Instructions to use llm-jp/optimal-sparsity-math-d1024-E32-k8-3.5B-A1.1B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use llm-jp/optimal-sparsity-math-d1024-E32-k8-3.5B-A1.1B with Transformers:

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

pipe = pipeline("text-generation", model="llm-jp/optimal-sparsity-math-d1024-E32-k8-3.5B-A1.1B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("llm-jp/optimal-sparsity-math-d1024-E32-k8-3.5B-A1.1B")
model = AutoModelForCausalLM.from_pretrained("llm-jp/optimal-sparsity-math-d1024-E32-k8-3.5B-A1.1B", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use llm-jp/optimal-sparsity-math-d1024-E32-k8-3.5B-A1.1B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "llm-jp/optimal-sparsity-math-d1024-E32-k8-3.5B-A1.1B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "llm-jp/optimal-sparsity-math-d1024-E32-k8-3.5B-A1.1B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/llm-jp/optimal-sparsity-math-d1024-E32-k8-3.5B-A1.1B

SGLang

How to use llm-jp/optimal-sparsity-math-d1024-E32-k8-3.5B-A1.1B 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 "llm-jp/optimal-sparsity-math-d1024-E32-k8-3.5B-A1.1B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "llm-jp/optimal-sparsity-math-d1024-E32-k8-3.5B-A1.1B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "llm-jp/optimal-sparsity-math-d1024-E32-k8-3.5B-A1.1B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "llm-jp/optimal-sparsity-math-d1024-E32-k8-3.5B-A1.1B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use llm-jp/optimal-sparsity-math-d1024-E32-k8-3.5B-A1.1B with Docker Model Runner:
```
docker model run hf.co/llm-jp/optimal-sparsity-math-d1024-E32-k8-3.5B-A1.1B
```

optimal-sparsity-math-d1024-E32-k8-3.5B-A1.1B

File size: 494 Bytes

58a7b38

{
  "add_bos_token": true,
  "add_eos_token": false,
  "unk_token": "<unk>",
  "bos_token": "<s>",
  "eos_token": "</s>",
  "pad_token": "<PAD|LLM-jp>",
  "cls_token": "<CLS|LLM-jp>",
  "sep_token": "<SEP|LLM-jp>",
  "eod_token": "</s>",
  "mask_token": "<MASK|LLM-jp>",
  "extra_ids": 0,
  "sp_model_kwargs": {},
  "model_max_length": 1000000000000000019884624838656,
  "clean_up_tokenization_spaces": false,
  "special_tokens_map_file": null,
  "tokenizer_class": "PreTrainedTokenizerFast"
}