Text Generation
Transformers
Safetensors
GGUF
English
gpt2
chatbot
lora
instruction-tuned
distilled
microllm2
conversational
text-generation-inference
Instructions to use MLVXN/MicroLLM2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MLVXN/MicroLLM2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="MLVXN/MicroLLM2") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("MLVXN/MicroLLM2") model = AutoModelForCausalLM.from_pretrained("MLVXN/MicroLLM2", 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
- llama.cpp
How to use MLVXN/MicroLLM2 with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf MLVXN/MicroLLM2:Q4_K_M # Run inference directly in the terminal: llama cli -hf MLVXN/MicroLLM2:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf MLVXN/MicroLLM2:Q4_K_M # Run inference directly in the terminal: llama cli -hf MLVXN/MicroLLM2:Q4_K_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf MLVXN/MicroLLM2:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf MLVXN/MicroLLM2:Q4_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf MLVXN/MicroLLM2:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf MLVXN/MicroLLM2:Q4_K_M
Use Docker
docker model run hf.co/MLVXN/MicroLLM2:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use MLVXN/MicroLLM2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "MLVXN/MicroLLM2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "MLVXN/MicroLLM2", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/MLVXN/MicroLLM2:Q4_K_M
- SGLang
How to use MLVXN/MicroLLM2 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 "MLVXN/MicroLLM2" \ --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": "MLVXN/MicroLLM2", "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 "MLVXN/MicroLLM2" \ --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": "MLVXN/MicroLLM2", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use MLVXN/MicroLLM2 with Ollama:
ollama run hf.co/MLVXN/MicroLLM2:Q4_K_M
- Unsloth Studio
How to use MLVXN/MicroLLM2 with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for MLVXN/MicroLLM2 to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for MLVXN/MicroLLM2 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for MLVXN/MicroLLM2 to start chatting
- Docker Model Runner
How to use MLVXN/MicroLLM2 with Docker Model Runner:
docker model run hf.co/MLVXN/MicroLLM2:Q4_K_M
- Lemonade
How to use MLVXN/MicroLLM2 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull MLVXN/MicroLLM2:Q4_K_M
Run and chat with the model
lemonade run user.MicroLLM2-Q4_K_M
List all available models
lemonade list
- Atomic Chat
File size: 73,319 Bytes
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[*] MicroLLM2 MMLU β model: /home/zeus/microllm2/microllm2-checkpoints/final_merged shots=5 limit=20
[*] GPT2-XL 1.5B 1024ctx vocab=50259 (ChatML) β MMLU via direct eval (no harness needed)
[*] lm-eval not installed β using lightweight direct MMLU eval (same logic, no harness)
[*] Install harness for official numbers: pip install lm-eval==0.4.4
[*] Loading tokenizer + model /home/zeus/microllm2/microllm2-checkpoints/final_merged ...
[+] Loaded on cuda:0 dtype=torch.bfloat16 β starting MMLU
MMLU subjects: 0%| | 0/57 [00:00<?, ?it/s]
============================================================
[>] abstract_algebra (shots=5)
abstract_algebra: 0%| | 0/20 [00:00<?, ?it/s][A
abstract_algebra: 5%|β | 1/20 [00:01<00:21, 1.15s/it][A
abstract_algebra: 15%|ββ | 3/20 [00:01<00:05, 2.92it/s][A
abstract_algebra: 25%|βββ | 5/20 [00:01<00:02, 5.08it/s][A
abstract_algebra: 40%|ββββ | 8/20 [00:01<00:01, 8.67it/s][A
abstract_algebra: 50%|βββββ | 10/20 [00:01<00:00, 10.56it/s][A
abstract_algebra: 70%|βββββββ | 14/20 [00:01<00:00, 15.27it/s][A
abstract_algebra: 85%|βββββββββ | 17/20 [00:01<00:00, 17.18it/s][A
abstract_algebra: 100%|ββββββββββ| 20/20 [00:02<00:00, 17.57it/s][A
[A
MMLU subjects: 2%|β | 1/57 [00:03<03:29, 3.73s/it][=] abstract_algebra: 6/20 = 30.0% (running avg 30.0%)
============================================================
[>] anatomy (shots=5)
anatomy: 0%| | 0/20 [00:00<?, ?it/s][A
anatomy: 10%|β | 2/20 [00:00<00:01, 13.20it/s][A
anatomy: 30%|βββ | 6/20 [00:00<00:00, 25.74it/s][A
anatomy: 45%|βββββ | 9/20 [00:00<00:00, 24.91it/s][A
anatomy: 60%|ββββββ | 12/20 [00:00<00:00, 24.58it/s][A
anatomy: 75%|ββββββββ | 15/20 [00:00<00:00, 23.82it/s][A
anatomy: 90%|βββββββββ | 18/20 [00:00<00:00, 21.69it/s][A
[A
MMLU subjects: 4%|β | 2/57 [00:06<02:43, 2.97s/it][=] anatomy: 5/20 = 25.0% (running avg 27.5%)
============================================================
[>] astronomy (shots=5)
astronomy: 0%| | 0/20 [00:00<?, ?it/s][A
astronomy: 10%|β | 2/20 [00:00<00:01, 12.23it/s][A
astronomy: 20%|ββ | 4/20 [00:00<00:01, 14.44it/s][A
astronomy: 30%|βββ | 6/20 [00:00<00:00, 15.75it/s][A
astronomy: 40%|ββββ | 8/20 [00:00<00:00, 16.45it/s][A
astronomy: 50%|βββββ | 10/20 [00:00<00:00, 16.60it/s][A
astronomy: 60%|ββββββ | 12/20 [00:00<00:00, 17.09it/s][A
astronomy: 70%|βββββββ | 14/20 [00:00<00:00, 17.48it/s][A
astronomy: 85%|βββββββββ | 17/20 [00:00<00:00, 19.61it/s][A
astronomy: 95%|ββββββββββ| 19/20 [00:01<00:00, 19.15it/s][A
[A
MMLU subjects: 5%|β | 3/57 [00:08<02:23, 2.66s/it][=] astronomy: 7/20 = 35.0% (running avg 30.0%)
============================================================
[>] business_ethics (shots=5)
business_ethics: 0%| | 0/20 [00:00<?, ?it/s][A
business_ethics: 10%|β | 2/20 [00:00<00:01, 12.90it/s][A
business_ethics: 20%|ββ | 4/20 [00:00<00:01, 15.43it/s][A
business_ethics: 40%|ββββ | 8/20 [00:00<00:00, 24.39it/s][A
business_ethics: 55%|ββββββ | 11/20 [00:00<00:00, 24.16it/s][A
business_ethics: 70%|βββββββ | 14/20 [00:00<00:00, 23.61it/s][A
business_ethics: 85%|βββββββββ | 17/20 [00:00<00:00, 23.27it/s][A
business_ethics: 100%|ββββββββββ| 20/20 [00:00<00:00, 22.96it/s][A
[A
MMLU subjects: 7%|β | 4/57 [00:10<02:10, 2.45s/it][=] business_ethics: 6/20 = 30.0% (running avg 30.0%)
============================================================
[>] clinical_knowledge (shots=5)
clinical_knowledge: 0%| | 0/20 [00:00<?, ?it/s][A
clinical_knowledge: 10%|β | 2/20 [00:00<00:00, 18.14it/s][A
clinical_knowledge: 20%|ββ | 4/20 [00:00<00:00, 18.00it/s][A
clinical_knowledge: 30%|βββ | 6/20 [00:00<00:00, 17.94it/s][A
clinical_knowledge: 40%|ββββ | 8/20 [00:00<00:00, 17.50it/s][A
clinical_knowledge: 50%|βββββ | 10/20 [00:00<00:00, 17.77it/s][A
clinical_knowledge: 60%|ββββββ | 12/20 [00:00<00:00, 17.75it/s][A
clinical_knowledge: 75%|ββββββββ | 15/20 [00:00<00:00, 19.69it/s][A
clinical_knowledge: 90%|βββββββββ | 18/20 [00:00<00:00, 20.96it/s][A
[A
MMLU subjects: 9%|β | 5/57 [00:12<02:05, 2.42s/it][=] clinical_knowledge: 9/20 = 45.0% (running avg 33.0%)
============================================================
[>] college_biology (shots=5)
college_biology: 0%| | 0/20 [00:00<?, ?it/s][A
college_biology: 10%|β | 2/20 [00:00<00:01, 13.18it/s][A
college_biology: 20%|ββ | 4/20 [00:00<00:01, 15.56it/s][A
college_biology: 35%|ββββ | 7/20 [00:00<00:00, 19.08it/s][A
college_biology: 45%|βββββ | 9/20 [00:00<00:00, 18.60it/s][A
college_biology: 65%|βββββββ | 13/20 [00:00<00:00, 21.91it/s][A
college_biology: 80%|ββββββββ | 16/20 [00:00<00:00, 22.47it/s][A
college_biology: 95%|ββββββββββ| 19/20 [00:00<00:00, 22.98it/s][A
[A
MMLU subjects: 11%|β | 6/57 [00:15<01:59, 2.35s/it][=] college_biology: 9/20 = 45.0% (running avg 35.0%)
