Instructions to use SSON9/solar-open2-tiny-dummy with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SSON9/solar-open2-tiny-dummy with Transformers:

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

pipe = pipeline("text-generation", model="SSON9/solar-open2-tiny-dummy")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("SSON9/solar-open2-tiny-dummy", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use SSON9/solar-open2-tiny-dummy with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SSON9/solar-open2-tiny-dummy"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SSON9/solar-open2-tiny-dummy",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/SSON9/solar-open2-tiny-dummy

SGLang

How to use SSON9/solar-open2-tiny-dummy 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 "SSON9/solar-open2-tiny-dummy" \
    --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": "SSON9/solar-open2-tiny-dummy",
		"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 "SSON9/solar-open2-tiny-dummy" \
        --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": "SSON9/solar-open2-tiny-dummy",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use SSON9/solar-open2-tiny-dummy with Docker Model Runner:
```
docker model run hf.co/SSON9/solar-open2-tiny-dummy
```

solar-open2-tiny-dummy

File size: 1,666 Bytes

919b792

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  "architectures": [
    "SolarOpen2ForCausalLM"
  ],
  "attention_bias": false,
  "attention_dropout": 0.0,
  "bos_token_id": 1,
  "dtype": "bfloat16",
  "eos_token_id": 2,
  "first_k_dense_replace": 0,
  "gqa_interval": 3,
  "gqa_layers": null,
  "head_dim": 128,
  "hidden_act": "silu",
  "hidden_size": 1024,
  "initializer_range": 0.02,
  "intermediate_size": 512,
  "kda_allow_neg_eigval": true,
  "kda_gate_lower_bound": -5.0,
  "kda_use_full_proj": false,
  "layer_types": [
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  ],
  "linear_attn_config": {
    "head_dim": 128,
    "num_heads": 8,
    "num_kv_heads": null,
    "short_conv_kernel_size": 4
  },
  "max_position_embeddings": 4096,
  "model_type": "solar_open2",
  "moe_intermediate_size": 256,
  "n_group": 1,
  "n_routed_experts": 16,
  "n_shared_experts": 1,
  "norm_topk_prob": true,
  "num_attention_heads": 8,
  "num_experts_per_tok": 4,
  "num_hidden_layers": 12,
  "num_key_value_heads": 2,
  "pad_token_id": 2,
  "partial_rotary_factor": 1.0,
  "rms_norm_eps": 1e-05,
  "rope_parameters": {
    "partial_rotary_factor": 1.0,
    "rope_theta": 10000.0,
    "rope_type": "default"
  },
  "routed_scaling_factor": 1.0,
  "tie_word_embeddings": false,
  "topk_group": 1,
  "transformers_version": "5.15.0.dev0",
  "use_cache": true,
  "use_gqa_gate": true,
  "use_gqa_gate_bias": false,
  "use_qk_norm": false,
  "use_rope": false,
  "vocab_size": 196608
}