Instructions to use MLVXN/MicroLLM2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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

MicroLLM2 / lm_eval_mmlu_harness.log

MLVXN

bench: add lm_eval_mmlu_harness.log harness 26.10% - scores only

a188657 verified 1 day ago

Raw

History Blame Contribute Delete

263 kB

	[LM-EVAL] start Sun Aug 9 23:20:39 UTC 2026 token=hf_oUMhDnr...
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	2026-08-09:23:20:43,827 INFO [__main__.py:251] Verbosity set to INFO
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	2026-08-09:23:20:46,667 INFO [__main__.py:336] Loading selected tasks...
	2026-08-09:23:20:46,669 INFO [evaluator.py:131] Setting random seed to 0 \| Setting numpy seed to 1234 \| Setting torch manual seed to 1234
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	2026-08-09:23:23:23,100 WARNING [evaluator.py:222] Overwriting default num_fewshot of mmlu_international_law from None to 5
	2026-08-09:23:23:23,101 WARNING [evaluator.py:222] Overwriting default num_fewshot of mmlu_moral_scenarios from None to 5
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	2026-08-09:23:23:23,101 WARNING [evaluator.py:222] Overwriting default num_fewshot of mmlu_high_school_world_history from None to 5
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	2026-08-09:23:23:23,101 WARNING [evaluator.py:222] Overwriting default num_fewshot of mmlu_logical_fallacies from None to 5
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	2026-08-09:23:23:23,101 WARNING [evaluator.py:222] Overwriting default num_fewshot of mmlu_formal_logic from None to 5
	2026-08-09:23:23:23,101 WARNING [evaluator.py:222] Overwriting default num_fewshot of mmlu_high_school_european_history from None to 5
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	2026-08-09:23:23:23,102 WARNING [evaluator.py:222] Overwriting default num_fewshot of mmlu_professional_psychology from None to 5
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	2026-08-09:23:23:23,102 WARNING [evaluator.py:222] Overwriting default num_fewshot of mmlu_security_studies from None to 5
	2026-08-09:23:23:23,102 WARNING [evaluator.py:222] Overwriting default num_fewshot of mmlu_high_school_macroeconomics from None to 5
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	2026-08-09:23:23:23,102 WARNING [evaluator.py:222] Overwriting default num_fewshot of mmlu_high_school_government_and_politics from None to 5
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	2026-08-09:23:23:23,102 WARNING [evaluator.py:222] Overwriting default num_fewshot of mmlu_high_school_geography from None to 5
	2026-08-09:23:23:23,102 WARNING [evaluator.py:222] Overwriting default num_fewshot of mmlu_high_school_microeconomics from None to 5
	2026-08-09:23:23:23,103 WARNING [evaluator.py:222] Overwriting default num_fewshot of mmlu_management from None to 5
	2026-08-09:23:23:23,103 WARNING [evaluator.py:222] Overwriting default num_fewshot of mmlu_professional_accounting from None to 5
	2026-08-09:23:23:23,103 WARNING [evaluator.py:222] Overwriting default num_fewshot of mmlu_college_medicine from None to 5
	2026-08-09:23:23:23,103 WARNING [evaluator.py:222] Overwriting default num_fewshot of mmlu_clinical_knowledge from None to 5
	2026-08-09:23:23:23,103 WARNING [evaluator.py:222] Overwriting default num_fewshot of mmlu_virology from None to 5
	2026-08-09:23:23:23,103 WARNING [evaluator.py:222] Overwriting default num_fewshot of mmlu_marketing from None to 5
	2026-08-09:23:23:23,103 WARNING [evaluator.py:222] Overwriting default num_fewshot of mmlu_nutrition from None to 5
	2026-08-09:23:23:23,103 WARNING [evaluator.py:222] Overwriting default num_fewshot of mmlu_medical_genetics from None to 5
	2026-08-09:23:23:23,103 WARNING [evaluator.py:222] Overwriting default num_fewshot of mmlu_global_facts from None to 5
	2026-08-09:23:23:23,103 WARNING [evaluator.py:222] Overwriting default num_fewshot of mmlu_miscellaneous from None to 5
	2026-08-09:23:23:23,103 WARNING [evaluator.py:222] Overwriting default num_fewshot of mmlu_professional_medicine from None to 5
