Instructions to use IMvision12/lamoe-247 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use IMvision12/lamoe-247 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="IMvision12/lamoe-247")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("IMvision12/lamoe-247", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use IMvision12/lamoe-247 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "IMvision12/lamoe-247"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "IMvision12/lamoe-247",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/IMvision12/lamoe-247

SGLang

How to use IMvision12/lamoe-247 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 "IMvision12/lamoe-247" \
    --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": "IMvision12/lamoe-247",
		"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 "IMvision12/lamoe-247" \
        --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": "IMvision12/lamoe-247",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use IMvision12/lamoe-247 with Docker Model Runner:
```
docker model run hf.co/IMvision12/lamoe-247
```

lamoe-247 / docs /EVALUATION.md

IMvision12

Release LaMoE 247.65M identity-corrected inference model

118f91b verified 11 days ago

preview code

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Evaluation protocols and results

The aggregate CSV contains 19 metric rows across 11 completed local evaluations of step-00129185. All local runs use context 2,048 and 0-shot prompts. Choice tasks use teacher-forced raw completion with native BOS; generation uses native chat unless noted. Greedy generation uses repetition penalty 1.0.

Task	Count	Scoring and generation allowance
HellaSwag	10,042	Raw and character-normalized choice accuracy
PIQA	1,838	Raw and character-normalized choice accuracy
ARC-Challenge	1,172	Raw and character-normalized choice accuracy
WinoGrande	1,267	Choice accuracy
IFEval	541 prompts / 834 instructions	Prompt and instruction strict/loose; max 1,280 new tokens
TriviaQA	17,944	Exact match; max 256; official newline/period/comma stops
GSM8K	1,319	Official strict and flexible extraction exact match; max 1,024
MMLU	14,042 / 57 subjects	Official raw multiple choice; question-weighted accuracy
HumanEval-Instruct	164	Official instruct prefill, greedy pass@1; max 1,024
MBPP-Instruct	500	Official instruct prefill, explicit 0-shot override; max 256
BBH	6,511 / 27 tasks	Official `bbh_cot_zeroshot`, strict/flexible extraction; max 1,024

BBH flexible 1,310/6,511 and strict 281/6,511 are question-weighted totals, not unweighted task means. MMLU is 3,531/14,042. GSM8K flexible 116/1,319 and strict 1/1,319 must remain separately labelled. IFEval instruction metrics have 834 constraints in their denominator; prompt metrics have 541 prompts. Selecting only certain bars for a graphic does not remove other measured metrics.

MBPP task 493 has a 2,647-token prefix. It is a context-limit failure in the 500-example denominator, with no forwarding, truncation or code execution. HumanEval and eligible MBPP candidates were graded twice in restricted networkless containers; both grading passes agreed. Their local container adapter differs from host code_eval. The measured historical coding image used nonroot UID, read-only root, no mounts/network/GPU/capabilities, 256 MiB, 32 PIDs and a 3-second limit. Historical BBH text was never executed.

BBH generated 847,520 tokens, with 726 outputs reaching their token allowance. Both official CPU grading passes agreed. The complete historical run included all 27 task scores and all 57 MMLU subject scores in its private proof directory; the source release includes their aggregate results rather than private raw responses or test data. Historical benchmark controllers and their private run contracts/assets are not distributed. The portable adapter below remains available.

Portable harness adapter

The optional adapter targets lm-eval==0.4.13. Install the evaluation extra in your own environment, supply local model/tokenizer paths, then register it by importing lamoe.evaluation.adapter:

from lamoe.evaluation.adapter import LaMoELocal
from lm_eval import evaluator

model = LaMoELocal(
    pretrained="/path/to/checkpoint",
    tokenizer="/path/to/tokenizer.json",
    device="cpu",
    gdn_backend="reference",
    chat=False,
)
results = evaluator.simple_evaluate(
    model=model, tasks=["piqa"], num_fewshot=0, batch_size=1
)

This example starts an evaluation when run. The harness may download task data and inspect its own environment. LaMoE itself loads only local weights/tokenizer. The portable adapter supports raw likelihood and greedy generate_until, one request at a time, with explicit context overflow failure. Rolling perplexity is not implemented. chat=True wraps generation in the native chat format and rejects likelihood scoring. It does not automatically reproduce the historical benchmark-specific caps, prefill, filters, double grading or proof collection. Those settings must be selected and recorded explicitly for a new evaluation. Never run generated candidates on the host; use a separately reviewed grading container for coding tasks. The adapter itself never executes generated text.

Published reference comparisons

The simplified charts contain historical model-card and third-party scores, alongside local measurements. Reference sources and exact caveats are recorded in benchmark_sources.json. Differences include raw vs normalized accuracy, cloze vs choice MMLU, shot counts, IFEval averaging, base vs instruct prompting, generation budgets, extraction and pass@1 protocols. Do not interpret the charts as a matched ranking or evidence that LaMoE is generally better than another model. No cross-task average is computed.

The selected chart set has HellaSwag, PIQA, ARC-Challenge, WinoGrande, IFEval, GSM8K, MMLU and BBH. Full measured results additionally retain TriviaQA and both coding tasks. LiveCodeBench was cancelled before scoring; CyberSecEval and SWE-bench have no completed result. BoolQ was not run. CyberGym tasks were skipped. No missing, cancelled or skipped task receives a zero score.

Training overlap is not known to be absent. Lexical screening against a frozen set is not exhaustive semantic decontamination. An external WSL interruption was recovered from verified cached GSM8K responses; final process time is not total effort. No completed benchmark was rerun to create this public source release.