---
language:
- en
license: apache-2.0
base_model:
- Qwen/Qwen3.5-4B
tags:
- cowboy
- unsloth
- lora
- gguf
- fine-tuned
- fun
- qwen3.5
library_name: transformers
pipeline_tag: text-generation
---

# 🤠 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
1. Download the .gguf file from this repo
2. Open LM Studio
3. Drag and drop the .gguf file in
4. 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! 🤠🌵*