============================================================
[>] college_chemistry (shots=5)
college_chemistry: 0%| | 0/20 [00:00<?, ?it/s][A
college_chemistry: 10%|β | 2/20 [00:00<00:01, 12.84it/s][A
college_chemistry: 25%|βββ | 5/20 [00:00<00:00, 18.26it/s][A
college_chemistry: 40%|ββββ | 8/20 [00:00<00:00, 20.43it/s][A
college_chemistry: 55%|ββββββ | 11/20 [00:00<00:00, 19.37it/s][A
college_chemistry: 65%|βββββββ | 13/20 [00:00<00:00, 18.98it/s][A
college_chemistry: 85%|βββββββββ | 17/20 [00:00<00:00, 21.96it/s][A
[A
MMLU subjects: 12%|ββ | 7/57 [00:17<01:56, 2.33s/it][=] college_chemistry: 3/20 = 15.0% (running avg 32.1%)
============================================================
[>] college_computer_science (shots=5)
college_computer_science: 0%| | 0/20 [00:00<?, ?it/s][A
college_computer_science: 5%|β | 1/20 [00:00<00:02, 9.28it/s][A
college_computer_science: 15%|ββ | 3/20 [00:00<00:01, 13.08it/s][A
college_computer_science: 25%|βββ | 5/20 [00:00<00:01, 14.86it/s][A
college_computer_science: 35%|ββββ | 7/20 [00:00<00:00, 15.74it/s][A
college_computer_science: 45%|βββββ | 9/20 [00:00<00:00, 16.53it/s][A
college_computer_science: 55%|ββββββ | 11/20 [00:00<00:00, 17.01it/s][A
college_computer_science: 65%|βββββββ | 13/20 [00:00<00:00, 17.24it/s][A
college_computer_science: 75%|ββββββββ | 15/20 [00:00<00:00, 17.33it/s][A
college_computer_science: 85%|βββββββββ | 17/20 [00:01<00:00, 17.21it/s][A
college_computer_science: 95%|ββββββββββ| 19/20 [00:01<00:00, 17.23it/s][A
[A
MMLU subjects: 14%|ββ | 8/57 [00:19<01:55, 2.37s/it][=] college_computer_science: 9/20 = 45.0% (running avg 33.8%)
============================================================
[>] college_mathematics (shots=5)
college_mathematics: 0%| | 0/20 [00:00<?, ?it/s][A
college_mathematics: 10%|β | 2/20 [00:00<00:01, 12.83it/s][A
college_mathematics: 20%|ββ | 4/20 [00:00<00:01, 15.33it/s][A
college_mathematics: 35%|ββββ | 7/20 [00:00<00:00, 18.80it/s][A
college_mathematics: 60%|ββββββ | 12/20 [00:00<00:00, 26.76it/s][A
college_mathematics: 80%|ββββββββ | 16/20 [00:00<00:00, 26.72it/s][A
college_mathematics: 95%|ββββββββββ| 19/20 [00:00<00:00, 23.14it/s][A
[A
MMLU subjects: 16%|ββ | 9/57 [00:22<01:52, 2.34s/it][=] college_mathematics: 7/20 = 35.0% (running avg 33.9%)
============================================================
[>] college_medicine (shots=5)
college_medicine: 0%| | 0/20 [00:00<?, ?it/s][A
college_medicine: 10%|β | 2/20 [00:00<00:01, 17.54it/s][A
college_medicine: 20%|ββ | 4/20 [00:00<00:00, 17.37it/s][A
college_medicine: 30%|βββ | 6/20 [00:00<00:00, 17.41it/s][A
college_medicine: 45%|βββββ | 9/20 [00:00<00:00, 19.51it/s][A
college_medicine: 55%|ββββββ | 11/20 [00:00<00:00, 18.71it/s][A
college_medicine: 65%|βββββββ | 13/20 [00:00<00:00, 18.52it/s][A
college_medicine: 75%|ββββββββ | 15/20 [00:00<00:00, 18.49it/s][A
college_medicine: 100%|ββββββββββ| 20/20 [00:00<00:00, 25.98it/s][A
[A
MMLU subjects: 18%|ββ | 10/57 [00:24<01:47, 2.28s/it][=] college_medicine: 6/20 = 30.0% (running avg 33.5%)
============================================================
[>] college_physics (shots=5)
college_physics: 0%| | 0/20 [00:00<?, ?it/s][A
college_physics: 10%|β | 2/20 [00:00<00:01, 12.81it/s][A
college_physics: 20%|ββ | 4/20 [00:00<00:01, 15.22it/s][A
college_physics: 40%|ββββ | 8/20 [00:00<00:00, 21.09it/s][A
college_physics: 60%|ββββββ | 12/20 [00:00<00:00, 23.72it/s][A
college_physics: 80%|ββββββββ | 16/20 [00:00<00:00, 27.98it/s][A
college_physics: 95%|ββββββββββ| 19/20 [00:00<00:00, 26.64it/s][A
[A
MMLU subjects: 19%|ββ | 11/57 [00:26<01:42, 2.23s/it][=] college_physics: 3/20 = 15.0% (running avg 31.8%)
============================================================
[>] computer_security (shots=5)
computer_security: 0%| | 0/20 [00:00<?, ?it/s][A
computer_security: 10%|β | 2/20 [00:00<00:00, 18.55it/s][A
computer_security: 20%|ββ | 4/20 [00:00<00:00, 18.25it/s][A
computer_security: 35%|ββββ | 7/20 [00:00<00:00, 20.93it/s][A
computer_security: 55%|ββββββ | 11/20 [00:00<00:00, 24.12it/s][A
computer_security: 70%|βββββββ | 14/20 [00:00<00:00, 23.97it/s][A
computer_security: 90%|βββββββββ | 18/20 [00:00<00:00, 28.34it/s][A
[A
MMLU subjects: 21%|ββ | 12/57 [00:28<01:37, 2.17s/it][=] computer_security: 6/20 = 30.0% (running avg 31.7%)
============================================================
[>] conceptual_physics (shots=5)
conceptual_physics: 0%| | 0/20 [00:00<?, ?it/s][A
conceptual_physics: 5%|β | 1/20 [00:00<00:02, 9.44it/s][A
conceptual_physics: 15%|ββ | 3/20 [00:00<00:01, 14.31it/s][A
conceptual_physics: 30%|βββ | 6/20 [00:00<00:00, 18.53it/s][A
conceptual_physics: 45%|βββββ | 9/20 [00:00<00:00, 20.38it/s][A
conceptual_physics: 65%|βββββββ | 13/20 [00:00<00:00, 22.74it/s][A
conceptual_physics: 85%|βββββββββ | 17/20 [00:00<00:00, 24.44it/s][A
conceptual_physics: 100%|ββββββββββ| 20/20 [00:00<00:00, 24.12it/s][A
[A
MMLU subjects: 23%|βββ | 13/57 [00:30<01:37, 2.22s/it][=] conceptual_physics: 1/20 = 5.0% (running avg 29.6%)
============================================================
[>] econometrics (shots=5)
econometrics: 0%| | 0/20 [00:00<?, ?it/s][A
econometrics: 20%|ββ | 4/20 [00:00<00:00, 27.82it/s][A
econometrics: 35%|ββββ | 7/20 [00:00<00:00, 21.59it/s][A
econometrics: 55%|ββββββ | 11/20 [00:00<00:00, 23.65it/s][A
econometrics: 80%|ββββββββ | 16/20 [00:00<00:00, 28.86it/s][A
econometrics: 95%|ββββββββββ| 19/20 [00:00<00:00, 26.62it/s][A
[A
MMLU subjects: 25%|βββ | 14/57 [00:32<01:33, 2.18s/it][=] econometrics: 6/20 = 30.0% (running avg 29.6%)
============================================================
[>] electrical_engineering (shots=5)
electrical_engineering: 0%| | 0/20 [00:00<?, ?it/s][A
electrical_engineering: 10%|β | 2/20 [00:00<00:01, 17.36it/s][A
electrical_engineering: 30%|βββ | 6/20 [00:00<00:00, 23.79it/s][A
electrical_engineering: 45%|βββββ | 9/20 [00:00<00:00, 20.65it/s][A
electrical_engineering: 65%|βββββββ | 13/20 [00:00<00:00, 25.92it/s][A
electrical_engineering: 95%|ββββββββββ| 19/20 [00:00<00:00, 35.89it/s][A
[A
MMLU subjects: 26%|βββ | 15/57 [00:34<01:30, 2.15s/it][=] electrical_engineering: 4/20 = 20.0% (running avg 29.0%)
============================================================
[>] elementary_mathematics (shots=5)
elementary_mathematics: 0%| | 0/20 [00:00<?, ?it/s][A
elementary_mathematics: 10%|β | 2/20 [00:00<00:01, 12.90it/s][A
elementary_mathematics: 20%|ββ | 4/20 [00:00<00:01, 15.49it/s][A
elementary_mathematics: 30%|βββ | 6/20 [00:00<00:00, 16.54it/s][A
elementary_mathematics: 50%|βββββ | 10/20 [00:00<00:00, 23.85it/s][A
elementary_mathematics: 70%|βββββββ | 14/20 [00:00<00:00, 28.23it/s][A
elementary_mathematics: 85%|βββββββββ | 17/20 [00:00<00:00, 26.39it/s][A
[A
MMLU subjects: 28%|βββ | 16/57 [00:36<01:26, 2.11s/it][=] elementary_mathematics: 6/20 = 30.0% (running avg 29.1%)
============================================================
[>] formal_logic (shots=5)
formal_logic: 0%| | 0/20 [00:00<?, ?it/s][A
formal_logic: 10%|β | 2/20 [00:00<00:01, 13.10it/s][A
formal_logic: 20%|ββ | 4/20 [00:00<00:01, 15.61it/s][A
formal_logic: 30%|βββ | 6/20 [00:00<00:00, 16.51it/s][A
formal_logic: 45%|βββββ | 9/20 [00:00<00:00, 19.07it/s][A
formal_logic: 65%|βββββββ | 13/20 [00:00<00:00, 22.24it/s][A
formal_logic: 80%|ββββββββ | 16/20 [00:00<00:00, 22.59it/s][A