	2026-08-09:23:23:23,103 WARNING [evaluator.py:222] Overwriting default num_fewshot of mmlu_human_aging from None to 5
	2026-08-09:23:23:23,103 WARNING [evaluator.py:222] Overwriting default num_fewshot of mmlu_business_ethics from None to 5
	2026-08-09:23:23:23,103 WARNING [evaluator.py:222] Overwriting default num_fewshot of mmlu_high_school_mathematics from None to 5
	2026-08-09:23:23:23,103 WARNING [evaluator.py:222] Overwriting default num_fewshot of mmlu_high_school_chemistry from None to 5
	2026-08-09:23:23:23,103 WARNING [evaluator.py:222] Overwriting default num_fewshot of mmlu_astronomy from None to 5
	2026-08-09:23:23:23,104 WARNING [evaluator.py:222] Overwriting default num_fewshot of mmlu_conceptual_physics from None to 5
	2026-08-09:23:23:23,104 WARNING [evaluator.py:222] Overwriting default num_fewshot of mmlu_abstract_algebra from None to 5
	2026-08-09:23:23:23,104 WARNING [evaluator.py:222] Overwriting default num_fewshot of mmlu_college_biology from None to 5
	2026-08-09:23:23:23,104 WARNING [evaluator.py:222] Overwriting default num_fewshot of mmlu_machine_learning from None to 5
	2026-08-09:23:23:23,104 WARNING [evaluator.py:222] Overwriting default num_fewshot of mmlu_college_physics from None to 5
	2026-08-09:23:23:23,104 WARNING [evaluator.py:222] Overwriting default num_fewshot of mmlu_college_mathematics from None to 5
	2026-08-09:23:23:23,104 WARNING [evaluator.py:222] Overwriting default num_fewshot of mmlu_computer_security from None to 5
	2026-08-09:23:23:23,104 WARNING [evaluator.py:222] Overwriting default num_fewshot of mmlu_electrical_engineering from None to 5
	2026-08-09:23:23:23,104 WARNING [evaluator.py:222] Overwriting default num_fewshot of mmlu_college_chemistry from None to 5
	2026-08-09:23:23:23,104 WARNING [evaluator.py:222] Overwriting default num_fewshot of mmlu_elementary_mathematics from None to 5
	2026-08-09:23:23:23,104 WARNING [evaluator.py:222] Overwriting default num_fewshot of mmlu_high_school_statistics from None to 5
	2026-08-09:23:23:23,104 WARNING [evaluator.py:222] Overwriting default num_fewshot of mmlu_high_school_biology from None to 5
	2026-08-09:23:23:23,104 WARNING [evaluator.py:222] Overwriting default num_fewshot of mmlu_anatomy from None to 5
	2026-08-09:23:23:23,105 WARNING [evaluator.py:222] Overwriting default num_fewshot of mmlu_high_school_physics from None to 5
	2026-08-09:23:23:23,105 WARNING [evaluator.py:222] Overwriting default num_fewshot of mmlu_high_school_computer_science from None to 5
	2026-08-09:23:23:23,105 WARNING [evaluator.py:222] Overwriting default num_fewshot of mmlu_college_computer_science from None to 5
	2026-08-09:23:23:23,109 INFO [task.py:395] Building contexts for mmlu_international_law on rank 0...
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	2026-08-09:23:23:24,300 INFO [task.py:395] Building contexts for mmlu_moral_scenarios on rank 0...
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	2026-08-09:23:23:32,778 INFO [task.py:395] Building contexts for mmlu_jurisprudence on rank 0...
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	2026-08-09:23:23:33,799 INFO [task.py:395] Building contexts for mmlu_professional_law on rank 0...
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	2026-08-09:23:23:48,429 INFO [task.py:395] Building contexts for mmlu_high_school_world_history on rank 0...
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	2026-08-09:23:23:50,689 INFO [task.py:395] Building contexts for mmlu_world_religions on rank 0...
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	2026-08-09:23:23:52,322 INFO [task.py:395] Building contexts for mmlu_logical_fallacies on rank 0...
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	2026-08-09:23:23:53,846 INFO [task.py:395] Building contexts for mmlu_prehistory on rank 0...
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	2026-08-09:23:23:56,900 INFO [task.py:395] Building contexts for mmlu_formal_logic on rank 0...
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	2026-08-09:23:23:58,085 INFO [task.py:395] Building contexts for mmlu_high_school_european_history on rank 0...
