🤠 Cowboy Qwen3.5-4B
A fine-tuned version of Qwen3.5-4B trained to respond like a cowboy! Saddle up partner, this model talks with full western charm, slang, and soul.
🐴 Model Details
| Property | Details |
|---|---|
| Base Model | Qwen3.5-4B |
| Fine-tuning Method | LoRA (bf16) |
| Training Framework | Unsloth |
| Training Epochs | 3 |
| Context Length | 2048 tokens |
| Model Type | Causal Language Model |
| Language | English 🤠 |
🌵 Example Output
User: How do I make a good cup of coffee?
Cowboy:
Grind yer beans fresh, dark as midnight. Use water hot but not boil-over, and bloom 'em first with just enough to wake 'em. Stir slow, pour steady, and taste like a good story — rich, with a little bitterness and a lot of soul. Don't rush it; good coffee takes its time, like a long ride on horseback.
🚀 How to Use
With Ollama (Recommended)
Step 1 — Create a Modelfile:
FROM ./cowboy.gguf
SYSTEM """
You are a cowboy assistant. You always respond in a cowboy accent and use western slang. Yee-haw partner!
"""
Step 2 — Build the model:
ollama create cowboy -f Modelfile
Step 3 — Run it:
ollama run cowboy
With LM Studio
- Download the .gguf file from this repo
- Open LM Studio
- Drag and drop the .gguf file in
- Start chatting! 🤠
With Python (Transformers)
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model = AutoModelForCausalLM.from_pretrained(
"QuillBytes/cowboy",
torch_dtype = torch.bfloat16,
device_map = "auto",
)
tokenizer = AutoTokenizer.from_pretrained("QuillBytes/cowboy")
messages = [{"role": "user", "content": "How do I rope a steer?"}]
text = tokenizer.apply_chat_template(
messages,
tokenize = False,
add_generation_prompt = True,
)
inputs = tokenizer(text=text, return_tensors="pt").to("cuda")
outputs = model.generate(
**inputs,
max_new_tokens = 256,
temperature = 0.7,
do_sample = True,
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
📦 Available Files
| File | Description |
|---|---|
| cowboy.gguf | 4-bit quantized GGUF (best for local use) |
| cowboy-F16.gguf | Multimodal projector |
| *.safetensors | Full precision model weights |
| tokenizer_config.json | Tokenizer config |
🛠️ Training Details
This model was fine-tuned on Kaggle using Unsloth with the following setup:
# LoRA Config
r = 16
lora_alpha = 16
lora_dropout = 0
target_modules = "all-linear"
use_gradient_checkpointing = "unsloth"
# Training Config
per_device_train_batch_size = 2
gradient_accumulation_steps = 4
warmup_steps = 5
num_train_epochs = 3
learning_rate = 2e-4
optim = "adamw_8bit"
⚠️ Limitations
- This model is trained for fun and entertainment purposes 🤠
- It will respond in cowboy style even when not appropriate
- Not suitable for serious/professional use cases
Made with ❤️ and a whole lot of yeehaw! 🤠🌵