Instructions to use theprint/Rewiz-Tom-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries
PEFT
How to use theprint/Rewiz-Tom-7B with PEFT:
```
Task type is invalid.
```

How to use theprint/Rewiz-Tom-7B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="theprint/Rewiz-Tom-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("theprint/Rewiz-Tom-7B")
model = AutoModelForCausalLM.from_pretrained("theprint/Rewiz-Tom-7B")
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 theprint/Rewiz-Tom-7B with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="theprint/Rewiz-Tom-7B",
	filename="gguf/Rewiz-Tom-7B-f16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use theprint/Rewiz-Tom-7B with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf theprint/Rewiz-Tom-7B:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf theprint/Rewiz-Tom-7B:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf theprint/Rewiz-Tom-7B:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf theprint/Rewiz-Tom-7B: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 theprint/Rewiz-Tom-7B:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf theprint/Rewiz-Tom-7B: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 theprint/Rewiz-Tom-7B:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf theprint/Rewiz-Tom-7B:Q4_K_M

Use Docker

docker model run hf.co/theprint/Rewiz-Tom-7B:Q4_K_M

LM Studio
Jan

vLLM

How to use theprint/Rewiz-Tom-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "theprint/Rewiz-Tom-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "theprint/Rewiz-Tom-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/theprint/Rewiz-Tom-7B:Q4_K_M

SGLang

How to use theprint/Rewiz-Tom-7B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "theprint/Rewiz-Tom-7B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "theprint/Rewiz-Tom-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "theprint/Rewiz-Tom-7B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "theprint/Rewiz-Tom-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use theprint/Rewiz-Tom-7B with Ollama:
```
ollama run hf.co/theprint/Rewiz-Tom-7B:Q4_K_M
```

Unsloth Studio new

How to use theprint/Rewiz-Tom-7B 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 theprint/Rewiz-Tom-7B 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 theprint/Rewiz-Tom-7B to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for theprint/Rewiz-Tom-7B to start chatting

Pi new

How to use theprint/Rewiz-Tom-7B with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf theprint/Rewiz-Tom-7B: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": "theprint/Rewiz-Tom-7B:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use theprint/Rewiz-Tom-7B with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf theprint/Rewiz-Tom-7B: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 theprint/Rewiz-Tom-7B:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use theprint/Rewiz-Tom-7B with Docker Model Runner:
```
docker model run hf.co/theprint/Rewiz-Tom-7B:Q4_K_M
```

Lemonade

How to use theprint/Rewiz-Tom-7B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull theprint/Rewiz-Tom-7B:Q4_K_M

Run and chat with the model

lemonade run user.Rewiz-Tom-7B-Q4_K_M

List all available models

lemonade list

Rewiz-Tom-7B

A fine-tuned 7B parameter model specialized in reasoning (Rewiz), based on a model that was already finetuned for step-by-step instruction and conversation (Tom).

Model Details

This model is a fine-tuned version of theprint/Tom-Qwen-7B-Instruct using the Unsloth framework with LoRA (Low-Rank Adaptation) for efficient training.

Developed by: theprint
Model type: Causal Language Model (Fine-tuned with LoRA)
Language: en
License: apache-2.0
Base model: theprint/Tom-Qwen-7B-Instruct
Fine-tuning method: LoRA with rank 128

Intended Use

Conversation, brainstorming, and general instruction following

Training Details

Training Data

The Rewiz data set is a curated mix of 20,000 reasoning-based entries.

Dataset: theprint/ReWiz
Format: alpaca

Training Procedure

Training epochs: 2
LoRA rank: 128
Learning rate: 0.0002
Batch size: 4
Framework: Unsloth + transformers + PEFT
Hardware: NVIDIA RTX 5090

Usage

from unsloth import FastLanguageModel
import torch

# Load model and tokenizer
model, tokenizer = FastLanguageModel.from_pretrained(
    model_name="theprint/Rewiz-Tom-7B",
    max_seq_length=4096,
    dtype=None,
    load_in_4bit=True,
)

# Enable inference mode
FastLanguageModel.for_inference(model)

# Example usage
inputs = tokenizer(["Your prompt here"], return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=256, temperature=0.7)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

Alternative Usage (Standard Transformers)

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model = AutoModelForCausalLM.from_pretrained(
    "theprint/Rewiz-Tom-7B",
    torch_dtype=torch.float16,
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("theprint/Rewiz-Tom-7B")

# Example usage
messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "Your question here"}
]

inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True)
outputs = model.generate(inputs, max_new_tokens=256, temperature=0.7, do_sample=True)
response = tokenizer.decode(outputs[0][inputs.shape[-1]:], skip_special_tokens=True)
print(response)

GGUF Quantized Versions

Quantized GGUF versions are available in the gguf/ directory for use with llama.cpp:

Rewiz-Tom-7B-f16.gguf (14531.9 MB) - 16-bit float (original precision, largest file)
Rewiz-Tom-7B-q3_k_m.gguf (3632.0 MB) - 3-bit quantization (medium quality)
Rewiz-Tom-7B-q4_k_m.gguf (4466.1 MB) - 4-bit quantization (medium, recommended for most use cases)
Rewiz-Tom-7B-q5_k_m.gguf (5192.6 MB) - 5-bit quantization (medium, good quality)
Rewiz-Tom-7B-q6_k.gguf (5964.5 MB) - 6-bit quantization (high quality)
Rewiz-Tom-7B-q8_0.gguf (7723.4 MB) - 8-bit quantization (very high quality)

Using with llama.cpp

# Download a quantized version (q4_k_m recommended for most use cases)
wget https://huggingface.co/theprint/Rewiz-Tom-7B/resolve/main/gguf/Rewiz-Tom-7B-q4_k_m.gguf

# Run with llama.cpp
./llama.cpp/main -m Rewiz-Tom-7B-q4_k_m.gguf -p "Your prompt here" -n 256

Limitations

May hallucinate or provide incorrect information.

Citation

If you use this model, please cite:

@misc{rewiz_tom_7b,
  title={Rewiz-Tom-7B: Fine-tuned theprint/Tom-Qwen-7B-Instruct},
  author={theprint},
  year={2025},
  publisher={Hugging Face},
  url={https://huggingface.co/theprint/Rewiz-Tom-7B}
}