phi-3-mini-text-to-cypher-supply

Phi-3 Mini — Fine-Tuned for Text-to-Cypher (Supply Chain) — GGUF Q8_0

By: M. Dhanush Vardhan (dhanushmekaka)

This is a fine-tuned version of Microsoft Phi-3 Mini 4K Instruct, trained for:

• Text → Cypher query generation
• Supply chain analytics
• Inventory, logistics & forecasting queries
• Structured reasoning

The model is exported to GGUF (Q8_0) for high-speed CPU inference using:

• llama.cpp
• Ollama
• LM Studio
• llama-cpp-python

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📁 Files

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🚀 Run with llama.cpp (CLI)

./main -m https://huggingface.co/dhanushmekaka/phi-3-mini-text-to-cypher-supply/resolve/main/phi3_gguf_q8_0.gguf \
       -p "<|user|>Top 5 products by total ordered quantity.<|end|><|assistant|>"

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🐍 Run with Python (llama-cpp-python)

from llama_cpp import Llama

llm = Llama.from_pretrained(
    repo_id="dhanushmekaka/phi-3-mini-text-to-cypher-supply",
    filename="phi3_gguf_q8_0.gguf",
    n_ctx=4096,
)

prompt = """<|user|>
Top 5 products by total ordered quantity.
<|end|>
<|assistant|>
"""

response = llm(prompt, max_tokens=150)
print(response["choices"][0]["text"])

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💬 Use with Ollama

Modelfile:

FROM ./phi3_gguf_q8_0.gguf
TEMPLATE """<|system|>{{ .System }}<|end|><|user|>{{ .Prompt }}<|end|><|assistant|>"""

Create:

ollama create phi3-supply -f Modelfile

Run:

ollama run phi3-supply

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🧠 Model Training Details

• Training method: QLoRA (4-bit)
• Dataset size: 5k+ records
• Task: text → Cypher / analytics reasoning
• Frameworks: TRL, PEFT, BitsAndBytes, HuggingFace
• Merged to fp16 → converted to GGUF (Q8_0)

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📌 License

• Base model: MIT
• Finetuned checkpoints: permissive for commercial use

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❤️ Author

M. Dhanush Vardhan Data Scientist • Supply Chain AI • LLM Engineer HuggingFace: https://huggingface.co/dhanushmekaka

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✅ 3️⃣ Ready Python Load Script

This loads your HF model directly and runs inference:

from llama_cpp import Llama

# Load GGUF from your HuggingFace repo
llm = Llama.from_pretrained(
    repo_id="dhanushmekaka/phi-3-mini-text-to-cypher-supply",
    filename="phi3_gguf_q8_0.gguf",
    n_ctx=4096,
)

# Prompt (Phi-3 Chat Template)
prompt = """<|user|>
Top 5 products by total ordered quantity.
<|end|>
<|assistant|>
"""

# Run inference
output = llm(prompt, max_tokens=200)
print("Model Response:\n", output["choices"][0]["text"])

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🎉 Everything is ready.

If you want, I can also generate:

Just tell me!