formal_logic: 95%|ββββββββββ| 19/20 [00:00<00:00, 22.94it/s][A
[A
MMLU subjects: 30%|βββ | 17/57 [00:39<01:24, 2.12s/it][=] formal_logic: 2/20 = 10.0% (running avg 27.9%)
============================================================
[>] global_facts (shots=5)
global_facts: 0%| | 0/20 [00:00<?, ?it/s][A
global_facts: 10%|β | 2/20 [00:00<00:01, 17.09it/s][A
global_facts: 30%|βββ | 6/20 [00:00<00:00, 28.49it/s][A
global_facts: 50%|βββββ | 10/20 [00:00<00:00, 32.74it/s][A
global_facts: 70%|βββββββ | 14/20 [00:00<00:00, 34.73it/s][A
[A
MMLU subjects: 32%|ββββ | 18/57 [00:40<01:18, 2.00s/it][=] global_facts: 7/20 = 35.0% (running avg 28.3%)
============================================================
[>] high_school_biology (shots=5)
high_school_biology: 0%| | 0/20 [00:00<?, ?it/s][A
high_school_biology: 15%|ββ | 3/20 [00:00<00:00, 24.19it/s][A
high_school_biology: 35%|ββββ | 7/20 [00:00<00:00, 31.92it/s][A
high_school_biology: 55%|ββββββ | 11/20 [00:00<00:00, 26.11it/s][A
high_school_biology: 75%|ββββββββ | 15/20 [00:00<00:00, 26.59it/s][A
high_school_biology: 95%|ββββββββββ| 19/20 [00:00<00:00, 29.76it/s][A
[A
MMLU subjects: 33%|ββββ | 19/57 [00:43<01:17, 2.04s/it][=] high_school_biology: 9/20 = 45.0% (running avg 29.2%)
============================================================
[>] high_school_chemistry (shots=5)
high_school_chemistry: 0%| | 0/20 [00:00<?, ?it/s][A
high_school_chemistry: 10%|β | 2/20 [00:00<00:01, 17.03it/s][A
high_school_chemistry: 40%|ββββ | 8/20 [00:00<00:00, 40.46it/s][A
high_school_chemistry: 65%|βββββββ | 13/20 [00:00<00:00, 40.38it/s][A
high_school_chemistry: 90%|βββββββββ | 18/20 [00:00<00:00, 40.22it/s][A
[A
MMLU subjects: 35%|ββββ | 20/57 [00:44<01:13, 1.99s/it][=] high_school_chemistry: 7/20 = 35.0% (running avg 29.5%)
============================================================
[>] high_school_computer_science (shots=5)
high_school_computer_science: 0%| | 0/20 [00:00<?, ?it/s][A
high_school_computer_science: 5%|β | 1/20 [00:00<00:02, 8.41it/s][A
high_school_computer_science: 25%|βββ | 5/20 [00:00<00:00, 20.00it/s][A
high_school_computer_science: 45%|βββββ | 9/20 [00:00<00:00, 26.45it/s][A
high_school_computer_science: 60%|ββββββ | 12/20 [00:00<00:00, 22.36it/s][A
high_school_computer_science: 80%|ββββββββ | 16/20 [00:00<00:00, 23.75it/s][A
high_school_computer_science: 95%|ββββββββββ| 19/20 [00:00<00:00, 23.32it/s][A
[A
MMLU subjects: 37%|ββββ | 21/57 [00:46<01:12, 2.01s/it][=] high_school_computer_science: 7/20 = 35.0% (running avg 29.8%)
============================================================
[>] high_school_european_history (shots=5)
high_school_european_history: 0%| | 0/20 [00:00<?, ?it/s][A
high_school_european_history: 15%|ββ | 3/20 [00:00<00:00, 29.59it/s][A
high_school_european_history: 40%|ββββ | 8/20 [00:00<00:00, 39.83it/s][A
high_school_european_history: 65%|βββββββ | 13/20 [00:00<00:00, 42.99it/s][A
high_school_european_history: 90%|βββββββββ | 18/20 [00:00<00:00, 44.36it/s][A
[A
MMLU subjects: 39%|ββββ | 22/57 [00:48<01:07, 1.94s/it][=] high_school_european_history: 4/20 = 20.0% (running avg 29.3%)
============================================================
[>] high_school_geography (shots=5)
high_school_geography: 0%| | 0/20 [00:00<?, ?it/s][A
high_school_geography: 15%|ββ | 3/20 [00:00<00:00, 22.09it/s][A
high_school_geography: 45%|βββββ | 9/20 [00:00<00:00, 34.56it/s][A
high_school_geography: 65%|βββββββ | 13/20 [00:00<00:00, 30.54it/s][A
high_school_geography: 85%|βββββββββ | 17/20 [00:00<00:00, 32.49it/s][A
[A
MMLU subjects: 40%|ββββ | 23/57 [00:50<01:05, 1.91s/it][=] high_school_geography: 5/20 = 25.0% (running avg 29.1%)
============================================================
[>] high_school_government_and_politics (shots=5)
high_school_government_and_politics: 0%| | 0/20 [00:00<?, ?it/s][A
high_school_government_and_politics: 10%|β | 2/20 [00:00<00:01, 14.50it/s][A
high_school_government_and_politics: 30%|βββ | 6/20 [00:00<00:00, 22.09it/s][A
high_school_government_and_politics: 60%|ββββββ | 12/20 [00:00<00:00, 35.58it/s][A
high_school_government_and_politics: 90%|βββββββββ | 18/20 [00:00<00:00, 38.62it/s][A
[A
MMLU subjects: 42%|βββββ | 24/57 [00:52<01:02, 1.89s/it][=] high_school_government_and_politics: 4/20 = 20.0% (running avg 28.7%)
============================================================
[>] high_school_macroeconomics (shots=5)
high_school_macroeconomics: 0%| | 0/20 [00:00<?, ?it/s][A
high_school_macroeconomics: 20%|ββ | 4/20 [00:00<00:00, 39.59it/s][A
high_school_macroeconomics: 40%|ββββ | 8/20 [00:00<00:00, 38.27it/s][A
high_school_macroeconomics: 60%|ββββββ | 12/20 [00:00<00:00, 32.61it/s][A
high_school_macroeconomics: 90%|βββββββββ | 18/20 [00:00<00:00, 37.33it/s][A
[A
MMLU subjects: 44%|βββββ | 25/57 [00:54<00:58, 1.84s/it][=] high_school_macroeconomics: 0/20 = 0.0% (running avg 27.6%)
============================================================
[>] high_school_mathematics (shots=5)
high_school_mathematics: 0%| | 0/20 [00:00<?, ?it/s][A
high_school_mathematics: 10%|β | 2/20 [00:00<00:01, 12.89it/s][A
high_school_mathematics: 30%|βββ | 6/20 [00:00<00:00, 21.24it/s][A
high_school_mathematics: 60%|ββββββ | 12/20 [00:00<00:00, 35.06it/s][A
high_school_mathematics: 90%|βββββββββ | 18/20 [00:00<00:00, 43.42it/s][A
[A
MMLU subjects: 46%|βββββ | 26/57 [00:55<00:57, 1.85s/it][=] high_school_mathematics: 4/20 = 20.0% (running avg 27.3%)
============================================================
[>] high_school_microeconomics (shots=5)
high_school_microeconomics: 0%| | 0/20 [00:00<?, ?it/s][A
high_school_microeconomics: 15%|ββ | 3/20 [00:00<00:00, 18.39it/s][A
high_school_microeconomics: 35%|ββββ | 7/20 [00:00<00:00, 27.64it/s][A
high_school_microeconomics: 55%|ββββββ | 11/20 [00:00<00:00, 31.83it/s][A
high_school_microeconomics: 75%|ββββββββ | 15/20 [00:00<00:00, 33.78it/s][A
high_school_microeconomics: 95%|ββββββββββ| 19/20 [00:00<00:00, 31.23it/s][A
[A
MMLU subjects: 47%|βββββ | 27/57 [00:57<00:55, 1.86s/it][=] high_school_microeconomics: 7/20 = 35.0% (running avg 27.6%)
============================================================
[>] high_school_physics (shots=5)
high_school_physics: 0%| | 0/20 [00:00<?, ?it/s][A
high_school_physics: 10%|β | 2/20 [00:00<00:00, 18.31it/s][A
high_school_physics: 30%|βββ | 6/20 [00:00<00:00, 29.27it/s][A
high_school_physics: 50%|βββββ | 10/20 [00:00<00:00, 28.30it/s][A
high_school_physics: 70%|βββββββ | 14/20 [00:00<00:00, 31.21it/s][A
high_school_physics: 90%|βββββββββ | 18/20 [00:00<00:00, 33.23it/s][A
[A
MMLU subjects: 49%|βββββ | 28/57 [00:59<00:55, 1.90s/it][=] high_school_physics: 4/20 = 20.0% (running avg 27.3%)
============================================================
[>] high_school_psychology (shots=5)
high_school_psychology: 0%| | 0/20 [00:00<?, ?it/s][A
high_school_psychology: 20%|ββ | 4/20 [00:00<00:00, 28.00it/s][A
high_school_psychology: 40%|ββββ | 8/20 [00:00<00:00, 33.07it/s][A
high_school_psychology: 60%|ββββββ | 12/20 [00:00<00:00, 34.99it/s][A
high_school_psychology: 80%|ββββββββ | 16/20 [00:00<00:00, 35.93it/s][A
high_school_psychology: 100%|ββββββββββ| 20/20 [00:00<00:00, 36.42it/s][A
[A
MMLU subjects: 51%|βββββ | 29/57 [01:01<00:52, 1.87s/it][=] high_school_psychology: 5/20 = 25.0% (running avg 27.2%)
============================================================