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	2026-08-09:23:23:59,642 INFO [task.py:395] Building contexts for mmlu_high_school_us_history on rank 0...
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	2026-08-09:23:24:01,549 INFO [task.py:395] Building contexts for mmlu_philosophy on rank 0...
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	2026-08-09:23:24:04,459 INFO [task.py:395] Building contexts for mmlu_moral_disputes on rank 0...
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	2026-08-09:23:24:07,708 INFO [task.py:395] Building contexts for mmlu_professional_psychology on rank 0...
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	2026-08-09:23:24:13,430 INFO [task.py:395] Building contexts for mmlu_high_school_psychology on rank 0...
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	2026-08-09:23:24:18,493 INFO [task.py:395] Building contexts for mmlu_security_studies on rank 0...
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	2026-08-09:23:24:20,794 INFO [task.py:395] Building contexts for mmlu_high_school_macroeconomics on rank 0...
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	2026-08-09:23:24:24,418 INFO [task.py:395] Building contexts for mmlu_us_foreign_policy on rank 0...
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	2026-08-09:23:24:25,346 INFO [task.py:395] Building contexts for mmlu_high_school_government_and_politics on rank 0...
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	2026-08-09:23:24:27,147 INFO [task.py:395] Building contexts for mmlu_human_sexuality on rank 0...
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	2026-08-09:23:24:28,366 INFO [task.py:395] Building contexts for mmlu_econometrics on rank 0...
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	2026-08-09:23:24:29,429 INFO [task.py:395] Building contexts for mmlu_public_relations on rank 0...
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	2026-08-09:23:24:30,458 INFO [task.py:395] Building contexts for mmlu_sociology on rank 0...
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	2026-08-09:23:24:32,336 INFO [task.py:395] Building contexts for mmlu_high_school_geography on rank 0...
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	2026-08-09:23:24:34,193 INFO [task.py:395] Building contexts for mmlu_high_school_microeconomics on rank 0...
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	2026-08-09:23:24:36,414 INFO [task.py:395] Building contexts for mmlu_management on rank 0...
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	2026-08-09:23:24:37,373 INFO [task.py:395] Building contexts for mmlu_professional_accounting on rank 0...
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	2026-08-09:23:24:39,998 INFO [task.py:395] Building contexts for mmlu_college_medicine on rank 0...
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	2026-08-09:23:24:41,621 INFO [task.py:395] Building contexts for mmlu_clinical_knowledge on rank 0...
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	2026-08-09:23:24:44,098 INFO [task.py:395] Building contexts for mmlu_virology on rank 0...
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	2026-08-09:23:24:45,654 INFO [task.py:395] Building contexts for mmlu_marketing on rank 0...
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	2026-08-09:23:24:47,836 INFO [task.py:395] Building contexts for mmlu_nutrition on rank 0...
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	2026-08-09:23:24:50,897 INFO [task.py:395] Building contexts for mmlu_medical_genetics on rank 0...
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	2026-08-09:23:24:51,839 INFO [task.py:395] Building contexts for mmlu_global_facts on rank 0...
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	2026-08-09:23:24:52,768 INFO [task.py:395] Building contexts for mmlu_miscellaneous on rank 0...
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	2026-08-09:23:25:00,099 INFO [task.py:395] Building contexts for mmlu_professional_medicine on rank 0...
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	2026-08-09:23:25:02,652 INFO [task.py:395] Building contexts for mmlu_human_aging on rank 0...
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	2026-08-09:23:25:04,729 INFO [task.py:395] Building contexts for mmlu_business_ethics on rank 0...
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	2026-08-09:23:25:05,669 INFO [task.py:395] Building contexts for mmlu_high_school_mathematics on rank 0...
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	2026-08-09:23:25:08,197 INFO [task.py:395] Building contexts for mmlu_high_school_chemistry on rank 0...
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	2026-08-09:23:25:10,091 INFO [task.py:395] Building contexts for mmlu_astronomy on rank 0...
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	2026-08-09:23:25:11,498 INFO [task.py:395] Building contexts for mmlu_conceptual_physics on rank 0...
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	2026-08-09:23:25:13,675 INFO [task.py:395] Building contexts for mmlu_abstract_algebra on rank 0...
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	2026-08-09:23:25:14,609 INFO [task.py:395] Building contexts for mmlu_college_biology on rank 0...
