Instructions to use vinoku89/aimo3-exp-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use vinoku89/aimo3-exp-1 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-0.6B") model = PeftModel.from_pretrained(base_model, "vinoku89/aimo3-exp-1") - Transformers
How to use vinoku89/aimo3-exp-1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="vinoku89/aimo3-exp-1") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("vinoku89/aimo3-exp-1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use vinoku89/aimo3-exp-1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "vinoku89/aimo3-exp-1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "vinoku89/aimo3-exp-1", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/vinoku89/aimo3-exp-1
- SGLang
How to use vinoku89/aimo3-exp-1 with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "vinoku89/aimo3-exp-1" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "vinoku89/aimo3-exp-1", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "vinoku89/aimo3-exp-1" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "vinoku89/aimo3-exp-1", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use vinoku89/aimo3-exp-1 with Docker Model Runner:
docker model run hf.co/vinoku89/aimo3-exp-1
Add runtime config for aimo3-exp-1
Browse files
logs/runtime_config_20260113_061348.yaml
ADDED
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| 1 |
+
_experiment_metadata:
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| 2 |
+
experiment_name: aimo3-exp-1
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| 3 |
+
timestamp: '2026-01-13T06:15:21.337589+08:00'
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| 4 |
+
wandb_run_id: ixd5w1kw
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| 5 |
+
experiment_name: aimo3-exp-1
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| 6 |
+
exp_tag: sft-lora
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| 7 |
+
framework: accelerate
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| 8 |
+
method: sft
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| 9 |
+
project:
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| 10 |
+
hf_username: vinoku89
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| 11 |
+
docker_username: vinoku89
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| 12 |
+
docker_image: vinoku89/llm-vastai-cuda-13.0.2-auto:latest
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| 13 |
+
git_repo: svk-123/distributed-sft-rl-inference-pipeline
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| 14 |
+
git_branch: dev
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| 15 |
+
model:
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| 16 |
+
name: Qwen/Qwen3-0.6B
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| 17 |
+
use_quantization: false
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| 18 |
+
quantized_model: null
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| 19 |
+
trust_remote_code: true
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| 20 |
+
data:
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| 21 |
+
handler: math_sft
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| 22 |
+
dataset_name: HAD653/GSM8K-OpenMath-MathReason-13k
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| 23 |
+
dataset_config: null
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| 24 |
+
train_file: null
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| 25 |
+
test_file: null
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| 26 |
+
max_seq_length: 8196
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| 27 |
+
problem_col: question
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| 28 |
+
solution_col: cot
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| 29 |
+
answer_col: final_answer
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| 30 |
+
use_thinking_format: true
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| 31 |
+
preprocessing:
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| 32 |
+
use_chat_template: true
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| 33 |
+
training:
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| 34 |
+
output_dir: ./checkpoints
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| 35 |
+
num_train_epochs: -1
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| 36 |
+
max_steps: 10
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| 37 |
+
per_device_train_batch_size: 2
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| 38 |
+
per_device_eval_batch_size: 2
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| 39 |
+
gradient_accumulation_steps: 4
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| 40 |
+
learning_rate: 2.0e-05
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| 41 |
+
weight_decay: 0.01
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| 42 |
+
max_grad_norm: 1.0
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| 43 |
+
optim: paged_adamw_8bit
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| 44 |
+
lr_scheduler_type: linear
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| 45 |
+
warmup_ratio: 0.05
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| 46 |
+
warmup_steps: 5
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| 47 |
+
bf16: true
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| 48 |
+
fp16: false
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| 49 |
+
gradient_checkpointing: true
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| 50 |
+
gradient_checkpointing_kwargs:
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| 51 |
+
use_reentrant: true
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| 52 |
+
dataloader_pin_memory: true
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| 53 |
+
logging_steps: 10
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| 54 |
+
eval_strategy: epoch
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| 55 |
+
eval_steps: 500
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| 56 |
+
save_strategy: epoch
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| 57 |
+
save_steps: 500
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| 58 |
+
save_total_limit: 2
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| 59 |
+
seed: 42
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| 60 |
+
resume_from_checkpoint: null
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| 61 |
+
report_to: wandb
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| 62 |
+
model: Qwen/Qwen3-0.6B
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| 63 |
+
dataset: HAD653/GSM8K-OpenMath-MathReason-13k
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| 64 |
+
problem_col: question
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| 65 |
+
solution_col: cot
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| 66 |
+
max_seq_length: 8196
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| 67 |
+
epochs: -1
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| 68 |
+
lora:
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| 69 |
+
enabled: true
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| 70 |
+
r: 16
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| 71 |
+
lora_alpha: 16
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| 72 |
+
lora_dropout: 0.05
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| 73 |
+
bias: none
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| 74 |
+
task_type: CAUSAL_LM
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| 75 |
+
target_modules:
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| 76 |
+
- q_proj
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| 77 |
+
- k_proj
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| 78 |
+
- v_proj
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| 79 |
+
- o_proj
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| 80 |
+
- gate_proj
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| 81 |
+
- up_proj
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| 82 |
+
- down_proj
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| 83 |
+
prompts:
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| 84 |
+
system: You are a helpful and reflective maths assistant. Please reason step by
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| 85 |
+
step to put the answer in \boxed{}.