[>] high_school_statistics (shots=5)
high_school_statistics: 0%| | 0/20 [00:00<?, ?it/s][A
high_school_statistics: 10%|β | 2/20 [00:00<00:01, 12.71it/s][A
high_school_statistics: 25%|βββ | 5/20 [00:00<00:00, 18.45it/s][A
high_school_statistics: 35%|ββββ | 7/20 [00:00<00:00, 18.23it/s][A
high_school_statistics: 45%|βββββ | 9/20 [00:00<00:00, 18.11it/s][A
high_school_statistics: 55%|ββββββ | 11/20 [00:00<00:00, 17.87it/s][A
high_school_statistics: 65%|βββββββ | 13/20 [00:00<00:00, 17.72it/s][A
high_school_statistics: 75%|ββββββββ | 15/20 [00:00<00:00, 17.67it/s][A
high_school_statistics: 85%|βββββββββ | 17/20 [00:00<00:00, 17.60it/s][A
high_school_statistics: 95%|ββββββββββ| 19/20 [00:01<00:00, 17.72it/s][A
[A
MMLU subjects: 53%|ββββββ | 30/57 [01:04<00:54, 2.04s/it][=] high_school_statistics: 8/20 = 40.0% (running avg 27.7%)
============================================================
[>] high_school_us_history (shots=5)
high_school_us_history: 0%| | 0/20 [00:00<?, ?it/s][A
high_school_us_history: 20%|ββ | 4/20 [00:00<00:00, 33.99it/s][A
high_school_us_history: 45%|βββββ | 9/20 [00:00<00:00, 42.16it/s][A
high_school_us_history: 70%|βββββββ | 14/20 [00:00<00:00, 44.91it/s][A
high_school_us_history: 95%|ββββββββββ| 19/20 [00:00<00:00, 45.94it/s][A
[A
MMLU subjects: 54%|ββββββ | 31/57 [01:05<00:49, 1.92s/it][=] high_school_us_history: 4/20 = 20.0% (running avg 27.4%)
============================================================
[>] high_school_world_history (shots=5)
high_school_world_history: 0%| | 0/20 [00:00<?, ?it/s][A
high_school_world_history: 15%|ββ | 3/20 [00:00<00:00, 20.31it/s][A
high_school_world_history: 40%|ββββ | 8/20 [00:00<00:00, 32.41it/s][A
high_school_world_history: 65%|βββββββ | 13/20 [00:00<00:00, 39.35it/s][A
high_school_world_history: 90%|βββββββββ | 18/20 [00:00<00:00, 43.13it/s][A
[A
MMLU subjects: 56%|ββββββ | 32/57 [01:07<00:46, 1.86s/it][=] high_school_world_history: 7/20 = 35.0% (running avg 27.7%)
============================================================
[>] human_aging (shots=5)
human_aging: 0%| | 0/20 [00:00<?, ?it/s][A
human_aging: 5%|β | 1/20 [00:00<00:01, 9.91it/s][A
human_aging: 20%|ββ | 4/20 [00:00<00:00, 18.51it/s][A
human_aging: 40%|ββββ | 8/20 [00:00<00:00, 23.30it/s][A
human_aging: 55%|ββββββ | 11/20 [00:00<00:00, 23.62it/s][A
human_aging: 70%|βββββββ | 14/20 [00:00<00:00, 23.48it/s][A
human_aging: 90%|βββββββββ | 18/20 [00:00<00:00, 25.27it/s][A
[A
MMLU subjects: 58%|ββββββ | 33/57 [01:09<00:45, 1.90s/it][=] human_aging: 8/20 = 40.0% (running avg 28.0%)
============================================================
[>] human_sexuality (shots=5)
human_sexuality: 0%| | 0/20 [00:00<?, ?it/s][A
human_sexuality: 10%|β | 2/20 [00:00<00:01, 17.26it/s][A
human_sexuality: 20%|ββ | 4/20 [00:00<00:00, 17.97it/s][A
human_sexuality: 50%|βββββ | 10/20 [00:00<00:00, 31.12it/s][A
human_sexuality: 70%|βββββββ | 14/20 [00:00<00:00, 33.83it/s][A
human_sexuality: 90%|βββββββββ | 18/20 [00:00<00:00, 35.24it/s][A
[A
MMLU subjects: 60%|ββββββ | 34/57 [01:11<00:44, 1.94s/it][=] human_sexuality: 3/20 = 15.0% (running avg 27.6%)
============================================================
[>] international_law (shots=5)
international_law: 0%| | 0/20 [00:00<?, ?it/s][A
international_law: 10%|β | 2/20 [00:00<00:01, 13.41it/s][A
international_law: 30%|βββ | 6/20 [00:00<00:00, 21.45it/s][A
international_law: 60%|ββββββ | 12/20 [00:00<00:00, 34.70it/s][A
international_law: 80%|ββββββββ | 16/20 [00:00<00:00, 28.56it/s][A
international_law: 100%|ββββββββββ| 20/20 [00:00<00:00, 25.67it/s][A
[A
MMLU subjects: 61%|βββββββ | 35/57 [01:13<00:43, 1.99s/it][=] international_law: 7/20 = 35.0% (running avg 27.9%)
============================================================
[>] jurisprudence (shots=5)
jurisprudence: 0%| | 0/20 [00:00<?, ?it/s][A
jurisprudence: 10%|β | 2/20 [00:00<00:01, 17.11it/s][A
jurisprudence: 40%|ββββ | 8/20 [00:00<00:00, 32.97it/s][A
jurisprudence: 60%|ββββββ | 12/20 [00:00<00:00, 34.56it/s][A
jurisprudence: 90%|βββββββββ | 18/20 [00:00<00:00, 37.91it/s][A
[A
MMLU subjects: 63%|βββββββ | 36/57 [01:15<00:40, 1.92s/it][=] jurisprudence: 8/20 = 40.0% (running avg 28.2%)
============================================================
[>] logical_fallacies (shots=5)
logical_fallacies: 0%| | 0/20 [00:00<?, ?it/s][A
logical_fallacies: 20%|ββ | 4/20 [00:00<00:00, 38.49it/s][A
logical_fallacies: 50%|βββββ | 10/20 [00:00<00:00, 49.03it/s][A
logical_fallacies: 75%|ββββββββ | 15/20 [00:00<00:00, 38.28it/s][A
logical_fallacies: 100%|ββββββββββ| 20/20 [00:00<00:00, 38.86it/s][A
[A
MMLU subjects: 65%|βββββββ | 37/57 [01:17<00:37, 1.85s/it][=] logical_fallacies: 7/20 = 35.0% (running avg 28.4%)
============================================================
[>] machine_learning (shots=5)
Downloading data: 0%| | 0.00/5.25k [00:00<?, ?B/s][A
Downloading data: 100%|ββββββββββ| 5.25k/5.25k [00:00<00:00, 19.1kB/s][A
Downloading data: 100%|ββββββββββ| 5.25k/5.25k [00:00<00:00, 19.0kB/s]
Generating test split: 0%| | 0/112 [00:00<?, ? examples/s][A
Generating test split: 100%|ββββββββββ| 112/112 [00:00<00:00, 19509.20 examples/s]
Generating validation split: 0%| | 0/11 [00:00<?, ? examples/s][A
Generating validation split: 100%|ββββββββββ| 11/11 [00:00<00:00, 3115.91 examples/s]
Generating dev split: 0%| | 0/5 [00:00<?, ? examples/s][A
Generating dev split: 100%|ββββββββββ| 5/5 [00:00<00:00, 1538.18 examples/s]
machine_learning: 0%| | 0/20 [00:00<?, ?it/s][A
machine_learning: 10%|β | 2/20 [00:00<00:01, 13.64it/s][A
machine_learning: 30%|βββ | 6/20 [00:00<00:00, 25.60it/s][A
machine_learning: 50%|βββββ | 10/20 [00:00<00:00, 30.54it/s][A
machine_learning: 80%|ββββββββ | 16/20 [00:00<00:00, 35.58it/s][A
machine_learning: 100%|ββββββββββ| 20/20 [00:00<00:00, 32.40it/s][A
[A
MMLU subjects: 67%|βββββββ | 38/57 [01:19<00:38, 2.03s/it][=] machine_learning: 10/20 = 50.0% (running avg 28.9%)
============================================================
[>] management (shots=5)
Downloading data: 0%| | 0.00/14.7k [00:00<?, ?B/s][A
Downloading data: 100%|ββββββββββ| 14.7k/14.7k [00:00<00:00, 71.1kB/s][A
Downloading data: 100%|ββββββββββ| 14.7k/14.7k [00:00<00:00, 70.8kB/s]
Downloading data: 0%| | 0.00/4.50k [00:00<?, ?B/s][A
Downloading data: 100%|ββββββββββ| 4.50k/4.50k [00:00<00:00, 25.8kB/s][A
Downloading data: 100%|ββββββββββ| 4.50k/4.50k [00:00<00:00, 25.7kB/s]
Downloading data: 0%| | 0.00/3.61k [00:00<?, ?B/s][A
Downloading data: 100%|ββββββββββ| 3.61k/3.61k [00:00<00:00, 19.8kB/s][A
Downloading data: 100%|ββββββββββ| 3.61k/3.61k [00:00<00:00, 19.7kB/s]
Generating test split: 0%| | 0/103 [00:00<?, ? examples/s][A
Generating test split: 100%|ββββββββββ| 103/103 [00:00<00:00, 23526.29 examples/s]
Generating validation split: 0%| | 0/11 [00:00<?, ? examples/s][A
Generating validation split: 100%|ββββββββββ| 11/11 [00:00<00:00, 3129.65 examples/s]
Generating dev split: 0%| | 0/5 [00:00<?, ? examples/s][A
Generating dev split: 100%|ββββββββββ| 5/5 [00:00<00:00, 1433.95 examples/s]
management: 0%| | 0/20 [00:00<?, ?it/s][A
management: 10%|β | 2/20 [00:00<00:01, 14.08it/s][A
management: 20%|ββ | 4/20 [00:00<00:00, 16.16it/s][A
management: 35%|ββββ | 7/20 [00:00<00:00, 19.49it/s][A
management: 45%|βββββ | 9/20 [00:00<00:00, 18.65it/s][A
management: 60%|ββββββ | 12/20 [00:00<00:00, 20.56it/s][A
