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

Training Details

Training Data

This data set was created to limit sycofancy in language models and encouraging the models to (gently) push back and call out bad ideas.

Dataset: theprint/Gentle-Pushback-8.5k-alpaca
Format: alpaca

Training Procedure

Training epochs: 2
LoRA rank: 128
Learning rate: 0.0001
Batch size: 6
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/Genuine-7B-Instruct",
    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/Genuine-7B-Instruct",
    torch_dtype=torch.float16,
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("theprint/Genuine-7B-Instruct")

# 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)

Using with llama.cpp

bash

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

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

Limitations

May provide incorrect information.

Citation

If you use this model, please cite:

bibtex

@misc{genuine_7b_instruct,
  title={Genuine-7B-Instruct: Fine-tuned Qwen/Qwen2.5-7B-Instruct},
  author={theprint},
  year={2025},
  publisher={Hugging Face},
  url={https://huggingface.co/theprint/Genuine-7B-Instruct}
}

Acknowledgments

Base model: Qwen/Qwen2.5-7B-Instruct
Training dataset: theprint/Gentle-Pushback-8.5k-alpaca
Fine-tuning framework: Unsloth
Quantization: llama.cpp