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README.md

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+---
+base_model: unsloth/Qwen3-Coder-30B-A3B-Instruct
+base_model_relation: adapter
+library_name: peft
+license: apache-2.0
+language:
+- code
+model_name: qwen3-coder-30b-a3b-codemonkey
+pipeline_tag: text-generation
+tags:
+- lora
+- peft
+- qwen3
+- qwen3-coder
+- qwen3moe
+- sft
+- code
+- unsloth
+---
+
+# qwen3-coder-30b-a3b-codemonkey
+
+LoRA adapter for `unsloth/Qwen3-Coder-30B-A3B-Instruct`.
+
+## Files
+
+- `adapter_model.safetensors`: adapter weights
+- `adapter_config.json`: PEFT config
+- `tokenizer.json`, `tokenizer_config.json`, `chat_template.jinja`: tokenizer and chat template assets
+
+## Load with Transformers + PEFT
+
+```python
+from peft import PeftModel
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+base_id = "unsloth/Qwen3-Coder-30B-A3B-Instruct"
+adapter_id = "1337Hero/qwen3-coder-30b-a3b-codemonkey"
+
+tokenizer = AutoTokenizer.from_pretrained(base_id)
+base_model = AutoModelForCausalLM.from_pretrained(
+    base_id,
+    torch_dtype="auto",
+    device_map="auto",
+)
+model = PeftModel.from_pretrained(base_model, adapter_id)
+
+messages = [
+    {"role": "user", "content": "Write a Python function that atomically replaces a file."}
+]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True,
+)
+inputs = tokenizer(text, return_tensors="pt").to(model.device)
+outputs = model.generate(**inputs, max_new_tokens=512)
+completion = outputs[0][inputs.input_ids.shape[1]:]
+print(tokenizer.decode(completion, skip_special_tokens=True))
+```
+
+## Adapter details
+
+- Base model: `unsloth/Qwen3-Coder-30B-A3B-Instruct`
+- PEFT type: `LoRA`
+- Rank: `r=16`
+- Alpha: `32`
+- Target modules: `q_proj`, `k_proj`, `v_proj`, `o_proj`
+
+## GGUF
+
+A merged GGUF release can live in a separate repo such as
+`1337Hero/qwen3-coder-30b-a3b-codemonkey-GGUF`.