Instructions to use solomoniw/CallForge-1B-v0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use solomoniw/CallForge-1B-v0 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="solomoniw/CallForge-1B-v0")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("solomoniw/CallForge-1B-v0")
model = AutoModelForCausalLM.from_pretrained("solomoniw/CallForge-1B-v0", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use solomoniw/CallForge-1B-v0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "solomoniw/CallForge-1B-v0"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "solomoniw/CallForge-1B-v0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/solomoniw/CallForge-1B-v0

SGLang

How to use solomoniw/CallForge-1B-v0 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "solomoniw/CallForge-1B-v0" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "solomoniw/CallForge-1B-v0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "solomoniw/CallForge-1B-v0" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "solomoniw/CallForge-1B-v0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use solomoniw/CallForge-1B-v0 with Docker Model Runner:
```
docker model run hf.co/solomoniw/CallForge-1B-v0
```

CallForge-1B v0

CallForge-1B v0 is a lightweight 1B parameter tool-calling specialist fine-tuned from openbmb/MiniCPM5-1B. It is designed to reliably select tools, construct schema-valid arguments, execute calls, and repair execution failures in agentic tool-use workflows.

Model Summary

Base Model: openbmb/MiniCPM5-1B
Parameters: ~1.08B
Architecture: LlamaForCausalLM
Format: Native MiniCPM5 chat template with XML tool-call markup
License: Apache-2.0

Evaluation & Results

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%

Quickstart & Inference

You can run CallForge-1B v0 using Hugging Face Transformers:

import torch
from 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)

Intended Use

CallForge-1B v0 is designed for local, resource-constrained agentic applications requiring reliable tool use and structured function calling.

Downloads last month: -

Safetensors

Model size

1B params

Tensor type

F32

Model tree for solomoniw/CallForge-1B-v0

Base model

openbmb/MiniCPM5-1B

Finetuned

(53)

this model