armand0e/Qwen3.5-9B-Opus-Agent-LoRA

Qwen3.5 9B - Opus Agent

This is a finetune on Opus traces and a small dataset. Reasoning was left untouched

Total train time: 4 hours

Benchmarks

General benchmarks

Benchmarks provided by @nightmedia, as always thanks for taking the time :)

                                  arc	    arc/e	boolq
armand0e/Qwen3.5-9B-Opus-Agent	0.589	0.747	0.901
   Jackrong/Qwopus3.5-9B-Coder	0.561	0.721	0.89
                    Qwen3.5-9B	0.571	0.719	0.895

Targeted benchmarks

Conducted via BenchLocal. All benchmarks are 2-shot (1 retry on failure) for ease of comparison to the numbers found in Jackrong's Qwopus3.5 Coder

All benchmarks for other models were done in Q8_0 only this model's benchmarks were done in Q4_K_M

1. Instruction Following - InstructFollow-15

InstructFollow-15 evaluates formatting, count, numbering, sentence, and length constraints.

Instruction Following - InstructFollow-15 Metrics
Model	Test Set	Comprehensive Score	Dimension Scores (A/B/C/D/E)
armand0e/Qwen3.5-9B-Opus-Agent	InstructFollow-15	97	100 / 100 / 100 / 85 / 100
Jackrong/Qwopus3.5-9B-coder	InstructFollow-15	93	100 / 100 / 100 / 67 / 100

2. Code Debugging & Bug Fixing - BugFind-15

BugFind-15 evaluates real debugging capability across syntax bugs, logic errors, and trap code.

Code Debugging & Bug Fixing - BugFind-15 Metrics
Model	Test Set	Comprehensive Score	Dimension Scores (A/B/C/D/E)
armand0e/Qwen3.5-9B-Opus-Agent	BugFind-15	84	67 / 100 / 87 / 67 / 90
Jackrong/Qwopus3.5-9B-coder	BugFind-15	79	67 / 87 / 100 / 77 / 43
Jackrong/MLX-Qwen3.5-9B-DeepSeek-V4-Flash	BugFind-15	75	67 / 100 / 67 / 57 / 80
armand0e/Qwen3.5-9B-Agent	BugFind-15	58	29 / 87 / 73 / 20 / 67

3. Tool Call Stability - ToolCall-15

ToolCall-15 targets stability and precision in direct tool-calling behavior.

Tool Call Stability - ToolCall-15 Metrics
Model	Test Set	Comprehensive Score	Dimension Scores (A/B/C/D/E)
armand0e/Qwen3.5-9B-Opus-Agent	ToolCall-15	100	100 / 100 / 100 / 100 / 100
Jackrong/Qwopus3.5-9B-coder	ToolCall-15	100	100 / 100 / 100 / 100 / 100
Qwen/Qwen3.5-9B	ToolCall-15	100	100 / 100 / 100 / 100 / 100
armand0e/Qwen3.5-9B-Agent	ToolCall-15	93	100 / 100 / 100 / 67 / 100

4. Complex Agent Performance - HermesAgent-20

HermesAgent-20 evaluates complex agent behavior across memory, orchestration, skill use, scheduling, and delegation.

Complex Agent Performance - HermesAgent-20 Metrics
Model	Test Set	Comprehensive Score	Core Dimensions (Memory / Orchestration / Skills / Scheduling / Boundaries)
Jackrong/Qwopus3.5-9B-coder	HermesAgent-20	85	84 / 93 / 88 / 75 / 84
armand0e/Qwen3.5-9B-Opus-Agent	HermesAgent-20	80	100 / 93 / 80 / 75 / 50
Qwen/Qwen3.5-9B	HermesAgent-20	71	75 / 58 / 100 / 53 / 69
armand0e/Qwen3.5-9B-Agent	HermesAgent-20	68	71 / 83 / 43 / 61 / 80
DJLougen/Harmonic-Hermes-9B	HermesAgent-20	47	60 / 45 / 23 / 69 / 38