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| 86 |
+
user_suffix: '
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| 87 |
+
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| 88 |
+
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| 89 |
+
Please reason step by step, and put your final answer within \boxed{}.'
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| 90 |
+
validation:
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| 91 |
+
enabled: true
|
| 92 |
+
required_after_training: true
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| 93 |
+
datasets:
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| 94 |
+
- name: aimo3-ref-val-1
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| 95 |
+
path: vinoku89/aimo3-ref-val
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| 96 |
+
split: validation
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| 97 |
+
num_samples: 1
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| 98 |
+
metric: accuracy
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| 99 |
+
columns:
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| 100 |
+
question: problem
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| 101 |
+
ground_truth: answer
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| 102 |
+
- name: aimo3-syn-val-1
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| 103 |
+
path: vinoku89/aimo3-syn-val-1
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| 104 |
+
split: validation
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| 105 |
+
num_samples: 1
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| 106 |
+
metric: accuracy
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| 107 |
+
columns:
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| 108 |
+
question: problem
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| 109 |
+
ground_truth: answer
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| 110 |
+
generation:
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| 111 |
+
max_new_tokens: '[REDACTED]'
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| 112 |
+
temperature: 0.6
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| 113 |
+
top_p: 0.95
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| 114 |
+
top_k: 20
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| 115 |
+
do_sample: true
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| 116 |
+
thresholds: {}
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| 117 |
+
inference:
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| 118 |
+
engine: vllm
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| 119 |
+
tensor_parallel_size: 0
|
| 120 |
+
batch_size: 8
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| 121 |
+
gpu_memory_utilization: 0.9
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| 122 |
+
max_model_len: 4096
|
| 123 |
+
dtype: half
|
| 124 |
+
max_num_seqs: 128
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| 125 |
+
wandb:
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| 126 |
+
enabled: true
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| 127 |
+
entity: null
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| 128 |
+
upload_results: true
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| 129 |
+
output_dir: validation_results
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| 130 |
+
wandb:
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| 131 |
+
enabled: true
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| 132 |
+
tags:
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| 133 |
+
- model:qwen3-0.6b
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| 134 |
+
- dataset:gsm8k-openmath-mathreason-13k
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| 135 |
+
- training
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| 136 |
+
huggingface:
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| 137 |
+
push_to_hub: true
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| 138 |
+
hub_model_id: vinoku89/aimo3-exp-1
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| 139 |
+
private: true
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| 140 |
+
infrastructure:
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| 141 |
+
gpu: RTX_4090
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| 142 |
+
ngpu: 1
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| 143 |
+
disk: 100
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| 144 |
+
budget: 1.0
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| 145 |
+
timeout: 300
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| 146 |
+
auto_destroy: true
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| 147 |
+
watchdog:
|
| 148 |
+
enabled: true
|
| 149 |
+
grace_period: 600
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| 150 |
+
idle_threshold: 10
|
| 151 |
+
idle_duration: 1200
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| 152 |
+
check_interval: 60
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| 153 |
+
_runtime:
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| 154 |
+
accelerate_config: configs/accelerate_runtime.yaml
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| 155 |
+
deepspeed_config: configs/deepspeed_runtime.json
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| 156 |
+
deepspeed_preset: zero0
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| 157 |
+
num_gpus: 1
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| 158 |
+
_training_timestamp: '20260113_061348'
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| 159 |
+
_run_number: '001'
|