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	2026-08-09:23:25:15,948 INFO [task.py:395] Building contexts for mmlu_machine_learning on rank 0...
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	2026-08-09:23:25:16,994 INFO [task.py:395] Building contexts for mmlu_college_physics on rank 0...
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	2026-08-09:23:25:17,949 INFO [task.py:395] Building contexts for mmlu_college_mathematics on rank 0...
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	2026-08-09:23:25:18,885 INFO [task.py:395] Building contexts for mmlu_computer_security on rank 0...
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	2026-08-09:23:25:19,819 INFO [task.py:395] Building contexts for mmlu_electrical_engineering on rank 0...
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	2026-08-09:23:25:21,169 INFO [task.py:395] Building contexts for mmlu_college_chemistry on rank 0...
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	2026-08-09:23:25:22,115 INFO [task.py:395] Building contexts for mmlu_elementary_mathematics on rank 0...
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	2026-08-09:23:25:25,642 INFO [task.py:395] Building contexts for mmlu_high_school_statistics on rank 0...
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	2026-08-09:23:25:27,657 INFO [task.py:395] Building contexts for mmlu_high_school_biology on rank 0...
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	2026-08-09:23:25:30,577 INFO [task.py:395] Building contexts for mmlu_anatomy on rank 0...
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	2026-08-09:23:25:31,831 INFO [task.py:395] Building contexts for mmlu_high_school_physics on rank 0...
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	2026-08-09:23:25:33,242 INFO [task.py:395] Building contexts for mmlu_high_school_computer_science on rank 0...
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	2026-08-09:23:25:34,179 INFO [task.py:395] Building contexts for mmlu_college_computer_science on rank 0...
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	2026-08-09:23:25:35,110 INFO [evaluator.py:362] Running loglikelihood requests
	Token indices sequence length is longer than the specified maximum sequence length for this model (1682 > 1024). Running this sequence through the model will result in indexing errors
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	fatal: not a git repository (or any parent up to mount point /home)
	Stopping at filesystem boundary (GIT_DISCOVERY_ACROSS_FILESYSTEM not set).
	hf (pretrained=/home/zeus/microllm2/microllm2-checkpoints/final_merged,dtype=bfloat16,trust_remote_code=False), gen_kwargs: (None), limit: None, num_fewshot: 5, batch_size: 8
	\| Tasks \|Version\|Filter\|n-shot\|Metric\|Value \| \|Stderr\|
	\|---------------------------------------\|-------\|------\|-----:\|------\|-----:\|---\|-----:\|
	\|mmlu \|N/A \|none \| 0\|acc \|0.2610\|± \|0.0037\|
	\| - humanities \|N/A \|none \| 5\|acc \|0.2623\|± \|0.0064\|
	\| - formal_logic \| 0\|none \| 5\|acc \|0.1667\|± \|0.0333\|
	\| - high_school_european_history \| 0\|none \| 5\|acc \|0.2970\|± \|0.0357\|
	\| - high_school_us_history \| 0\|none \| 5\|acc \|0.2745\|± \|0.0313\|
	\| - high_school_world_history \| 0\|none \| 5\|acc \|0.2321\|± \|0.0275\|
