Triangle104/Virtuoso-Small-v2-Q4_K_S-GGUF

This model was converted to GGUF format from arcee-ai/Virtuoso-Small-v2 using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.

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Virtuoso-Small-v2 (14B) is our next-generation, 14-billion-parameter language model that builds upon the original Virtuoso-Small architecture. This version is distilled from Deepseek-v3, leveraging an expanded dataset of 5B+ tokens worth of logits. Model Details

Architecture Base: Qwen-2.5-14B
Parameter Count: 14B
Tokenizer:
    Initially integrated with Deepseek-v3 tokenizer for logit extraction.
    Final alignment uses the Qwen tokenizer, using specialized “tokenizer surgery” for cross-architecture compatibility.
Distillation Data:
    ~1.1B tokens/logits from Deepseek-v3’s training data.
    Logit-level distillation using a proprietary “fusion merging” approach afterwards for maximum fidelity.
License: Apache-2.0

Background on Deepseek Distillation

Deepseek-v3 serves as the teacher model, from which we capture logits across billions of tokens. Rather than standard supervised fine-tuning, we apply a full logit-level replication. This ensures more precise transference of knowledge, including advanced reasoning in:

Technical and scientific queries
Complex code generation
Mathematical problem-solving

How to Use

Below is a sample code snippet using transformers:

from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "arcee-ai/Virtuoso-Small-v2" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name)

prompt = "Provide a concise summary of quantum entanglement." inputs = tokenizer(prompt, return_tensors="pt") outputs = model.generate(**inputs, max_new_tokens=150) print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Training & Fine-Tuning

Initial Training: Began with Qwen-14B, calibrated for large-scale text ingestion.
Distillation & Merging:
    Trained on ~1.1B tokens worth of Deepseek-v3 logits.
    Employed “fusion merging” to retain as much teacher expertise as possible.
    Final step included DPO to improve alignment and reduce model hallucinations.
Continuous Development: Additional R1 distillations are in progress to further enhance performance and specialization.

Limitations

Context Length: 128k Tokens
Knowledge Cut-off: Training data may not reflect the latest events or developments, leading to gaps in current knowledge beyond June 2024.

Ethical Considerations

Content Generation Risks: Like any language model, Virtuoso-Small-v2 can potentially generate harmful or biased content if prompted in certain ways.

License

Virtuoso-Small-v2 (14B) is released under the Apache-2.0 License. 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.

If you have questions or would like to share your experiences using these models, please connect with us on social media. We’re excited to see what you build—and how these models help you innovate!

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Use with llama.cpp

Install llama.cpp through brew (works on Mac and Linux)

brew install llama.cpp

Invoke the llama.cpp server or the CLI.

CLI:

llama-cli --hf-repo Triangle104/Virtuoso-Small-v2-Q4_K_S-GGUF --hf-file virtuoso-small-v2-q4_k_s.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo Triangle104/Virtuoso-Small-v2-Q4_K_S-GGUF --hf-file virtuoso-small-v2-q4_k_s.gguf -c 2048

Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.

Step 1: Clone llama.cpp from GitHub.

git clone https://github.com/ggerganov/llama.cpp

Step 2: Move into the llama.cpp folder and build it with LLAMA_CURL=1 flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).

cd llama.cpp && LLAMA_CURL=1 make

Step 3: Run inference through the main binary.

./llama-cli --hf-repo Triangle104/Virtuoso-Small-v2-Q4_K_S-GGUF --hf-file virtuoso-small-v2-q4_k_s.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo Triangle104/Virtuoso-Small-v2-Q4_K_S-GGUF --hf-file virtuoso-small-v2-q4_k_s.gguf -c 2048