management: 75%|ββββββββ | 15/20 [00:00<00:00, 19.77it/s][A
[A
MMLU subjects: 68%|βββββββ | 39/57 [01:23<00:45, 2.55s/it][=] management: 4/20 = 20.0% (running avg 28.7%)
============================================================
[>] marketing (shots=5)
Downloading data: 0%| | 0.00/37.3k [00:00<?, ?B/s][A
Downloading data: 100%|ββββββββββ| 37.3k/37.3k [00:00<00:00, 216kB/s][A
Downloading data: 100%|ββββββββββ| 37.3k/37.3k [00:00<00:00, 214kB/s]
Downloading data: 0%| | 0.00/8.21k [00:00<?, ?B/s][A
Downloading data: 100%|ββββββββββ| 8.21k/8.21k [00:00<00:00, 40.3kB/s][A
Downloading data: 100%|ββββββββββ| 8.21k/8.21k [00:00<00:00, 40.1kB/s]
Downloading data: 0%| | 0.00/4.28k [00:00<?, ?B/s][A
Downloading data: 100%|ββββββββββ| 4.28k/4.28k [00:00<00:00, 13.5kB/s][A
Downloading data: 100%|ββββββββββ| 4.28k/4.28k [00:00<00:00, 13.5kB/s]
Generating test split: 0%| | 0/234 [00:00<?, ? examples/s][A
Generating test split: 100%|ββββββββββ| 234/234 [00:00<00:00, 47706.56 examples/s]
Generating validation split: 0%| | 0/25 [00:00<?, ? examples/s][A
Generating validation split: 100%|ββββββββββ| 25/25 [00:00<00:00, 6806.73 examples/s]
Generating dev split: 0%| | 0/5 [00:00<?, ? examples/s][A
Generating dev split: 100%|ββββββββββ| 5/5 [00:00<00:00, 1449.31 examples/s]
marketing: 0%| | 0/20 [00:00<?, ?it/s][A
marketing: 25%|βββ | 5/20 [00:00<00:00, 43.68it/s][A
marketing: 55%|ββββββ | 11/20 [00:00<00:00, 52.50it/s][A
marketing: 85%|βββββββββ | 17/20 [00:00<00:00, 55.39it/s][A
[A
MMLU subjects: 70%|βββββββ | 40/57 [01:26<00:47, 2.77s/it][=] marketing: 7/20 = 35.0% (running avg 28.9%)
============================================================
[>] medical_genetics (shots=5)
Downloading data: 0%| | 0.00/16.4k [00:00<?, ?B/s][A
Downloading data: 100%|ββββββββββ| 16.4k/16.4k [00:00<00:00, 84.6kB/s][A
Downloading data: 100%|ββββββββββ| 16.4k/16.4k [00:00<00:00, 84.2kB/s]
Downloading data: 0%| | 0.00/5.63k [00:00<?, ?B/s][A
Downloading data: 100%|ββββββββββ| 5.63k/5.63k [00:00<00:00, 25.0kB/s][A
Downloading data: 100%|ββββββββββ| 5.63k/5.63k [00:00<00:00, 24.9kB/s]
Downloading data: 0%| | 0.00/3.77k [00:00<?, ?B/s][A
Downloading data: 100%|ββββββββββ| 3.77k/3.77k [00:00<00:00, 22.5kB/s][A
Downloading data: 100%|ββββββββββ| 3.77k/3.77k [00:00<00:00, 22.4kB/s]
Generating test split: 0%| | 0/100 [00:00<?, ? examples/s][A
Generating test split: 100%|ββββββββββ| 100/100 [00:00<00:00, 23050.69 examples/s]
Generating validation split: 0%| | 0/11 [00:00<?, ? examples/s][A
Generating validation split: 100%|ββββββββββ| 11/11 [00:00<00:00, 3044.36 examples/s]
Generating dev split: 0%| | 0/5 [00:00<?, ? examples/s][A
Generating dev split: 100%|ββββββββββ| 5/5 [00:00<00:00, 1133.23 examples/s]
medical_genetics: 0%| | 0/20 [00:00<?, ?it/s][A
medical_genetics: 15%|ββ | 3/20 [00:00<00:00, 25.31it/s][A
medical_genetics: 50%|βββββ | 10/20 [00:00<00:00, 45.69it/s][A
medical_genetics: 75%|ββββββββ | 15/20 [00:00<00:00, 42.92it/s][A
[A
MMLU subjects: 72%|ββββββββ | 41/57 [01:29<00:47, 2.94s/it][=] medical_genetics: 8/20 = 40.0% (running avg 29.1%)
============================================================
[>] miscellaneous (shots=5)
Downloading data: 0%| | 0.00/98.6k [00:00<?, ?B/s][A
Downloading data: 100%|ββββββββββ| 98.6k/98.6k [00:00<00:00, 387kB/s][A
Downloading data: 100%|ββββββββββ| 98.6k/98.6k [00:00<00:00, 386kB/s]
Downloading data: 0%| | 0.00/13.2k [00:00<?, ?B/s][A
Downloading data: 100%|ββββββββββ| 13.2k/13.2k [00:00<00:00, 73.2kB/s][A
Downloading data: 100%|ββββββββββ| 13.2k/13.2k [00:00<00:00, 72.8kB/s]
Downloading data: 0%| | 0.00/3.37k [00:00<?, ?B/s][A
Downloading data: 100%|ββββββββββ| 3.37k/3.37k [00:00<00:00, 18.5kB/s][A
Downloading data: 100%|ββββββββββ| 3.37k/3.37k [00:00<00:00, 18.4kB/s]
Generating test split: 0%| | 0/783 [00:00<?, ? examples/s][A
Generating test split: 100%|ββββββββββ| 783/783 [00:00<00:00, 137394.47 examples/s]
Generating validation split: 0%| | 0/86 [00:00<?, ? examples/s][A
Generating validation split: 100%|ββββββββββ| 86/86 [00:00<00:00, 21921.01 examples/s]
Generating dev split: 0%| | 0/5 [00:00<?, ? examples/s][A
Generating dev split: 100%|ββββββββββ| 5/5 [00:00<00:00, 1380.52 examples/s]
miscellaneous: 0%| | 0/20 [00:00<?, ?it/s][A
miscellaneous: 15%|ββ | 3/20 [00:00<00:00, 26.43it/s][A
miscellaneous: 35%|ββββ | 7/20 [00:00<00:00, 33.14it/s][A
miscellaneous: 55%|ββββββ | 11/20 [00:00<00:00, 23.92it/s][A
miscellaneous: 70%|βββββββ | 14/20 [00:00<00:00, 23.88it/s][A
miscellaneous: 90%|βββββββββ | 18/20 [00:00<00:00, 28.17it/s][A
[A
MMLU subjects: 74%|ββββββββ | 42/57 [01:33<00:46, 3.09s/it][=] miscellaneous: 6/20 = 30.0% (running avg 29.2%)
============================================================
[>] moral_disputes (shots=5)
Downloading data: 0%| | 0.00/60.9k [00:00<?, ?B/s][A
Downloading data: 100%|ββββββββββ| 60.9k/60.9k [00:00<00:00, 299kB/s][A
Downloading data: 100%|ββββββββββ| 60.9k/60.9k [00:00<00:00, 298kB/s]
Downloading data: 0%| | 0.00/10.7k [00:00<?, ?B/s][A
Downloading data: 100%|ββββββββββ| 10.7k/10.7k [00:00<00:00, 51.6kB/s][A
Downloading data: 100%|ββββββββββ| 10.7k/10.7k [00:00<00:00, 51.4kB/s]
Downloading data: 0%| | 0.00/4.41k [00:00<?, ?B/s][A
Downloading data: 100%|ββββββββββ| 4.41k/4.41k [00:00<00:00, 26.7kB/s][A
Downloading data: 100%|ββββββββββ| 4.41k/4.41k [00:00<00:00, 26.5kB/s]
Generating test split: 0%| | 0/346 [00:00<?, ? examples/s][A
Generating test split: 100%|ββββββββββ| 346/346 [00:00<00:00, 62921.83 examples/s]
Generating validation split: 0%| | 0/38 [00:00<?, ? examples/s][A
Generating validation split: 100%|ββββββββββ| 38/38 [00:00<00:00, 9795.56 examples/s]
Generating dev split: 0%| | 0/5 [00:00<?, ? examples/s][A
Generating dev split: 100%|ββββββββββ| 5/5 [00:00<00:00, 1382.98 examples/s]
moral_disputes: 0%| | 0/20 [00:00<?, ?it/s][A
moral_disputes: 25%|βββ | 5/20 [00:00<00:00, 43.64it/s][A
moral_disputes: 50%|βββββ | 10/20 [00:00<00:00, 41.80it/s][A
moral_disputes: 80%|ββββββββ | 16/20 [00:00<00:00, 49.01it/s][A
[A
MMLU subjects: 75%|ββββββββ | 43/57 [01:36<00:43, 3.11s/it][=] moral_disputes: 4/20 = 20.0% (running avg 29.0%)
============================================================
[>] moral_scenarios (shots=5)
Downloading data: 0%| | 0.00/89.8k [00:00<?, ?B/s][A
Downloading data: 100%|ββββββββββ| 89.8k/89.8k [00:00<00:00, 502kB/s][A
Downloading data: 100%|ββββββββββ| 89.8k/89.8k [00:00<00:00, 499kB/s]
Downloading data: 0%| | 0.00/14.9k [00:00<?, ?B/s][A
Downloading data: 100%|ββββββββββ| 14.9k/14.9k [00:00<00:00, 97.2kB/s][A
Downloading data: 100%|ββββββββββ| 14.9k/14.9k [00:00<00:00, 96.7kB/s]
Downloading data: 0%| | 0.00/5.14k [00:00<?, ?B/s][A
Downloading data: 100%|ββββββββββ| 5.14k/5.14k [00:00<00:00, 27.5kB/s][A
Downloading data: 100%|ββββββββββ| 5.14k/5.14k [00:00<00:00, 27.4kB/s]
Generating test split: 0%| | 0/895 [00:00<?, ? examples/s][A
Generating test split: 100%|ββββββββββ| 895/895 [00:00<00:00, 133752.66 examples/s]
Generating validation split: 0%| | 0/100 [00:00<?, ? examples/s][A
Generating validation split: 100%|ββββββββββ| 100/100 [00:00<00:00, 20927.57 examples/s]
Generating dev split: 0%| | 0/5 [00:00<?, ? examples/s][A
Generating dev split: 100%|ββββββββββ| 5/5 [00:00<00:00, 1409.47 examples/s]
moral_scenarios: 0%| | 0/20 [00:00<?, ?it/s][A
moral_scenarios: 15%|ββ | 3/20 [00:00<00:00, 24.61it/s][A
moral_scenarios: 35%|ββββ | 7/20 [00:00<00:00, 31.12it/s][A