	\| - international_law \| 0\|none \| 5\|acc \|0.3554\|± \|0.0437\|
	\| - jurisprudence \| 0\|none \| 5\|acc \|0.2870\|± \|0.0437\|
	\| - logical_fallacies \| 0\|none \| 5\|acc \|0.2883\|± \|0.0356\|
	\| - moral_disputes \| 0\|none \| 5\|acc \|0.2803\|± \|0.0242\|
	\| - moral_scenarios \| 0\|none \| 5\|acc \|0.2525\|± \|0.0145\|
	\| - philosophy \| 0\|none \| 5\|acc \|0.3248\|± \|0.0266\|
	\| - prehistory \| 0\|none \| 5\|acc \|0.2500\|± \|0.0241\|
	\| - professional_law \| 0\|none \| 5\|acc \|0.2471\|± \|0.0110\|
	\| - world_religions \| 0\|none \| 5\|acc \|0.2807\|± \|0.0345\|
	\| - other \|N/A \|none \| 5\|acc \|0.2671\|± \|0.0079\|
	\| - business_ethics \| 0\|none \| 5\|acc \|0.2300\|± \|0.0423\|
	\| - clinical_knowledge \| 0\|none \| 5\|acc \|0.2377\|± \|0.0262\|
	\| - college_medicine \| 0\|none \| 5\|acc \|0.2543\|± \|0.0332\|
	\| - global_facts \| 0\|none \| 5\|acc \|0.3100\|± \|0.0465\|
	\| - human_aging \| 0\|none \| 5\|acc \|0.3049\|± \|0.0309\|
	\| - management \| 0\|none \| 5\|acc \|0.2427\|± \|0.0425\|
	\| - marketing \| 0\|none \| 5\|acc \|0.2778\|± \|0.0293\|
	\| - medical_genetics \| 0\|none \| 5\|acc \|0.3300\|± \|0.0473\|
	\| - miscellaneous \| 0\|none \| 5\|acc \|0.2771\|± \|0.0160\|
	\| - nutrition \| 0\|none \| 5\|acc \|0.2451\|± \|0.0246\|
	\| - professional_accounting \| 0\|none \| 5\|acc \|0.2695\|± \|0.0265\|
	\| - professional_medicine \| 0\|none \| 5\|acc \|0.2169\|± \|0.0250\|
	\| - virology \| 0\|none \| 5\|acc \|0.3072\|± \|0.0359\|
	\| - social_sciences \|N/A \|none \| 5\|acc \|0.2343\|± \|0.0076\|
	\| - econometrics \| 0\|none \| 5\|acc \|0.2456\|± \|0.0405\|
	\| - high_school_geography \| 0\|none \| 5\|acc \|0.2525\|± \|0.0310\|
	\| - high_school_government_and_politics\| 0\|none \| 5\|acc \|0.2228\|± \|0.0300\|
	\| - high_school_macroeconomics \| 0\|none \| 5\|acc \|0.2462\|± \|0.0218\|
	\| - high_school_microeconomics \| 0\|none \| 5\|acc \|0.2059\|± \|0.0263\|
	\| - high_school_psychology \| 0\|none \| 5\|acc \|0.2404\|± \|0.0183\|
	\| - human_sexuality \| 0\|none \| 5\|acc \|0.1908\|± \|0.0345\|
	\| - professional_psychology \| 0\|none \| 5\|acc \|0.2647\|± \|0.0178\|
	\| - public_relations \| 0\|none \| 5\|acc \|0.2909\|± \|0.0435\|
	\| - security_studies \| 0\|none \| 5\|acc \|0.1673\|± \|0.0239\|
	\| - sociology \| 0\|none \| 5\|acc \|0.2239\|± \|0.0295\|
	\| - us_foreign_policy \| 0\|none \| 5\|acc \|0.1900\|± \|0.0394\|
	\| - stem \|N/A \|none \| 5\|acc \|0.2791\|± \|0.0080\|
	\| - abstract_algebra \| 0\|none \| 5\|acc \|0.2500\|± \|0.0435\|
	\| - anatomy \| 0\|none \| 5\|acc \|0.3481\|± \|0.0412\|
	\| - astronomy \| 0\|none \| 5\|acc \|0.3355\|± \|0.0384\|
	\| - college_biology \| 0\|none \| 5\|acc \|0.2847\|± \|0.0377\|
	\| - college_chemistry \| 0\|none \| 5\|acc \|0.1700\|± \|0.0378\|
	\| - college_computer_science \| 0\|none \| 5\|acc \|0.3300\|± \|0.0473\|
	\| - college_mathematics \| 0\|none \| 5\|acc \|0.3200\|± \|0.0469\|
	\| - college_physics \| 0\|none \| 5\|acc \|0.2549\|± \|0.0434\|
	\| - computer_security \| 0\|none \| 5\|acc \|0.3000\|± \|0.0461\|
	\| - conceptual_physics \| 0\|none \| 5\|acc \|0.2170\|± \|0.0269\|
	\| - electrical_engineering \| 0\|none \| 5\|acc \|0.2897\|± \|0.0378\|
	\| - elementary_mathematics \| 0\|none \| 5\|acc \|0.2672\|± \|0.0228\|
