README.md

metadata

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

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.