moral_scenarios: 60%|ββββββ | 12/20 [00:00<00:00, 34.85it/s][A
moral_scenarios: 80%|ββββββββ | 16/20 [00:00<00:00, 35.45it/s][A
[A
MMLU subjects: 77%|ββββββββ | 44/57 [01:39<00:40, 3.15s/it][=] moral_scenarios: 3/20 = 15.0% (running avg 28.6%)
============================================================
[>] nutrition (shots=5)
Downloading data: 0%| | 0.00/55.0k [00:00<?, ?B/s][A
Downloading data: 100%|ββββββββββ| 55.0k/55.0k [00:00<00:00, 173kB/s][A
Downloading data: 100%|ββββββββββ| 55.0k/55.0k [00:00<00:00, 172kB/s]
Downloading data: 0%| | 0.00/9.02k [00:00<?, ?B/s][A
Downloading data: 100%|ββββββββββ| 9.02k/9.02k [00:00<00:00, 58.6kB/s][A
Downloading data: 100%|ββββββββββ| 9.02k/9.02k [00:00<00:00, 58.3kB/s]
Downloading data: 0%| | 0.00/4.99k [00:00<?, ?B/s][A
Downloading data: 100%|ββββββββββ| 4.99k/4.99k [00:00<00:00, 28.0kB/s][A
Downloading data: 100%|ββββββββββ| 4.99k/4.99k [00:00<00:00, 27.9kB/s]
Generating test split: 0%| | 0/306 [00:00<?, ? examples/s][A
Generating test split: 100%|ββββββββββ| 306/306 [00:00<00:00, 60429.26 examples/s]
Generating validation split: 0%| | 0/33 [00:00<?, ? examples/s][A
Generating validation split: 100%|ββββββββββ| 33/33 [00:00<00:00, 9086.33 examples/s]
Generating dev split: 0%| | 0/5 [00:00<?, ? examples/s][A
Generating dev split: 100%|ββββββββββ| 5/5 [00:00<00:00, 1449.21 examples/s]
nutrition: 0%| | 0/20 [00:00<?, ?it/s][A
nutrition: 10%|β | 2/20 [00:00<00:00, 19.34it/s][A
nutrition: 30%|βββ | 6/20 [00:00<00:00, 24.76it/s][A
nutrition: 45%|βββββ | 9/20 [00:00<00:00, 24.27it/s][A
nutrition: 65%|βββββββ | 13/20 [00:00<00:00, 25.76it/s][A
nutrition: 85%|βββββββββ | 17/20 [00:00<00:00, 29.38it/s][A
[A
MMLU subjects: 79%|ββββββββ | 45/57 [01:43<00:40, 3.34s/it][=] nutrition: 4/20 = 20.0% (running avg 28.4%)
============================================================
[>] philosophy (shots=5)
philosophy: 0%| | 0/20 [00:00<?, ?it/s][A
philosophy: 10%|β | 2/20 [00:00<00:01, 17.21it/s][A
philosophy: 45%|βββββ | 9/20 [00:00<00:00, 43.07it/s][A
philosophy: 80%|ββββββββ | 16/20 [00:00<00:00, 51.44it/s][A
[A
MMLU subjects: 81%|ββββββββ | 46/57 [01:45<00:31, 2.87s/it][=] philosophy: 3/20 = 15.0% (running avg 28.2%)
============================================================
[>] prehistory (shots=5)
Downloading data: 0%| | 0.00/54.3k [00:00<?, ?B/s][A
Downloading data: 100%|ββββββββββ| 54.3k/54.3k [00:00<00:00, 336kB/s][A
Downloading data: 100%|ββββββββββ| 54.3k/54.3k [00:00<00:00, 334kB/s]
Downloading data: 0%| | 0.00/9.89k [00:00<?, ?B/s][A
Downloading data: 100%|ββββββββββ| 9.89k/9.89k [00:00<00:00, 50.2kB/s][A
Downloading data: 100%|ββββββββββ| 9.89k/9.89k [00:00<00:00, 49.9kB/s]
Downloading data: 0%| | 0.00/4.62k [00:00<?, ?B/s][A
Downloading data: 100%|ββββββββββ| 4.62k/4.62k [00:00<00:00, 21.8kB/s][A
Downloading data: 100%|ββββββββββ| 4.62k/4.62k [00:00<00:00, 21.7kB/s]
Generating test split: 0%| | 0/324 [00:00<?, ? examples/s][A
Generating test split: 100%|ββββββββββ| 324/324 [00:00<00:00, 63532.23 examples/s]
Generating validation split: 0%| | 0/35 [00:00<?, ? examples/s][A
Generating validation split: 100%|ββββββββββ| 35/35 [00:00<00:00, 9094.33 examples/s]
Generating dev split: 0%| | 0/5 [00:00<?, ? examples/s][A
Generating dev split: 100%|ββββββββββ| 5/5 [00:00<00:00, 1395.22 examples/s]
prehistory: 0%| | 0/20 [00:00<?, ?it/s][A
prehistory: 25%|βββ | 5/20 [00:00<00:00, 43.02it/s][A
prehistory: 50%|βββββ | 10/20 [00:00<00:00, 41.15it/s][A
prehistory: 75%|ββββββββ | 15/20 [00:00<00:00, 35.37it/s][A
[A
MMLU subjects: 82%|βββββββββ | 47/57 [01:48<00:30, 3.04s/it][=] prehistory: 5/20 = 25.0% (running avg 28.1%)
============================================================
[>] professional_accounting (shots=5)
Downloading data: 0%| | 0.00/69.5k [00:00<?, ?B/s][A
Downloading data: 100%|ββββββββββ| 69.5k/69.5k [00:00<00:00, 356kB/s][A
Downloading data: 100%|ββββββββββ| 69.5k/69.5k [00:00<00:00, 354kB/s]
Downloading data: 0%| | 0.00/12.9k [00:00<?, ?B/s][A
Downloading data: 100%|ββββββββββ| 12.9k/12.9k [00:00<00:00, 61.9kB/s][A
Downloading data: 100%|ββββββββββ| 12.9k/12.9k [00:00<00:00, 61.7kB/s]
Downloading data: 0%| | 0.00/4.89k [00:00<?, ?B/s][A
Downloading data: 100%|ββββββββββ| 4.89k/4.89k [00:00<00:00, 27.4kB/s][A
Downloading data: 100%|ββββββββββ| 4.89k/4.89k [00:00<00:00, 27.3kB/s]
Generating test split: 0%| | 0/282 [00:00<?, ? examples/s][A
Generating test split: 100%|ββββββββββ| 282/282 [00:00<00:00, 54398.83 examples/s]
Generating validation split: 0%| | 0/31 [00:00<?, ? examples/s][A
Generating validation split: 100%|ββββββββββ| 31/31 [00:00<00:00, 8148.87 examples/s]
Generating dev split: 0%| | 0/5 [00:00<?, ? examples/s][A
Generating dev split: 100%|ββββββββββ| 5/5 [00:00<00:00, 1535.93 examples/s]
professional_accounting: 0%| | 0/20 [00:00<?, ?it/s][A
professional_accounting: 10%|β | 2/20 [00:00<00:00, 18.76it/s][A
professional_accounting: 30%|βββ | 6/20 [00:00<00:00, 24.19it/s][A
professional_accounting: 50%|βββββ | 10/20 [00:00<00:00, 29.18it/s][A
professional_accounting: 70%|βββββββ | 14/20 [00:00<00:00, 31.16it/s][A
professional_accounting: 95%|ββββββββββ| 19/20 [00:00<00:00, 33.78it/s][A
[A
MMLU subjects: 84%|βββββββββ | 48/57 [01:52<00:28, 3.19s/it][=] professional_accounting: 6/20 = 30.0% (running avg 28.1%)
============================================================
[>] professional_law (shots=5)
Downloading data: 0%| | 0.00/1.04M [00:00<?, ?B/s][A
Downloading data: 100%|ββββββββββ| 1.04M/1.04M [00:00<00:00, 3.96MB/s][A
Downloading data: 100%|ββββββββββ| 1.04M/1.04M [00:00<00:00, 3.93MB/s]
Downloading data: 0%| | 0.00/116k [00:00<?, ?B/s][A
Downloading data: 100%|ββββββββββ| 116k/116k [00:00<00:00, 603kB/s][A
Downloading data: 100%|ββββββββββ| 116k/116k [00:00<00:00, 600kB/s]
Downloading data: 0%| | 0.00/15.1k [00:00<?, ?B/s][A
Downloading data: 100%|ββββββββββ| 15.1k/15.1k [00:00<00:00, 90.5kB/s][A
Downloading data: 100%|ββββββββββ| 15.1k/15.1k [00:00<00:00, 90.1kB/s]
Generating test split: 0%| | 0/1534 [00:00<?, ? examples/s][A
Generating test split: 100%|ββββββββββ| 1534/1534 [00:00<00:00, 72607.74 examples/s]
Generating validation split: 0%| | 0/170 [00:00<?, ? examples/s][A
Generating validation split: 100%|ββββββββββ| 170/170 [00:00<00:00, 29159.27 examples/s]
Generating dev split: 0%| | 0/5 [00:00<?, ? examples/s][A
Generating dev split: 100%|ββββββββββ| 5/5 [00:00<00:00, 1370.87 examples/s]
professional_law: 0%| | 0/20 [00:00<?, ?it/s][A
professional_law: 20%|ββ | 4/20 [00:00<00:00, 36.28it/s][A
professional_law: 50%|βββββ | 10/20 [00:00<00:00, 44.88it/s][A
professional_law: 75%|ββββββββ | 15/20 [00:00<00:00, 46.92it/s][A
[A
MMLU subjects: 86%|βββββββββ | 49/57 [01:55<00:25, 3.17s/it][=] professional_law: 7/20 = 35.0% (running avg 28.3%)
============================================================
[>] professional_medicine (shots=5)
Downloading data: 0%| | 0.00/125k [00:00<?, ?B/s][A
Downloading data: 100%|ββββββββββ| 125k/125k [00:00<00:00, 666kB/s][A
Downloading data: 100%|ββββββββββ| 125k/125k [00:00<00:00, 663kB/s]
Downloading data: 0%| | 0.00/19.9k [00:00<?, ?B/s][A
Downloading data: 100%|ββββββββββ| 19.9k/19.9k [00:00<00:00, 134kB/s][A
Downloading data: 100%|ββββββββββ| 19.9k/19.9k [00:00<00:00, 134kB/s]
Downloading data: 0%| | 0.00/8.45k [00:00<?, ?B/s][A
Downloading data: 100%|ββββββββββ| 8.45k/8.45k [00:00<00:00, 52.0kB/s][A
