Instructions to use amihai4by/logic-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use amihai4by/logic-v2 with Transformers:

# Use a pipeline as a high-level helper
# Warning: Pipeline type "image-to-text" is no longer supported in transformers v5.
# You must load the model directly (see below) or downgrade to v4.x with:
# 'pip install "transformers<5.0.0'
from transformers import pipeline

pipe = pipeline("image-to-text", model="amihai4by/logic-v2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("amihai4by/logic-v2")
model = AutoModelForCausalLM.from_pretrained("amihai4by/logic-v2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

llama-cpp-python

How to use amihai4by/logic-v2 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="amihai4by/logic-v2",
	filename="logic-v2.Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = "\"cats.jpg\""
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use amihai4by/logic-v2 with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf amihai4by/logic-v2:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf amihai4by/logic-v2:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf amihai4by/logic-v2:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf amihai4by/logic-v2:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf amihai4by/logic-v2:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf amihai4by/logic-v2:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf amihai4by/logic-v2:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf amihai4by/logic-v2:Q4_K_M

Use Docker

docker model run hf.co/amihai4by/logic-v2:Q4_K_M

LM Studio
Jan
Ollama
How to use amihai4by/logic-v2 with Ollama:
```
ollama run hf.co/amihai4by/logic-v2:Q4_K_M
```

Unsloth Studio new

How to use amihai4by/logic-v2 with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for amihai4by/logic-v2 to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for amihai4by/logic-v2 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for amihai4by/logic-v2 to start chatting

Pi new

How to use amihai4by/logic-v2 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf amihai4by/logic-v2:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "amihai4by/logic-v2:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use amihai4by/logic-v2 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf amihai4by/logic-v2:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default amihai4by/logic-v2:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use amihai4by/logic-v2 with Docker Model Runner:
```
docker model run hf.co/amihai4by/logic-v2:Q4_K_M
```

Lemonade

How to use amihai4by/logic-v2 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull amihai4by/logic-v2:Q4_K_M

Run and chat with the model

lemonade run user.logic-v2-Q4_K_M

List all available models

lemonade list

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This repository is publicly accessible, but you have to accept the conditions to access its files and content.

Logic-v2

A practical multimodal reasoning engine for verification and inspection

Welcome

Logic-v2 is a multimodal model built for teams who need more than captions.
It is designed to help systems inspect inputs, reason about correctness, and produce conclusions you can automate.

If you are building an internal service, an engineering workflow, or a “gatekeeper” step in a pipeline (approve/reject/flag), this model is intended for that kind of work.

What it is good for

Logic-v2 is optimized for logic-first multimodal reasoning, especially when the question is:

Is something missing, inconsistent, or incorrect?
Does this violate an expected constraint or rule?
Can this be validated, or should it be rejected?
What evidence supports the decision?

Typical inputs include:

diagrams, dashboards, screenshots
infrastructure photos (racks, cabling, labels)
QA/inspection images
structured prompts that ask for validation, not creativity

What it is not

Logic-v2 is not intended for:

general-purpose chat
creative writing or storytelling
meme generation
consumer-grade low-latency experiences

If your goal is conversation or creativity, you will likely prefer a different model.

Design principles

Logic over fluency
Predictability over creativity
Systems over chat interfaces
Private inference over public endpoints

This model is meant to be a reliable component inside engineering and enterprise workflows.

Hardware and deployment intent

Logic-v2 was built and validated in a cluster-style environment and is intended for serious GPU infrastructure, particularly NVIDIA Blackwell-class systems (e.g., B200).

Recommended deployment patterns:

private inference service (internal API)
pipeline stage (validation/inspection gate)
controlled environments (security-boundary friendly)

Usage (Transformers)

from transformers import AutoModelForVision2Seq, AutoProcessor

model_id = "amihai4by/logic-v2"

model = AutoModelForVision2Seq.from_pretrained(
    model_id,
    trust_remote_code=True
)

processor = AutoProcessor.from_pretrained(model_id)

For production workloads, consider serving with vLLM or a dedicated inference stack that matches your latency and concurrency requirements.

Limitations and considerations

Model outputs can be sensitive to prompt structure. For decision workflows, prefer:
- explicit constraints
- requested output schema (JSON)
- “state assumptions” and “cite evidence from input” patterns
This model is not designed to replace domain experts. It is designed to assist and gate workflows with high signal.

Responsible use

Use Logic-v2 in contexts where:

automated decisions can be reviewed or audited
failure modes are understood and monitored
you have a fallback path for ambiguous or low-confidence cases

Avoid using it as the sole authority for high-stakes decisions without human oversight.

License

MIT

Downloads last month: -

Safetensors

Model size

8B params

Tensor type

BF16