| --- |
| 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! 🤠🌵* |