Downloading data: 100%|ββββββββββ| 8.45k/8.45k [00:00<00:00, 51.7kB/s]
Generating test split: 0%| | 0/272 [00:00<?, ? examples/s][A
Generating test split: 100%|ββββββββββ| 272/272 [00:00<00:00, 37004.56 examples/s]
Generating validation split: 0%| | 0/31 [00:00<?, ? examples/s][A
Generating validation split: 100%|ββββββββββ| 31/31 [00:00<00:00, 7737.65 examples/s]
Generating dev split: 0%| | 0/5 [00:00<?, ? examples/s][A
Generating dev split: 100%|ββββββββββ| 5/5 [00:00<00:00, 1409.19 examples/s]
professional_medicine: 0%| | 0/20 [00:00<?, ?it/s][A
professional_medicine: 20%|ββ | 4/20 [00:00<00:00, 37.94it/s][A
professional_medicine: 50%|βββββ | 10/20 [00:00<00:00, 47.05it/s][A
professional_medicine: 80%|ββββββββ | 16/20 [00:00<00:00, 49.68it/s][A
[A
MMLU subjects: 88%|βββββββββ | 50/57 [01:58<00:21, 3.13s/it][=] professional_medicine: 1/20 = 5.0% (running avg 27.8%)
============================================================
[>] professional_psychology (shots=5)
Downloading data: 0%| | 0.00/133k [00:00<?, ?B/s][A
Downloading data: 100%|ββββββββββ| 133k/133k [00:00<00:00, 599kB/s][A
Downloading data: 100%|ββββββββββ| 133k/133k [00:00<00:00, 596kB/s]
Downloading data: 0%| | 0.00/22.1k [00:00<?, ?B/s][A
Downloading data: 100%|ββββββββββ| 22.1k/22.1k [00:00<00:00, 81.8kB/s][A
Downloading data: 100%|ββββββββββ| 22.1k/22.1k [00:00<00:00, 81.6kB/s]
Downloading data: 0%| | 0.00/4.69k [00:00<?, ?B/s][A
Downloading data: 100%|ββββββββββ| 4.69k/4.69k [00:00<00:00, 30.9kB/s][A
Downloading data: 100%|ββββββββββ| 4.69k/4.69k [00:00<00:00, 30.8kB/s]
Generating test split: 0%| | 0/612 [00:00<?, ? examples/s][A
Generating test split: 100%|ββββββββββ| 612/612 [00:00<00:00, 95884.13 examples/s]
Generating validation split: 0%| | 0/69 [00:00<?, ? examples/s][A
Generating validation split: 100%|ββββββββββ| 69/69 [00:00<00:00, 16709.41 examples/s]
Generating dev split: 0%| | 0/5 [00:00<?, ? examples/s][A
Generating dev split: 100%|ββββββββββ| 5/5 [00:00<00:00, 1311.87 examples/s]
professional_psychology: 0%| | 0/20 [00:00<?, ?it/s][A
professional_psychology: 20%|ββ | 4/20 [00:00<00:00, 36.84it/s][A
professional_psychology: 40%|ββββ | 8/20 [00:00<00:00, 36.65it/s][A
professional_psychology: 60%|ββββββ | 12/20 [00:00<00:00, 27.62it/s][A
professional_psychology: 80%|ββββββββ | 16/20 [00:00<00:00, 30.48it/s][A
professional_psychology: 100%|ββββββββββ| 20/20 [00:00<00:00, 29.12it/s][A
[A
MMLU subjects: 89%|βββββββββ | 51/57 [02:01<00:19, 3.24s/it][=] professional_psychology: 9/20 = 45.0% (running avg 28.1%)
============================================================
[>] public_relations (shots=5)
Downloading data: 0%| | 0.00/20.6k [00:00<?, ?B/s][A
Downloading data: 100%|ββββββββββ| 20.6k/20.6k [00:00<00:00, 125kB/s][A
Downloading data: 100%|ββββββββββ| 20.6k/20.6k [00:00<00:00, 125kB/s]
Downloading data: 0%| | 0.00/6.45k [00:00<?, ?B/s][A
Downloading data: 100%|ββββββββββ| 6.45k/6.45k [00:00<00:00, 35.2kB/s][A
Downloading data: 100%|ββββββββββ| 6.45k/6.45k [00:00<00:00, 35.0kB/s]
Downloading data: 0%| | 0.00/4.43k [00:00<?, ?B/s][A
Downloading data: 100%|ββββββββββ| 4.43k/4.43k [00:00<00:00, 29.9kB/s][A
Downloading data: 100%|ββββββββββ| 4.43k/4.43k [00:00<00:00, 29.7kB/s]
Generating test split: 0%| | 0/110 [00:00<?, ? examples/s][A
Generating test split: 100%|ββββββββββ| 110/110 [00:00<00:00, 24437.15 examples/s]
Generating validation split: 0%| | 0/12 [00:00<?, ? examples/s][A
Generating validation split: 100%|ββββββββββ| 12/12 [00:00<00:00, 3341.85 examples/s]
Generating dev split: 0%| | 0/5 [00:00<?, ? examples/s][A
Generating dev split: 100%|ββββββββββ| 5/5 [00:00<00:00, 1466.54 examples/s]
public_relations: 0%| | 0/20 [00:00<?, ?it/s][A
public_relations: 10%|β | 2/20 [00:00<00:00, 19.12it/s][A
public_relations: 30%|βββ | 6/20 [00:00<00:00, 29.51it/s][A
public_relations: 55%|ββββββ | 11/20 [00:00<00:00, 34.93it/s][A
public_relations: 75%|ββββββββ | 15/20 [00:00<00:00, 35.89it/s][A
[A
MMLU subjects: 91%|βββββββββ | 52/57 [02:05<00:16, 3.20s/it][=] public_relations: 9/20 = 45.0% (running avg 28.5%)
============================================================
[>] security_studies (shots=5)
Downloading data: 0%| | 0.00/114k [00:00<?, ?B/s][A
Downloading data: 100%|ββββββββββ| 114k/114k [00:00<00:00, 675kB/s][A
Downloading data: 100%|ββββββββββ| 114k/114k [00:00<00:00, 671kB/s]
Downloading data: 0%| | 0.00/18.7k [00:00<?, ?B/s][A
Downloading data: 100%|ββββββββββ| 18.7k/18.7k [00:00<00:00, 106kB/s][A
Downloading data: 100%|ββββββββββ| 18.7k/18.7k [00:00<00:00, 106kB/s]
Downloading data: 0%| | 0.00/7.49k [00:00<?, ?B/s][A
Downloading data: 100%|ββββββββββ| 7.49k/7.49k [00:00<00:00, 45.0kB/s][A
Downloading data: 100%|ββββββββββ| 7.49k/7.49k [00:00<00:00, 44.8kB/s]
Generating test split: 0%| | 0/245 [00:00<?, ? examples/s][A
Generating test split: 100%|ββββββββββ| 245/245 [00:00<00:00, 37800.42 examples/s]
Generating validation split: 0%| | 0/27 [00:00<?, ? examples/s][A
Generating validation split: 100%|ββββββββββ| 27/27 [00:00<00:00, 6508.78 examples/s]
Generating dev split: 0%| | 0/5 [00:00<?, ? examples/s][A
Generating dev split: 100%|ββββββββββ| 5/5 [00:00<00:00, 1451.42 examples/s]
security_studies: 0%| | 0/20 [00:00<?, ?it/s][A
security_studies: 20%|ββ | 4/20 [00:00<00:00, 37.50it/s][A
security_studies: 50%|βββββ | 10/20 [00:00<00:00, 46.09it/s][A
security_studies: 80%|ββββββββ | 16/20 [00:00<00:00, 48.87it/s][A
[A
MMLU subjects: 93%|ββββββββββ| 53/57 [02:08<00:12, 3.18s/it][=] security_studies: 5/20 = 25.0% (running avg 28.4%)
============================================================
[>] sociology (shots=5)
Downloading data: 0%| | 0.00/43.9k [00:00<?, ?B/s][A
Downloading data: 100%|ββββββββββ| 43.9k/43.9k [00:00<00:00, 228kB/s][A
Downloading data: 100%|ββββββββββ| 43.9k/43.9k [00:00<00:00, 227kB/s]
Downloading data: 0%| | 0.00/8.36k [00:00<?, ?B/s][A
Downloading data: 100%|ββββββββββ| 8.36k/8.36k [00:00<00:00, 51.2kB/s][A
Downloading data: 100%|ββββββββββ| 8.36k/8.36k [00:00<00:00, 50.9kB/s]
Downloading data: 0%| | 0.00/4.21k [00:00<?, ?B/s][A
Downloading data: 100%|ββββββββββ| 4.21k/4.21k [00:00<00:00, 27.3kB/s][A
Downloading data: 100%|ββββββββββ| 4.21k/4.21k [00:00<00:00, 27.1kB/s]
Generating test split: 0%| | 0/201 [00:00<?, ? examples/s][A
Generating test split: 100%|ββββββββββ| 201/201 [00:00<00:00, 40145.48 examples/s]
Generating validation split: 0%| | 0/22 [00:00<?, ? examples/s][A
Generating validation split: 100%|ββββββββββ| 22/22 [00:00<00:00, 5844.98 examples/s]
Generating dev split: 0%| | 0/5 [00:00<?, ? examples/s][A
Generating dev split: 100%|ββββββββββ| 5/5 [00:00<00:00, 1353.79 examples/s]
sociology: 0%| | 0/20 [00:00<?, ?it/s][A
sociology: 10%|β | 2/20 [00:00<00:01, 13.70it/s][A
sociology: 30%|βββ | 6/20 [00:00<00:00, 25.24it/s][A
sociology: 60%|ββββββ | 12/20 [00:00<00:00, 38.57it/s][A
sociology: 90%|βββββββββ | 18/20 [00:00<00:00, 45.52it/s][A
[A
MMLU subjects: 95%|ββββββββββ| 54/57 [02:11<00:09, 3.13s/it][=] sociology: 4/20 = 20.0% (running avg 28.2%)
============================================================
[>] us_foreign_policy (shots=5)
Downloading data: 0%| | 0.00/19.5k [00:00<?, ?B/s][A
Downloading data: 100%|ββββββββββ| 19.5k/19.5k [00:00<00:00, 114kB/s][A
Downloading data: 100%|ββββββββββ| 19.5k/19.5k [00:00<00:00, 113kB/s]
