Instructions to use maple-matrix/quasar-sn24-e2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use maple-matrix/quasar-sn24-e2 with Transformers:

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

pipe = pipeline("text-generation", model="maple-matrix/quasar-sn24-e2", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("maple-matrix/quasar-sn24-e2", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use maple-matrix/quasar-sn24-e2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "maple-matrix/quasar-sn24-e2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "maple-matrix/quasar-sn24-e2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/maple-matrix/quasar-sn24-e2

SGLang

How to use maple-matrix/quasar-sn24-e2 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 "maple-matrix/quasar-sn24-e2" \
    --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": "maple-matrix/quasar-sn24-e2",
		"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 "maple-matrix/quasar-sn24-e2" \
        --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": "maple-matrix/quasar-sn24-e2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use maple-matrix/quasar-sn24-e2 with Docker Model Runner:
```
docker model run hf.co/maple-matrix/quasar-sn24-e2
```

quasar-sn24-e2

File size: 1,304 Bytes

{
  "model_type": "quasar",
  "vocab_size": 248320,
  "d_model": 1536,
  "n_heads": 12,
  "n_layers": 24,
  "d_ff": 4096,
  "head_dim": 128,
  "max_seq_len": 16384,
  "dropout": 0.0,
  "rms_norm_eps": 1e-06,
  "initializer_range": 0.02,
  "use_cache": true,
  "tie_word_embeddings": false,
  "quasar_layers": 4,
  "gated_layers": 2,
  "use_gla_first": false,
  "use_short_conv": true,
  "conv_size": 4,
  "conv_bias": false,
  "allow_neg_eigval": false,
  "attn_mode": "chunk",
  "expand_k": 0.5,
  "expand_v": 1.0,
  "gla_mode": "chunk",
  "memory_slots": 128,
  "memory_dim": 128,
  "moe_type": "bigmac",
  "num_shared_experts": 1,
  "num_routed_experts": 64,
  "top_k": 4,
  "shared_expert_size": 3072,
  "routed_expert_size": 256,
  "dense_input_layers": 4,
  "bigmac_r": 0.25,
  "moe_z_loss_coeff": 0.0001,
  "moe_aux_loss_coeff": 0.0001,
  "smebu_kappa": 2.0,
  "smebu_lambda": 0.002,
  "smebu_beta": 0.5,
  "num_loops": 1,
  "use_looped_injection": false,
  "looped_injection_init": 0.1,
  "rope_theta": 1000000.0,
  "hidden_act": "silu",
  "residual_scale": 0.1,
  "bos_token_id": 1,
  "eos_token_id": 2,
  "auto_map": {
    "AutoConfig": "configuration_quasar.QuasarConfig",
    "AutoModelForCausalLM": "modeling_quasar.QuasarForCausalLM"
  },
  "architectures": [
    "QuasarForCausalLM"
  ]
}