Click to show screenshots

Ignore the gemma-4 llama.cpp alias I had set, this was old and I forgot to change it

ToolCall-15

HermesAgent-20

BugFind-15

InstructFollow-15

Training Script

Training Script

# -*- coding: utf-8 -*-
import os
from unsloth import FastModel
import torch
from trl import SFTConfig, SFTTrainer
from teich import mask_data, prepare_data

MAX_SEQ_LEN = 32768
MODEL_NAME = os.environ.get("MODEL_NAME", "qwen/Qwen3.5-9B")
OUTPUT_DIR = os.environ.get("OUTPUT_DIR", "outputs/qwen-tool-sft")
HUB_REPO_ID = os.environ.get("HUB_REPO_ID", "armand0e/Qwen3.5-9B-Opus-Agent")
HF_TOKEN = os.environ.get("HF_TOKEN", "")

model, tokenizer = FastModel.from_pretrained(
    model_name=MODEL_NAME,
    max_seq_length=MAX_SEQ_LEN,
    load_in_4bit=False,
    load_in_8bit=False,
    full_finetuning=False,
)

model = FastModel.get_peft_model(
    model,
    finetune_vision_layers     = False, # Turn off for just text!
    finetune_language_layers   = True,  # Should leave on!
    finetune_attention_modules = True,  # Attention good for GRPO
    finetune_mlp_modules       = True,  # Should leave on always!

    r = 32,           # Larger = higher accuracy, but might overfit
    lora_alpha = 64,  # Recommended alpha == r at least
    lora_dropout = 0,
    bias = "none",
    random_state = 3407,
)

train_dataset = prepare_data(
    {
        "chat": {
            "source": "TeichAI/claude-4.5-opus-high-reasoning-250x"
        },
        "opus-agent": {
            "source": "armand0e/badlogicgames-pi-mono-opus-filtered",
        },
    },
    tokenizer,
    split="train",
    hf_token=HF_TOKEN,
    chat_template_kwargs={"enable_thinking": True},
    max_length=MAX_SEQ_LEN,
    drop_oversized_examples=True,
    trim_oversized_followups=True,
    tokenize=True,
    strict=True,
)

trainer = SFTTrainer(
    model=model,
    tokenizer=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=None,
    args=SFTConfig(
        dataset_text_field="text",
        dataset_num_proc=1,
        max_length=MAX_SEQ_LEN,
        packing=False,
        per_device_train_batch_size=1,
        gradient_accumulation_steps=8,
        warmup_steps= 5,
        num_train_epochs=2,
        learning_rate=2e-5,
        logging_steps=1,
        save_steps=100,
        save_total_limit=3,
        optim="adamw_8bit",
        weight_decay=0.01,
        max_grad_norm=0.3,
        lr_scheduler_type="linear",
        output_dir=OUTPUT_DIR,
        seed=3407,
        report_to="none",
    ),
)

trainer = mask_data(
    trainer,
    tokenizer=tokenizer,
    train_on_reasoning=False,
    train_on_final_answers=True,
    train_on_tools=True,
)

print(trainer.train_dataset.preview())

trainer_stats = trainer.train(resume_from_checkpoint=False)

model.push_to_hub(f"{HUB_REPO_ID}-LoRA", token=HF_TOKEN)
tokenizer.push_to_hub(f"{HUB_REPO_ID}-LoRA", token=HF_TOKEN)

model.push_to_hub_merged(HUB_REPO_ID, tokenizer, save_method="merged_16bit", token=HF_TOKEN)

---

The data for this model was easily formatted and masked with Teich

• Developed by: armand0e
• License: apache-2.0
• Finetuned from model : Qwen/Qwen3.5-9B

This qwen3_5 model was trained 2x faster with Unsloth and Huggingface's TRL library.