Neural Chat 7B
Intel's Conversational AI Model

Running with OpenVINO (Intel CPU Optimization)

# Install OpenVINO and optimum-intel
pip install optimum[openvino] transformers

from optimum.intel import OVModelForCausalLM
from transformers import AutoTokenizer

# Load the model optimized for Intel CPUs
model = OVModelForCausalLM.from_pretrained(
    "Intel/neural-chat-7b-v3",
    export=True,  # Convert to OpenVINO format on first load
)
tokenizer = AutoTokenizer.from_pretrained("Intel/neural-chat-7b-v3")

# Generate text
inputs = tokenizer("Explain the difference between AI and ML", return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

OpenVINO inference can be 2-3x faster than standard PyTorch on Intel CPUs (12th gen+). This is the main advantage of choosing Neural Chat over other Mistral fine-tunes.

Python API Integration

import requests

def chat(prompt: str, system: str = "") -> str:
    """Chat with Neural Chat 7B via Ollama API."""
    response = requests.post(
        "http://localhost:11434/api/generate",
        json={
            "model": "neural-chat",
            "prompt": prompt,
            "system": system,
            "stream": False,
            "options": {"temperature": 0.7, "num_ctx": 8192}
        }
    )
    return response.json()["response"]

# Example: General conversation
print(chat("What are the pros and cons of remote work?"))

# Example: With system prompt
print(chat(
    "Draft a professional email declining a meeting invitation",
    system="You are a professional communication assistant. "
           "Write concise, polite responses."
))

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