Text Generation
Transformers
Safetensors
Korean
qwen3
rlvr
reasoning
countdown
conversational
text-generation-inference
Instructions to use NotoriousH2/Qwen3-4B-Countdown-RLVR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NotoriousH2/Qwen3-4B-Countdown-RLVR with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="NotoriousH2/Qwen3-4B-Countdown-RLVR") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("NotoriousH2/Qwen3-4B-Countdown-RLVR") model = AutoModelForCausalLM.from_pretrained("NotoriousH2/Qwen3-4B-Countdown-RLVR", device_map="auto") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use NotoriousH2/Qwen3-4B-Countdown-RLVR with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "NotoriousH2/Qwen3-4B-Countdown-RLVR" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "NotoriousH2/Qwen3-4B-Countdown-RLVR", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/NotoriousH2/Qwen3-4B-Countdown-RLVR
- SGLang
How to use NotoriousH2/Qwen3-4B-Countdown-RLVR 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 "NotoriousH2/Qwen3-4B-Countdown-RLVR" \ --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": "NotoriousH2/Qwen3-4B-Countdown-RLVR", "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 "NotoriousH2/Qwen3-4B-Countdown-RLVR" \ --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": "NotoriousH2/Qwen3-4B-Countdown-RLVR", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use NotoriousH2/Qwen3-4B-Countdown-RLVR with Docker Model Runner:
docker model run hf.co/NotoriousH2/Qwen3-4B-Countdown-RLVR
| { | |
| "schema_version": 5, | |
| "plan": { | |
| "system_prompt": "제시된 숫자는 각 항목을 정확히 한 번씩 사용하여 목표값을 만드세요. 허용 연산은 +, -, *, /이며 괄호를 사용할 수 있습니다. 연산 기호는 두 수 사이에만 사용하세요. 숫자를 이어 붙여 새 수를 만들지 마세요. 풀이를 간결하게 검토한 뒤 <answer>와 </answer> 사이에는 등호와 계산 결과 없이 수식만 작성하세요.", | |
| "training_lineage": { | |
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| "validation": "before_model_merge_and_evaluation", | |
| "seal": { | |
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| "task": "countdown_rlvr", | |
| "model": { | |
| "repo_id": "Qwen/Qwen3-4B", | |
| "revision": "1cfa9a7208912126459214e8b04321603b3df60c" | |
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| "baseline": { | |
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| "model_id": "Qwen/Qwen3-4B", | |
| "revision": "1cfa9a7208912126459214e8b04321603b3df60c" | |
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| { | |
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| "step": 480 | |
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| "validation": { | |
| "path": "data/countdown/validation.jsonl", | |
| "subset": "all_rows", | |
| "expected_samples": 128 | |
| }, | |
| "final_holdout": { | |
| "path": "data/countdown/test.jsonl", | |
| "expected_samples": 256, | |
| "sealed_before_selection": true, | |
| "used_for_selection": false | |
| }, | |
| "decoding": { | |
| "completion_count": 8, | |
| "max_tokens": 3072, | |
| "temperature": 0.6, | |
| "top_p": 0.95, | |
| "top_k": 20, | |
| "seed": 42, | |
| "request_seed_rule": "base_seed_plus_problem_index" | |
| }, | |
| "eligibility": [ | |
| "complete_validation_and_completion_grid", | |
| "valid_expression_rate_at_least_base", | |
