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solomoniw
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Model Details
Model Provider
solomoniw
Model Tree
openbmb/MiniCPM5-1B
Input Modalities
Output Modalities
Supported Functionality
CallForge-1B v0 demonstrates significant improvements in zero-shot generalization to unseen tools while maintaining solid performance on seen tool suites:
| Metric | Base MiniCPM5-1B | CallForge-1B v0 |
|---|---|---|
| Seen Task Success Rate | 20% | 40% |
| Held-out Tool Task Success Rate | 0% | 43% |
| Parser Well-formed Rate | 100% | 92% |
| Parser Schema-valid Rate | 100% | 92% |
You can run CallForge-1B v0 using Hugging Face Transformers:
python
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizermodel_id = "solomoniw/CallForge-1B-v0"tokenizer = AutoTokenizer.from_pretrained(model_id)model = AutoModelForCausalLM.from_pretrained(model_id,torch_dtype=torch.bfloat16 if torch.cuda.is_available() else torch.float32,device_map="auto")tools = [{"type": "function","function": {"name": "get_weather","description": "Fetch current weather conditions for a given city.","parameters": {"type": "object","properties": {"city": {"type": "string", "description": "City name"}},"required": ["city"]}}}]messages = [{"role": "user", "content": "What is the weather like in Tokyo right now?"}]inputs = tokenizer.apply_chat_template(messages,tools=tools,add_generation_prompt=True,return_tensors="pt").to(model.device)outputs = model.generate(inputs, max_new_tokens=256)response = tokenizer.decode(outputs[0][inputs.shape[-1]:], skip_special_tokens=True)print(response)
CallForge-1B v0 is designed for local, resource-constrained agentic applications requiring reliable tool use and structured function calling.