ToolSmith-8b

krishna-toolcall-7b

A fine-tuned Qwen2.5-7B-Instruct model specialized for reliable JSON tool/function calling in AI agent workflows. Built to output structured function call schemas consistently, making it suitable for local agentic pipelines where tool invocation accuracy matters.

Key Details

Training Metrics

Training ran for 500 steps across ~3.2 epochs. Loss decreased from 1.17 to 0.29 over training with stable gradient norms throughout.

Usage

Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("sriksven/krishna-toolcall-7b")
tokenizer = AutoTokenizer.from_pretrained("sriksven/krishna-toolcall-7b")

messages = [
    {
        "role": "system",
        "content": (
            "You are a helpful assistant with access to the following functions. "
            "Use them if required -\n"
            '{"name": "get_weather", "description": "Get current weather", '
            '"parameters": {"type": "object", "properties": {"location": '
            '{"type": "string"}}, "required": ["location"]}}'
        ),
    },
    {"role": "user", "content": "What's the weather in Boston?"},
]

inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True)
outputs = model.generate(inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Unsloth (faster inference)

from unsloth import FastLanguageModel

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name="sriksven/krishna-toolcall-7b",
    max_seq_length=2048,
    load_in_4bit=True,
)
FastLanguageModel.for_inference(model)

Intended Use

• Building AI agents that invoke tools via structured JSON function calls
• Local/private agentic pipelines where API-based models are not an option
• Prototyping multi-agent systems with reliable tool-use behavior
• Research on function-calling capabilities in open-weight 7B models

Limitations

• Trained on synthetic function-calling data (glaive-v2), not real API traces
• 10K training examples — may not cover all tool-calling edge cases
• No RLHF or DPO alignment applied — outputs may occasionally be off-format
• Best used with the ChatML prompt template matching the training format
• Not suitable for safety-critical applications without additional validation

Training Infrastructure

Source Code

Training scripts and configs: github.com/sriksven/LLM-FineTune-Suite

License

Apache 2.0