Instructions to use Blockway/Agens-Volundr-tiny-random with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Blockway/Agens-Volundr-tiny-random with Transformers:

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

pipe = pipeline("text-generation", model="Blockway/Agens-Volundr-tiny-random")
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("Blockway/Agens-Volundr-tiny-random", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Blockway/Agens-Volundr-tiny-random with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Blockway/Agens-Volundr-tiny-random

SGLang

How to use Blockway/Agens-Volundr-tiny-random 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 "Blockway/Agens-Volundr-tiny-random" \
    --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": "Blockway/Agens-Volundr-tiny-random",
		"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 "Blockway/Agens-Volundr-tiny-random" \
        --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": "Blockway/Agens-Volundr-tiny-random",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Blockway/Agens-Volundr-tiny-random with Docker Model Runner:
```
docker model run hf.co/Blockway/Agens-Volundr-tiny-random
```

Agens-Volundr-tiny-random

File size: 1,646 Bytes

973876f

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  "attention_bias": false,
  "attention_dropout": 0.0,
  "attn_output_gate": true,
  "bos_token_id": 248044,
  "dtype": "bfloat16",
  "eos_token_id": 248046,
  "full_attention_interval": 4,
  "head_dim": 128,
  "hidden_act": "silu",
  "hidden_size": 64,
  "initializer_range": 0.02,
  "intermediate_size": 256,
  "layer_types": [
    "linear_attention",
    "linear_attention",
    "full_attention",
    "full_attention"
  ],
  "linear_conv_kernel_dim": 4,
  "linear_key_head_dim": 128,
  "linear_num_key_heads": 2,
  "linear_num_value_heads": 6,
  "linear_value_head_dim": 128,
  "mamba_ssm_dtype": "float32",
  "max_position_embeddings": 32768,
  "model_type": "volundr_text",
  "mtp_num_hidden_layers": 0,
  "mtp_use_dedicated_embeddings": false,
  "num_attention_heads": 2,
  "num_hidden_layers": 4,
  "num_key_value_heads": 1,
  "output_gate_type": "swish",
  "pad_token_id": 248044,
  "partial_rotary_factor": 0.25,
  "rms_norm_eps": 1e-06,
  "rope_parameters": {
    "mrope_interleaved": true,
    "mrope_section": [
      6,
      5,
      5
    ],
    "partial_rotary_factor": 0.25,
    "rope_theta": 10000000,
    "rope_type": "default"
  },
  "tie_word_embeddings": false,
  "use_cache": true,
  "vocab_size": 248320,
  "architectures": [
    "VolundrForCausalLM"
  ],
  "mixer_types": [
    "kda",
    "kda",
    "bcsa",
    "attn"
  ],
  "new_block_indices": [],
  "kda_gate_rank": 16,
  "bcsa_window": 64,
  "bcsa_compress": 4,
  "bcsa_topk": 8,
  "engram_layers": [
    1
  ],
  "mhc_streams": 4,
  "engram_orders": [
    2,
    3
  ],
  "engram_rows": 4099,
  "engram_dim": 32,
  "mhc_sinkhorn_iters": 10,
  "bcsa_impl": "ref"
}