	\| - high_school_biology \| 0\|none \| 5\|acc \|0.2645\|± \|0.0251\|
	\| - high_school_chemistry \| 0\|none \| 5\|acc \|0.2365\|± \|0.0299\|
	\| - high_school_computer_science \| 0\|none \| 5\|acc \|0.3300\|± \|0.0473\|
	\| - high_school_mathematics \| 0\|none \| 5\|acc \|0.2593\|± \|0.0267\|
	\| - high_school_physics \| 0\|none \| 5\|acc \|0.2715\|± \|0.0363\|
	\| - high_school_statistics \| 0\|none \| 5\|acc \|0.3519\|± \|0.0326\|
	\| - machine_learning \| 0\|none \| 5\|acc \|0.3036\|± \|0.0436\|

	\| Groups \|Version\|Filter\|n-shot\|Metric\|Value \| \|Stderr\|
	\|------------------\|-------\|------\|-----:\|------\|-----:\|---\|-----:\|
	\|mmlu \|N/A \|none \| 0\|acc \|0.2610\|± \|0.0037\|
	\| - humanities \|N/A \|none \| 5\|acc \|0.2623\|± \|0.0064\|
	\| - other \|N/A \|none \| 5\|acc \|0.2671\|± \|0.0079\|
	\| - social_sciences\|N/A \|none \| 5\|acc \|0.2343\|± \|0.0076\|
	\| - stem \|N/A \|none \| 5\|acc \|0.2791\|± \|0.0080\|

	[LM-EVAL] exit 0 at Sun Aug 9 23:29:33 UTC 2026
	total 88K
	-rw-r--r-- 1 adelynmosley2369 adelynmosley2369 85K Aug 9 23:29 results.json
	{
	"results": {
	"mmlu": {
	"acc,none": 0.26100270616721266,
	"acc_stderr,none": 0.0037004678245414263,
	"alias": "mmlu"
	},
	"mmlu_humanities": {
	"alias": " - humanities",
	"acc,none": 0.2622741764080765,
	"acc_stderr,none": 0.006405056497034186
	},
	"mmlu_formal_logic": {
	"alias": " - formal_logic",
	"acc,none": 0.16666666666666666,
	"acc_stderr,none": 0.03333333333333333
	},
	"mmlu_high_school_european_history": {
	"alias": " - high_school_european_history",
	"acc,none": 0.296969696969697,
	"acc_stderr,none": 0.03567969772268046
	},
	"mmlu_high_school_us_history": {
	"alias": " - high_school_us_history",
	"acc,none": 0.27450980392156865,
	"acc_stderr,none": 0.03132179803083291
	},
	"mmlu_high_school_world_history": {
	"alias": " - high_school_world_history",
	"acc,none": 0.2320675105485232,
	"acc_stderr,none": 0.027479744550808465
	},
	"mmlu_international_law": {
	"alias": " - international_law",
	"acc,none": 0.35537190082644626,
	"acc_stderr,none": 0.043692363265739845
	},
	"mmlu_jurisprudence": {
	"alias": " - jurisprudence",
	"acc,none": 0.28703703703703703,
	"acc_stderr,none": 0.04373313040914761
	},
	"mmlu_logical_fallacies": {
	"alias": " - logical_fallacies",
	"acc,none": 0.2883435582822086,
	"acc_stderr,none": 0.03559039531617345
	},
	"mmlu_moral_disputes": {
	"alias": " - moral_disputes",
	"acc,none": 0.28034682080924855,
	"acc_stderr,none": 0.024182427496577626
	},
	"mmlu_moral_scenarios": {
	"alias": " - moral_scenarios",
	"acc,none": 0.25251396648044694,
	"acc_stderr,none": 0.014530330201468655
	},
	"mmlu_philosophy": {
	"alias": " - philosophy",
	"acc,none": 0.3247588424437299,
	"acc_stderr,none": 0.02659678228769707
	},
	"mmlu_prehistory": {
	"alias": " - prehistory",
	"acc,none": 0.25,
	"acc_stderr,none": 0.02409347123262133
	},
	"mmlu_professional_law": {
	"alias": " - professional_law",
	"acc,none": 0.2470664928292047,
	"acc_stderr,none": 0.011015752255279445
	},
	"mmlu_world_religions": {
	"alias": " - world_religions",
	"acc,none": 0.2807017543859649,
	"acc_stderr,none": 0.03446296217088429
	},
	"mmlu_other": {
	"alias": " - other",
	"acc,none": 0.2671387190215642,
	"acc_stderr,none": 0.007937843638596481
	},
	"mmlu_business_ethics": {
	"alias": " - business_ethics",
	"acc,none": 0.23,
