Carnice MoE 35B-A3B — Hermes-Focused Agentic Model

QLoRA fine-tune of Qwen3.5-35B-A3B (MoE, 3B active parameters) optimized for agentic workflows and Hermes Agent runtime. Two-stage training adapted from kai-os/Carnice-9b.

Credits

Training methodology adapted from kai-os/Carnice-9b — same two-stage approach and datasets, applied to the larger MoE architecture. Key inspiration: training on actual Hermes Agent execution traces for native agentic behavior.

For GGUF quantizations (Q4, Q5, Q6, Q8, MXFP4), see samuelcardillo/Carnice-MoE-35B-A3B-GGUF.

Model Details

What Makes This Different

Unlike generic reasoning distillation, this model was trained on actual Hermes Agent execution traces — real conversations where an AI agent:

• Executes terminal commands and processes output
• Performs file editing operations
• Chains multi-step tool calls with results feeding back
• Uses browser-assisted workflows
• Makes decisions based on environmental feedback

This teaches the model the exact conversation patterns Hermes expects, rather than just generic reasoning.

Training Details

Two-Stage Approach

Stage A — Reasoning Repair (1 epoch)

• Strengthens base model reasoning before agent-specific training
• Loss: 0.4159

Stage B — Hermes Traces (2 epochs)

• Agent-specific behavioral training on real execution traces
• Loss: 0.3115

Training Configuration

Trainable Parameters

6,881,280 (0.02% of 35B total)

Usage with llama.cpp

llama-server \
  --model Carnice-MoE-35B-A3B-Q8_0.gguf \
  --n-gpu-layers -1 \
  --ctx-size 131072 \
  --host 0.0.0.0 --port 8082

Acknowledgements

• kai-os — Carnice training methodology and Hermes traces dataset
• open-thoughts — Agent SFT dataset
• bespokelabs — Bespoke-Stratos reasoning dataset
• Unsloth — QLoRA training framework
• Qwen — Base model