Carnice Qwen3.6 MoE 35B-A3B — Hermes-Focused Agentic Model

QLoRA fine-tune of Qwen3.6-35B-A3B (MoE, 3B active parameters) optimized for agentic workflows and Hermes Agent runtime. Two-stage training adapted from kai-os/Carnice-9b.

This is the successor to Carnice-MoE-35B-A3B (based on Qwen3.5), retrained on the newer Qwen3.6 base which brings improved agentic coding, extended context (262K native, up to 1M with RoPE scaling), and native multimodal support.

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.

Available Formats

FP8 Usage (vLLM)

# Clone the repo and point vLLM to the fp8/ subfolder
vllm serve samuelcardillo/Carnice-Qwen3.6-MoE-35B-A3B --quantization fp8 --dtype auto

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: 0.4281

Stage B — Hermes Traces (2 epochs)

• Agent-specific behavioral training on real execution traces
• Loss: 0.3045

Training Configuration

Trainable Parameters

13,762,560 (0.04% of 35.1B total)

Usage

Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "samuelcardillo/Carnice-Qwen3.6-MoE-35B-A3B",
    torch_dtype="auto",
    device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained("samuelcardillo/Carnice-Qwen3.6-MoE-35B-A3B")

messages = [{"role": "user", "content": "Explain the Riemann hypothesis in simple terms."}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=2048)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

vLLM

vllm serve samuelcardillo/Carnice-Qwen3.6-MoE-35B-A3B --dtype auto --max-model-len 262144

llama.cpp

For llama.cpp usage, see the GGUF repo.

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