Downloading data: 0%| | 0.00/5.27k [00:00<?, ?B/s][A
Downloading data: 100%|ββββββββββ| 5.27k/5.27k [00:00<00:00, 34.0kB/s][A
Downloading data: 100%|ββββββββββ| 5.27k/5.27k [00:00<00:00, 33.8kB/s]
Downloading data: 0%| | 0.00/4.22k [00:00<?, ?B/s][A
Downloading data: 100%|ββββββββββ| 4.22k/4.22k [00:00<00:00, 22.7kB/s][A
Downloading data: 100%|ββββββββββ| 4.22k/4.22k [00:00<00:00, 22.6kB/s]
Generating test split: 0%| | 0/100 [00:00<?, ? examples/s][A
Generating test split: 100%|ββββββββββ| 100/100 [00:00<00:00, 23196.02 examples/s]
Generating validation split: 0%| | 0/11 [00:00<?, ? examples/s][A
Generating validation split: 100%|ββββββββββ| 11/11 [00:00<00:00, 2971.81 examples/s]
Generating dev split: 0%| | 0/5 [00:00<?, ? examples/s][A
Generating dev split: 100%|ββββββββββ| 5/5 [00:00<00:00, 1376.35 examples/s]
us_foreign_policy: 0%| | 0/20 [00:00<?, ?it/s][A
us_foreign_policy: 25%|βββ | 5/20 [00:00<00:00, 44.09it/s][A
us_foreign_policy: 55%|ββββββ | 11/20 [00:00<00:00, 52.64it/s][A
us_foreign_policy: 85%|βββββββββ | 17/20 [00:00<00:00, 55.54it/s][A
[A
MMLU subjects: 96%|ββββββββββ| 55/57 [02:14<00:06, 3.20s/it][=] us_foreign_policy: 5/20 = 25.0% (running avg 28.2%)
============================================================
[>] virology (shots=5)
Downloading data: 0%| | 0.00/27.3k [00:00<?, ?B/s][A
Downloading data: 100%|ββββββββββ| 27.3k/27.3k [00:00<00:00, 176kB/s][A
Downloading data: 100%|ββββββββββ| 27.3k/27.3k [00:00<00:00, 175kB/s]
Downloading data: 0%| | 0.00/7.05k [00:00<?, ?B/s][A
Downloading data: 100%|ββββββββββ| 7.05k/7.05k [00:00<00:00, 42.4kB/s][A
Downloading data: 100%|ββββββββββ| 7.05k/7.05k [00:00<00:00, 42.2kB/s]
Downloading data: 0%| | 0.00/3.87k [00:00<?, ?B/s][A
Downloading data: 100%|ββββββββββ| 3.87k/3.87k [00:00<00:00, 23.4kB/s][A
Downloading data: 100%|ββββββββββ| 3.87k/3.87k [00:00<00:00, 23.3kB/s]
Generating test split: 0%| | 0/166 [00:00<?, ? examples/s][A
Generating test split: 100%|ββββββββββ| 166/166 [00:00<00:00, 42164.02 examples/s]
Generating validation split: 0%| | 0/18 [00:00<?, ? examples/s][A
Generating validation split: 100%|ββββββββββ| 18/18 [00:00<00:00, 5863.43 examples/s]
Generating dev split: 0%| | 0/5 [00:00<?, ? examples/s][A
Generating dev split: 100%|ββββββββββ| 5/5 [00:00<00:00, 1653.77 examples/s]
virology: 0%| | 0/20 [00:00<?, ?it/s][A
virology: 15%|ββ | 3/20 [00:00<00:00, 27.33it/s][A
virology: 45%|βββββ | 9/20 [00:00<00:00, 45.23it/s][A
virology: 70%|βββββββ | 14/20 [00:00<00:00, 42.99it/s][A
virology: 100%|ββββββββββ| 20/20 [00:00<00:00, 48.97it/s][A
[A
MMLU subjects: 98%|ββββββββββ| 56/57 [02:17<00:03, 3.17s/it][=] virology: 5/20 = 25.0% (running avg 28.1%)
============================================================
[>] world_religions (shots=5)
Downloading data: 0%| | 0.00/18.9k [00:00<?, ?B/s][A
Downloading data: 100%|ββββββββββ| 18.9k/18.9k [00:00<00:00, 106kB/s][A
Downloading data: 100%|ββββββββββ| 18.9k/18.9k [00:00<00:00, 106kB/s]
Downloading data: 0%| | 0.00/4.94k [00:00<?, ?B/s][A
Downloading data: 100%|ββββββββββ| 4.94k/4.94k [00:00<00:00, 30.3kB/s][A
Downloading data: 100%|ββββββββββ| 4.94k/4.94k [00:00<00:00, 30.2kB/s]
Downloading data: 0%| | 0.00/3.30k [00:00<?, ?B/s][A
Downloading data: 100%|ββββββββββ| 3.30k/3.30k [00:00<00:00, 21.2kB/s][A
Downloading data: 100%|ββββββββββ| 3.30k/3.30k [00:00<00:00, 21.1kB/s]
Generating test split: 0%| | 0/171 [00:00<?, ? examples/s][A
Generating test split: 100%|ββββββββββ| 171/171 [00:00<00:00, 39673.97 examples/s]
Generating validation split: 0%| | 0/19 [00:00<?, ? examples/s][A
Generating validation split: 100%|ββββββββββ| 19/19 [00:00<00:00, 5365.37 examples/s]
Generating dev split: 0%| | 0/5 [00:00<?, ? examples/s][A
Generating dev split: 100%|ββββββββββ| 5/5 [00:00<00:00, 1399.69 examples/s]
world_religions: 0%| | 0/20 [00:00<?, ?it/s][A
world_religions: 10%|β | 2/20 [00:00<00:01, 13.20it/s][A
world_religions: 25%|βββ | 5/20 [00:00<00:00, 18.83it/s][A
world_religions: 40%|ββββ | 8/20 [00:00<00:00, 20.93it/s][A
world_religions: 60%|ββββββ | 12/20 [00:00<00:00, 23.89it/s][A
world_religions: 75%|ββββββββ | 15/20 [00:00<00:00, 23.91it/s][A
world_religions: 95%|ββββββββββ| 19/20 [00:00<00:00, 28.23it/s][A
[A
MMLU subjects: 100%|ββββββββββ| 57/57 [02:20<00:00, 3.22s/it]
MMLU subjects: 100%|ββββββββββ| 57/57 [02:20<00:00, 2.47s/it]
[=] world_religions: 3/20 = 15.0% (running avg 27.9%)
============================================================
MMLU RESULT β /home/zeus/microllm2/microllm2-checkpoints/final_merged
Shots: 5 Subjects: 57/57
abstract_algebra 30.0% (6/20)
anatomy 25.0% (5/20)
astronomy 35.0% (7/20)
business_ethics 30.0% (6/20)
clinical_knowledge 45.0% (9/20)
college_biology 45.0% (9/20)
college_chemistry 15.0% (3/20)
college_computer_science 45.0% (9/20)
college_mathematics 35.0% (7/20)
college_medicine 30.0% (6/20)
college_physics 15.0% (3/20)
computer_security 30.0% (6/20)
conceptual_physics 5.0% (1/20)
econometrics 30.0% (6/20)
electrical_engineering 20.0% (4/20)
elementary_mathematics 30.0% (6/20)
formal_logic 10.0% (2/20)
global_facts 35.0% (7/20)
high_school_biology 45.0% (9/20)
high_school_chemistry 35.0% (7/20)
high_school_computer_science 35.0% (7/20)
high_school_european_history 20.0% (4/20)
high_school_geography 25.0% (5/20)
high_school_government_and_politics 20.0% (4/20)
high_school_macroeconomics 0.0% (0/20)
high_school_mathematics 20.0% (4/20)
high_school_microeconomics 35.0% (7/20)
high_school_physics 20.0% (4/20)
high_school_psychology 25.0% (5/20)
high_school_statistics 40.0% (8/20)
high_school_us_history 20.0% (4/20)
high_school_world_history 35.0% (7/20)
human_aging 40.0% (8/20)
human_sexuality 15.0% (3/20)
international_law 35.0% (7/20)
jurisprudence 40.0% (8/20)
logical_fallacies 35.0% (7/20)
machine_learning 50.0% (10/20)
management 20.0% (4/20)
marketing 35.0% (7/20)
medical_genetics 40.0% (8/20)
miscellaneous 30.0% (6/20)
moral_disputes 20.0% (4/20)
moral_scenarios 15.0% (3/20)
nutrition 20.0% (4/20)
philosophy 15.0% (3/20)
prehistory 25.0% (5/20)
professional_accounting 30.0% (6/20)
professional_law 35.0% (7/20)
professional_medicine 5.0% (1/20)
professional_psychology 45.0% (9/20)
public_relations 45.0% (9/20)
security_studies 25.0% (5/20)
sociology 20.0% (4/20)
us_foreign_policy 25.0% (5/20)
virology 25.0% (5/20)
world_religions 15.0% (3/20)
OVERALL: 318/1140 = 27.89%
============================================================
[+] Saved mmlu_results.json
Note: GPT2-XL base ~24-26% MMLU (random 25%). MicroLLM2 distilled should be 25-30% β
MMLU is knowledge-heavy; GPT2 1.5B 1024ctx cannot match 7B+ models. Use as sanity check, not SOTA claim.
[MMLU57-TOKEN] exit 0 at Sun Aug 9 23:12:32 UTC 2026
{
"model": "/home/zeus/microllm2/microllm2-checkpoints/final_merged",
"shots": 5,
"limit": 20,
"overall": {
"correct": 318,
"total": 1140,
"accuracy": 0.2789473684210526
},
"subjects": {
"abstract_algebra": {
"correct": 6,
"total": 20,
"accuracy": 0.3
},
"anatomy": {
"correct": 5,
"total": 20,
"accuracy": 0.25
},
"astronomy": {
"correct": 7,
"total": 20,
"accuracy": 0.35
},
"business_ethics": {
"correct": 6,
"total": 20,
"accuracy": 0.3
},
FINAL57 318 / 1140 27.89 57
'mmlu_results.json' -> 'mmlu57_results.json'
'mmlu_results.json' -> '/teamspace/studios/this_studio/microllm2/mmlu57_results.json'
'mmlu57_token.log' -> '/teamspace/studios/this_studio/microllm2/mmlu57_token.log'
|