Text Generation
Transformers
Safetensors
PyTorch
English
lamoe
mixture-of-experts
custom-code
conversational
Instructions to use IMvision12/lamoe-247 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
Download docs/benchmark_sources.json from IMvision12/lamoe-247: direct link, hf CLI and curl.
- Browser
- Download file 3.81 kB
-
https://huggingface.co/IMvision12/lamoe-247/resolve/main/docs/benchmark_sources.json
- Command line
-
hf download hf://IMvision12/lamoe-247/docs/benchmark_sources.json
-
curl -L -o benchmark_sources.json https://huggingface.co/IMvision12/lamoe-247/resolve/main/docs/benchmark_sources.json
3.81 kB
| [ | |
| { | |
| "id": "S1", | |
| "title": "EleutherAI Pythia-160M v1.1, step143000: official zero-shot evaluation JSON", | |
| "url": "https://raw.githubusercontent.com/EleutherAI/pythia/main/evals/pythia-v1/pythia-160m/zero-shot/160m_step143000.json", | |
| "note": "Explicit acc / acc_norm fields; full limit=null; old task version 0. Model identifier in JSON is pythia-v1.1-160m. HellaSwag is absent from this file.", | |
| "retrieved_sha256": "47d6986215acd4ab22ab5a82e75d49645bfbb757efc76b0d0f305d62a1362a60", | |
| "model_args": "pretrained=EleutherAI/pythia-v1.1-160m,revision=step143000" | |
| }, | |
| { | |
| "id": "S2", | |
| "title": "EleutherAI Pythia-160M model card", | |
| "url": "https://huggingface.co/EleutherAI/pythia-160m", | |
| "note": "Apache-2.0; exact total parameter count 162,322,944. Pythia-410M is outside the requested range." | |
| }, | |
| { | |
| "id": "S3", | |
| "title": "Cerebras-GPT-256M official model card: zero-shot table", | |
| "url": "https://huggingface.co/cerebras/Cerebras-GPT-256M", | |
| "note": "Apache-2.0; includes 111M and 256M base models. Published results rounded to three decimal places." | |
| }, | |
| { | |
| "id": "S4", | |
| "title": "Cerebras-GPT paper, Appendix C.2", | |
| "url": "https://arxiv.org/html/2304.03208v1", | |
| "note": "Maximum continuation probability is used for main results; length-normalized results are separately tabulated. This report uses the unnormalized main results only." | |
| }, | |
| { | |
| "id": "S5", | |
| "title": "HuggingFaceTB SmolLM2-135M-Instruct official card", | |
| "url": "https://huggingface.co/HuggingFaceTB/SmolLM2-135M-Instruct", | |
| "note": "Apache-2.0. Zero-shot unless noted. Base table column is specifically labelled SmolLM2-135M-8k. IFEval is prompt/instruction average; MMLU is cloze; GSM8K 5-shot; BBH 3-shot." | |
| }, | |
| { | |
| "id": "S6", | |
| "title": "HuggingFaceTB SmolLM2-360M-Instruct official card", | |
| "url": "https://huggingface.co/HuggingFaceTB/SmolLM2-360M-Instruct", | |
| "note": "Apache-2.0. Zero-shot unless noted. IFEval average, MMLU cloze, GSM8K 5-shot and BBH 3-shot must not be pooled with local metrics." | |
| }, | |
| { | |
| "id": "S7", | |
| "title": "Meta MobileLLM-R1: SmolLM2 and Gemma baseline reevaluations", | |
| "url": "https://github.com/facebookresearch/MobileLLM-R1#evaluation", | |
| "note": "External reevaluations: HumanEval 0-shot pass@1; MBPP 3-shot; Gemma PT MMLU 5-shot and GSM8K 8-shot; Gemma IT GSM8K 0-shot. The Gemma rows identify size268M. These results are distinct from model-author card results." | |
| }, | |
| { | |
| "id": "S8", | |
| "title": "Google Gemma 3 model card: dedicated 270M benchmark tables", | |
| "url": "https://ai.google.dev/gemma/docs/core/model_card_3#gemma_3_270m", | |
| "note": "Dedicated270M PT and IT tables only. PT: HellaSwag10-shot, PIQA0, TriviaQA5, ARC-C25, WinoGrande5. IT: HellaSwag/PIQA/ARC-C/WinoGrande/IFEval0-shot; BBH few-shot count unspecified. IFEval51.2 aggregation is not specified. Larger-model rows are excluded." | |
| }, | |
| { | |
| "id": "S9", | |
| "title": "Google Gemma 3 270M model card and licence tag", | |
| "url": "https://huggingface.co/google/gemma-3-270m", | |
| "note": "Gemma custom terms; open-weight model, not labelled Apache/MIT here. Public card only was consulted; no gated weights or accounts were accessed." | |
| }, | |
| { | |
| "id": "S10", | |
| "title": "Meta OPT-125M model card", | |
| "url": "https://huggingface.co/facebook/opt-125m", | |
| "note": "OPT125M base model. Custom OPT licence; included at user request. Numeric zero-shot references come from Cerebras paper Table8, not a differently-shot leaderboard." | |
| }, | |
| { | |
| "id": "S11", | |
| "title": "Meta OPT-350M model card and distributed licence", | |
| "url": "https://huggingface.co/facebook/opt-350m", | |
| "note": "OPT350M base model, custom OPT licence (non-commercial research terms). Included at user request. No weights were downloaded." | |
| } | |
| ] | |