Instructions to use katuni4ka/tiny-random-snowflake with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use katuni4ka/tiny-random-snowflake with Transformers:

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

pipe = pipeline("text-generation", model="katuni4ka/tiny-random-snowflake", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("katuni4ka/tiny-random-snowflake", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use katuni4ka/tiny-random-snowflake with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "katuni4ka/tiny-random-snowflake"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "katuni4ka/tiny-random-snowflake",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/katuni4ka/tiny-random-snowflake

SGLang

How to use katuni4ka/tiny-random-snowflake 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 "katuni4ka/tiny-random-snowflake" \
    --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": "katuni4ka/tiny-random-snowflake",
		"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 "katuni4ka/tiny-random-snowflake" \
        --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": "katuni4ka/tiny-random-snowflake",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use katuni4ka/tiny-random-snowflake with Docker Model Runner:
```
docker model run hf.co/katuni4ka/tiny-random-snowflake
```

tiny-random-snowflake

File size: 1,347 Bytes

bc0dc25

{
  "_name_or_path": "/home/ea/work/snowflake",
  "architectures": [
    "ArcticForCausalLM"
  ],
  "attention_dropout": 0,
  "auto_map": {
    "AutoConfig": "configuration_arctic.ArcticConfig",
    "AutoModel": "modeling_arctic.ArcticModel",
    "AutoModelForCausalLM": "modeling_arctic.ArcticForCausalLM",
    "AutoModelForSequenceClassification": "modeling_arctic.ArcticForSequenceClassification"
  },
  "bos_token_id": 1,
  "enable_expert_tensor_parallelism": false,
  "enc_index": [
    0,
    1,
    2,
    3
  ],
  "eos_token_id": 2,
  "hidden_act": "silu",
  "hidden_size": 32,
  "initializer_range": 0.02,
  "intermediate_size": 16,
  "max_position_embeddings": 128,
  "max_sequence_length": 128,
  "model_type": "arctic",
  "moe_eval_capacity_factor": 1,
  "moe_layer_frequency": 1,
  "moe_min_capacity": 0,
  "moe_token_dropping": true,
  "moe_train_capacity_factor": 1,
  "num_attention_heads": 4,
  "num_experts_per_tok": 2,
  "num_hidden_layers": 4,
  "num_key_value_heads": 4,
  "num_local_experts": 4,
  "parallel_attn_mlp_res": true,
  "quantization": null,
  "rms_norm_eps": 1e-05,
  "rope_theta": 10000,
  "router_aux_loss_coef": 0.001,
  "sliding_window": null,
  "tie_word_embeddings": false,
  "torch_dtype": "float32",
  "transformers_version": "4.40.2",
  "use_cache": true,
  "use_residual": true,
  "vocab_size": 32000
}