM.from_pretrained(model_name)
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prompt = "Provide a concise summary of quantum entanglement."
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inputs = tokenizer(prompt, return_tensors="pt")
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outputs = model.generate(**inputs, max_new_tokens=150)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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### Training & Fine-Tuning
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- **Initial Training:** Began with Falcon-10B, optimized for large-scale text ingestion.
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- **Distillation & Merging:**
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- Trained on ~1.1B tokens/logits from Deepseek-v3.
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- Employed “fusion merging” to capture detailed teacher insights.
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- Final step included DPO to enhance alignment and mitigate hallucinations.
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- **Future Developments:** We plan to incorporate additional R1 distillations to further improve specialized performance and reduce model footprint.
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### Performance
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Virtuoso-Lite demonstrates strong results across multiple benchmarks (e.g., BBH, MMLU-PRO, MATH), often standing its ground against models with higher parameter counts. This efficiency is largely credited to logit-level distillation, which compresses the teacher model’s capabilities into a more parameter-friendly package.
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### Limitations
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- **Context Length:** 32k Tokens (may vary depending on the final tokenizer settings and system resources).
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- **Knowledge Cut-off:** Training data may not reflect the latest events or developments beyond June 2024.
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### Ethical Considerations
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- **Content Generation Risks:** Like any language model, Virtuoso-Lite can generate potentially harmful or biased content if prompted in certain ways.
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-
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### License
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**Virtuoso-Lite (10B)** is released under the [falcon-llm-license License](https://falconllm.tii.ae/falcon-terms-and-conditions.html). You are free to use, modify, and distribute this model in both commercial and non-commercial applications, subject to the terms and conditions of the license.
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If you have questions or would like to share your experiences using Virtuoso-Lite (10B), please connect with us on social media. We’re excited to see what you build—and how this model helps you innovate!
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config.json
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"LlamaForCausalLM"
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"model_type": "llama",
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}
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||||
41
special_tokens_map.json
Normal file
41
special_tokens_map.json
Normal file
@@ -0,0 +1,41 @@
|
||||
{
|
||||
"additional_special_tokens": [
|
||||
">>TITLE<<",
|
||||
">>ABSTRACT<<",
|
||||
">>INTRODUCTION<<",
|
||||
">>SUMMARY<<",
|
||||
">>COMMENT<<",
|
||||
">>ANSWER<<",
|
||||
">>QUESTION<<",
|
||||
">>DOMAIN<<",
|
||||
">>EMAIL_ADDRESS<<",
|
||||
">>IP_ADDRESS<<",
|
||||
"<|startoftext|>",
|
||||
">>IP_ADDRESS_0<<",
|
||||
">>IP_ADDRESS_1<<",
|
||||
">>IP_ADDRESS_2<<",
|
||||
">>IP_ADDRESS_3<<",
|
||||
">>IP_ADDRESS_4<<",
|
||||
">>IP_ADDRESS_5<<",
|
||||
">>IP_ADDRESS_6<<",
|
||||
">>IP_ADDRESS_7<<",
|
||||
">>IP_ADDRESS_8<<",
|
||||
">>IP_ADDRESS_9<<",
|
||||
">>PASSWORD<<",
|
||||
">>KEY<<"
|
||||
],
|
||||
"eos_token": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"pad_token": {
|
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"content": "<|pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
664576
tokenizer.json
Normal file
664576
tokenizer.json
Normal file
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Load Diff
16233
tokenizer_config.json
Normal file
16233
tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load Diff
BIN
virtuoso-lite.jpg
Normal file
BIN
virtuoso-lite.jpg
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 68 KiB |
Reference in New Issue
Block a user