	"acc_stderr,none": 0.04229525846816507
	},
	"mmlu_clinical_knowledge": {
	"alias": " - clinical_knowledge",
	"acc,none": 0.23773584905660378,
	"acc_stderr,none": 0.02619980880756196
	},
	"mmlu_college_medicine": {
	"alias": " - college_medicine",
	"acc,none": 0.2543352601156069,
	"acc_stderr,none": 0.03320556443085566
	},
	"mmlu_global_facts": {
	"alias": " - global_facts",
	"acc,none": 0.31,
	"acc_stderr,none": 0.04648231987117317
	},
	"mmlu_human_aging": {
	"alias": " - human_aging",
	"acc,none": 0.30493273542600896,
	"acc_stderr,none": 0.03089861088247754
	},
	"mmlu_management": {
	"alias": " - management",
	"acc,none": 0.24271844660194175,
	"acc_stderr,none": 0.04245022486384496
	},
	"mmlu_marketing": {
	"alias": " - marketing",
	"acc,none": 0.2777777777777778,
	"acc_stderr,none": 0.029343114798094483
	},
	"mmlu_medical_genetics": {
	"alias": " - medical_genetics",
	"acc,none": 0.33,
	"acc_stderr,none": 0.04725815626252609
	},
	"mmlu_miscellaneous": {
	"alias": " - miscellaneous",
	"acc,none": 0.2771392081736909,
	"acc_stderr,none": 0.01600563629412252
	},
	"mmlu_nutrition": {
	"alias": " - nutrition",
	"acc,none": 0.24509803921568626,
	"acc_stderr,none": 0.024630048979824716
	},
	"mmlu_professional_accounting": {
	"alias": " - professional_accounting",
	"acc,none": 0.2695035460992908,
	"acc_stderr,none": 0.02646903681859067
	},
	"mmlu_professional_medicine": {
	"alias": " - professional_medicine",
	"acc,none": 0.21691176470588236,
	"acc_stderr,none": 0.025035845227711254
	},
	"mmlu_virology": {
	"alias": " - virology",
	"acc,none": 0.3072289156626506,
	"acc_stderr,none": 0.03591566797824665
	},
	"mmlu_social_sciences": {
	"alias": " - social_sciences",
	"acc,none": 0.23431914202144946,
	"acc_stderr,none": 0.007631090779162625
	},
	"mmlu_econometrics": {
	"alias": " - econometrics",
	"acc,none": 0.24561403508771928,
	"acc_stderr,none": 0.040493392977481425
	},
	"mmlu_high_school_geography": {
	"alias": " - high_school_geography",
	"acc,none": 0.25252525252525254,
	"acc_stderr,none": 0.03095405547036587
	},
	"mmlu_high_school_government_and_politics": {
	"alias": " - high_school_government_and_politics",
	"acc,none": 0.22279792746113988,
	"acc_stderr,none": 0.030031147977641528
	},
	"mmlu_high_school_macroeconomics": {
	"alias": " - high_school_macroeconomics",
	"acc,none": 0.24615384615384617,
	"acc_stderr,none": 0.02184086699042312
	},
	"mmlu_high_school_microeconomics": {
	"alias": " - high_school_microeconomics",
	"acc,none": 0.20588235294117646,
	"acc_stderr,none": 0.026265024608275907
	},
	"mmlu_high_school_psychology": {
	"alias": " - high_school_psychology",
	"acc,none": 0.24036697247706423,
	"acc_stderr,none": 0.01832060732096402
	},
	"mmlu_human_sexuality": {
	"alias": " - human_sexuality",
	"acc,none": 0.19083969465648856,
	"acc_stderr,none": 0.03446513350752599
	},
	"mmlu_professional_psychology": {
	"alias": " - professional_psychology",
	"acc,none": 0.2647058823529412,
	"acc_stderr,none": 0.01784808957491327
	},
	"mmlu_public_relations": {
	"alias": " - public_relations",
	"acc,none": 0.2909090909090909,
	"acc_stderr,none": 0.04350271442923247
	},
	"mmlu_security_studies": {
	"alias": " - security_studies",
	"acc,none": 0.1673469387755102,
	'lm_eval_full.log' -> '/teamspace/studios/this_studio/microllm2/lm_eval_full.log'