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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, AutoTokenizer model_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.
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizer model_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)