---
base_model: theprint/Tom-Qwen-7B-Instruct
library_name: peft
pipeline_tag: text-generation
language: en
license: apache-2.0
tags:
- lora
- sft
- transformers
- trl
- unsloth
- fine-tuned
datasets:
- theprint/ReWiz
---
# 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

```python
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)

```python
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

```bash
# 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:

```bibtex
@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}
}
```

## Acknowledgments

- Base model: [theprint/Tom-Qwen-7B-Instruct](https://huggingface.co/theprint/Tom-Qwen-7B-Instruct)
- Training dataset: [theprint/ReWiz](https://huggingface.co/datasets/theprint/ReWiz)
- Fine-tuning framework: [Unsloth](https://github.com/unslothai/unsloth)
- Quantization: [llama.cpp](https://github.com/ggerganov/llama.cpp)