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Villanelle
blackmamba2003/Villanelle
Villanelle is a machine learning model from blackmamba2003. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as apache-2.0.
A LoRA fine-tune of Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled trained on OpenClaw tool-call data — optimized for agentic reasoning with structured tool invocation.
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At a glance
- Library
- transformers
- License
- apache-2.0
- Access
- Public
- Created
- Mar 26, 2026
- Updated
- Mar 26, 2026
- SHA
- 2e4aa967
Base models
Model card
From the Hugging Face model README
Medina-Qwen3.5-27B-OpenClaw
A LoRA fine-tune of Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled trained on OpenClaw tool-call data — optimized for agentic reasoning with structured tool invocation.
The base model is a Claude 4.6 Opus reasoning distillation of Qwen3.5-27B. This fine-tune adds structured tool-calling capability in the OpenClaw XML format, making it suitable for local agentic deployments.
GGUF Downloads
| Quantization | Size | Use case |
|---|---|---|
| Q4_K_M | 15.4 GB | ✅ Recommended — 24GB VRAM or 32GB unified memory |
| Q8_0 | 26.6 GB | Near-lossless, 32GB+ VRAM or 48GB unified memory |
Training Details
| Parameter | Value |
|---|---|
| Base model | Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled |
| Training GPU | NVIDIA A100 SXM4 80GB |
| Framework | Unsloth + TRL SFTTrainer |
| Dataset | OpenClaw tool-call examples (243 examples) |
| Training time | ~102 minutes |
| Epochs | 3 |
| Steps | 48 |
| Final loss | 0.7066 |
| LoRA rank | r=32, alpha=64, rsLoRA=True |
| LoRA dropout | 0.05 |
| LoRA targets | q/k/v/o/gate/up/down proj |
| Trainable params | 159,383,552 (0.58%) |
| Context window | 4096 tokens |
| Batch size | 1 (effective: 8 with grad accum) |
| Learning rate | 2e-4 (cosine schedule, 5% warmup) |
| Quantization | 4-bit NF4 during training |
| Optimizer | AdamW 8-bit |
What It Does
This adapter teaches the model the OpenClaw tool-calling format — a structured XML-style invocation pattern used by the OpenClaw AI agent platform:
<function_calls>
<invoke name="TOOL_NAME">
<parameter name="PARAM_NAME">value</parameter>
</invoke>
</function_calls>
Supported tools in training data: exec, read, write, edit, web_search, web_fetch, browser, memory_search, memory_get, message, cron, nodes, image, pdf, sessions_spawn, session_status
Usage with llama.cpp / Ollama
# Ollama (Q4_K_M)
ollama run hf.co/peterjohannmedina/Medina-Qwen3.5-27B-OpenClaw:Q4_K_M
# llama.cpp direct
./llama-cli -m Medina-Qwen3.5-27B-OpenClaw-Q4_K_M.gguf \
--ctx-size 4096 -p "You are an AI assistant with access to tools..."
Usage with Transformers (LoRA adapter)
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
import torch
base = AutoModelForCausalLM.from_pretrained(
"Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled",
torch_dtype=torch.bfloat16,
device_map="auto",
)
model = PeftModel.from_pretrained(base, "peterjohannmedina/Medina-Qwen3.5-27B-OpenClaw")
tokenizer = AutoTokenizer.from_pretrained("peterjohannmedina/Medina-Qwen3.5-27B-OpenClaw")
Companion Model
For a smaller version that runs on M3 MacBook / 16GB systems:
- Medina-Qwen3-14B-OpenClaw (Q4_K_M: 8.4 GB, Q8_0: 14.6 GB)
License
Apache 2.0 — same as the base model.
- Library
- transformers
- License
- apache-2.0
- Languages
- en
- Created
- Mar 26, 2026
- Updated
- Mar 26, 2026