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Thanush16
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Model Details
Model Provider
Thanush16
Model Tree
Base
meta-llama/Llama-3.2-1B-Instruct
Adapter
this model
Input Modalities
Text
Output Modalities
Text
Supported Functionality
Dedicated Endpoints
Container
Before (base model):
json
{"type": "function", "function": "get_weather", "parameters": {"city": "Nagoya", "unit": "celsius"}}
After (with this adapter):
json
[{"name": "get_weather", "arguments": {"city": "Nagoya", "unit": "celsius"}}]
The base model used "parameters" (wrong key) and a flattened structure. The adapter corrects it to the standard name/arguments format, wrapped in a list.
python
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfigfrom peft import PeftModelbase_id = "meta-llama/Llama-3.2-1B-Instruct"adapter_id = "Thanush16/llama-3.2-1b-function-calling"bnb_config = BitsAndBytesConfig(load_in_4bit=True,bnb_4bit_quant_type="nf4",bnb_4bit_compute_dtype=torch.bfloat16,bnb_4bit_use_double_quant=True,)base = AutoModelForCausalLM.from_pretrained(base_id, quantization_config=bnb_config, device_map="auto")model = PeftModel.from_pretrained(base, adapter_id)model.eval()tokenizer = AutoTokenizer.from_pretrained(adapter_id)
Build prompts with tokenizer.apply_chat_template(messages, tools=tools, add_generation_prompt=True).
Trained on 500 examples to fix output format, not to be a broadly capable tool-use model. It reliably produces well-formed calls but wasn't evaluated for tool-selection accuracy on hard or ambiguous queries. Only the LoRA adapter is released — load it alongside the base model.