ReWiz
Collection
The ReWiz series is based on a subset of data from 3 different data sets, which has been used for fine tuning.
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16 items
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Updated
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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).
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.
Conversation, brainstorming, and general instruction following
The Rewiz data set is a curated mix of 20,000 reasoning-based entries.
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)
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)
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)# 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
May hallucinate or provide incorrect information.
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}
}