| "truncated_completion_rate_at_most_base_plus_one_standard_error" | |
| ], | |
| "truncation_eligibility": { | |
| "sample_unit": "completion", | |
| "sample_count": 1024, | |
| "base_rate": "base_truncated_completion_rate", | |
| "base_standard_error_formula": "sqrt(base_rate * (1 - base_rate) / sample_count)", | |
| "maximum_eligible_rate_formula": "base_rate + base_standard_error", | |
| "comparison": "candidate_truncated_completion_rate <= maximum_eligible_rate" | |
| }, | |
| "selection": { | |
| "primary_metric": "pass_at_1", | |
| "improvement_gate": "best_lower_one_se_above_base", | |
| "tie_band": "within_one_se_of_best", | |
| "tie_breakers": [ | |
| "pass_at_8", | |
| "lower_truncated_completion_rate", | |
| "earlier_checkpoint" | |
| ], | |
| "fallback": "Base" | |
| }, | |
| "server_reproducibility": { | |
| "server_seed": 42, | |
| "environment": { | |
| "VLLM_BATCH_INVARIANT": "1" | |
| } | |
| }, | |
| "promotion": { | |
| "adapter_dir": "adapter", | |
| "merged_dir": ".", | |
| "enabled": true | |
| }, | |
| "workers": 16, | |
| "selection_path": "checkpoint_selection.json" | |
| }, | |
| "training_lineage": { | |
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| "task": "countdown_rlvr", | |
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| "resume_from_checkpoint": null | |
| }, | |
| { | |
| "name": "재개 구간", | |
| "start_step": 121, | |
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| "sha256": "b06a1be3604d637aa19bd61af57aadf98fbbffcb8ef4db8d477fe8535c617497", | |
| "samples": 128, | |
| "problem_ids": [ | |
| "countdown-4-1-3-8-9-t70", | |
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| "countdown-4-3-5-5-7-t20", | |
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| "countdown-4-2-2-7-8-t36", | |
| "countdown-4-1-2-6-6-t71", | |
| "countdown-4-1-2-4-9-t12", | |
| "countdown-4-1-2-4-6-t27", | |
| "countdown-4-2-3-4-5-t45", | |
| "countdown-4-2-4-6-8-t100", | |
| "countdown-4-4-5-7-9-t32", | |
| "countdown-4-2-4-5-7-t84", | |
| "countdown-4-1-5-7-9-t47", | |
| "countdown-4-1-6-7-9-t24", | |
| "countdown-4-2-5-7-9-t97", | |
| "countdown-4-4-6-6-7-t35", | |
| "countdown-4-1-4-6-7-t46", | |
| "countdown-4-1-1-5-8-t26", | |
| "countdown-4-1-2-6-8-t22", | |
| "countdown-4-2-6-7-9-t73", | |
| "countdown-4-2-3-4-7-t34", | |
| "countdown-4-3-6-7-8-t95", | |
| "countdown-4-2-4-9-9-t83", | |
| "countdown-4-2-4-5-5-t20", | |
| "countdown-4-2-3-8-9-t26", | |
| "countdown-4-4-4-7-9-t18", | |
| "countdown-4-2-2-3-8-t24", | |
| "countdown-4-1-3-4-8-t44", | |
| "countdown-4-1-8-8-9-t55", | |
| "countdown-4-3-4-7-7-t74", | |
| "countdown-4-1-6-7-9-t66", | |
| "countdown-4-4-5-7-7-t34", | |
| "countdown-4-1-2-4-4-t24", | |
| "countdown-4-1-1-4-5-t18", | |
| "countdown-4-3-4-5-8-t36", | |
| "countdown-4-3-3-6-8-t11", | |
| "countdown-4-1-6-8-8-t41", | |
| "countdown-4-4-5-9-9-t31", | |
| "countdown-4-6-8-8-9-t42", | |
| "countdown-4-5-6-8-8-t11", | |
| "countdown-4-1-3-6-8-t41", | |
| "countdown-4-2-4-6-7-t34", | |
| "countdown-4-3-5-6-9-t52", | |
| "countdown-4-1-2-4-5-t16", | |
| "countdown-4-1-5-7-8-t55", | |
| "countdown-4-3-5-9-9-t21", | |
| "countdown-4-3-4-5-7-t84", | |
| "countdown-4-2-5-5-5-t32", | |
| "countdown-4-3-4-6-7-t42", | |
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