Total runs:
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Run Growth:
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Growth Rate:
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Sponsored by
APIMart
armand0e / Qwen3.5-9B-Agent
Model's Last Updated: May 17 2026
image-text-to-text
Introduction of Qwen3.5-9B-Agent
Model Details of Qwen3.5-9B-Agent
This model was trained on the following datasets using the qwen3.6 chat template (training was done with enable_thinking and preserve_thinking set to
True
):
-
armand0e/badlogicgames-pi-mono-opus-filtered- Pi traces from Claude Opus (mainly 4.5) -
armand0e/kimi-k2.6-claude-code-traces- Claude Code traces from kimi k2.6 -
armand0e/kimi-k2.6-agent- Codex traces from kimi k2.6 -
armand0e/minimax-m2.7-agent- Pi traces from minimax m2.7 -
TeichAI/Claude-Opus-4.6-Reasoning-887x(Downsampled to 200 examples, only present to stabilize chat behavior)
Training specs:
MAX_SEQ_LEN = 49152
from unsloth import FastModel
import torch
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 = 64, # Larger = higher accuracy, but might overfit
lora_alpha = 64, # Recommended alpha == r at least
lora_dropout = 0,
bias = "none",
random_state = 3407,
)
from teich import prepare_data
train_dataset = prepare_data(
{
"opus-agent": {
"source": "armand0e/badlogicgames-pi-mono-opus-filtered",
},
"kimi-claude": {
"source": "armand0e/kimi-k2.6-claude-code-traces",
},
"kimi-codex": {
"source": "armand0e/kimi-k2.6-agent",
},
"minimax-m2.7": {
"source": "armand0e/ag-datagen-v2-test",
},
"chat": {
"source": "TeichAI/Claude-Opus-4.6-Reasoning-887x",
"max_examples": 200,
}
},
tokenizer,
split="train",
hf_token=HF_TOKEN,
chat_template_kwargs={"enable_thinking": True, "preserve_thinking": True},
max_length=MAX_SEQ_LEN,
drop_oversized_examples=True,
trim_oversized_followups=True,
tokenize=True,
strict=True,
)
from trl import SFTConfig, SFTTrainer
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-4,
logging_steps=1,
save_steps=100,
save_total_limit=3,
optim="adamw_8bit",
weight_decay=0.01,
lr_scheduler_type="linear",
output_dir=OUTPUT_DIR,
seed=3407,
report_to="none",
),
)
from teich import mask_data
trainer = mask_data(
trainer,
tokenizer=tokenizer,
train_on_reasoning=True,
train_on_final_answers=True,
train_on_tools=True,
)
This tune was very data limited, but still impresses me. I encourage everyone to generate their own high quality data for their own use cases, they can all be aggregated together.
Uploaded finetuned model
- Developed by: armand0e
- License: apache-2.0
- Finetuned from model : unsloth/Qwen3.5-9B
This qwen3_5 model was trained 2x faster with Unsloth and Huggingface's TRL library.
Runs of armand0e Qwen3.5-9B-Agent on huggingface.co
392
Total runs
0
24-hour runs
2
3-day runs
5
7-day runs
392
30-day runs
More Information About Qwen3.5-9B-Agent huggingface.co Model
Url of Qwen3.5-9B-Agent
Qwen3.5-9B-Agent huggingface.co Url
Provider of Qwen3.5-9B-Agent huggingface.co
Other API from armand0e
Updated:November 01 2025
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