kshitijthakkar/loggenix-moe-0.4B-0.2A-sft-s3.1

loggenix-moe-0.4B-0.2A-sft-s3.1

Model Description

This is a Mixture of Experts (MoE) language model fine-tuned for various tasks including:

• —Tool/Function Calling
• —Code Generation
• —Reasoning & Math
• —Safety & Content Moderation

Evaluation Results

Evaluation Date: 20260128_105522

Standard Benchmarks (lm-evaluation-harness)

Synthetic Task Categories

Tool-Calling Performance

Code Generation Performance

Overall Summary

• —Synthetic Tasks Mean Score: 21.68%
• —Total Tasks Evaluated: 170
• —Task Coverage: 180.9%

Usage

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("kshitijthakkar/loggenix-moe-0.4B-0.2A-sft-s3.1")
model = AutoModelForCausalLM.from_pretrained("kshitijthakkar/loggenix-moe-0.4B-0.2A-sft-s3.1", trust_remote_code=True)

# For tool calling
messages = [
    {"role": "system", "content": "You are a helpful assistant with access to tools."},
    {"role": "user", "content": "What's the weather in San Francisco?"}
]
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt")
outputs = model.generate(inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0]))

Training Data

The model was fine-tuned on a diverse dataset including:

• —Tool-calling datasets (Toucan, ToolACE, SmoLAgents)
• —Safety datasets (HelpSteer3, Safety-Guard, Content-Safety-Reasoning)
• —Math datasets (GSM8K, MetaMath, Big-Math-RL)
• —Code datasets (Magicoder)
• —Reasoning datasets (Reasoning-Gemini, Textbook-Reasoning)

License

Apache 2.0