Instructions to use aduncan94/EnhancAR-Sorted with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use aduncan94/EnhancAR-Sorted with Transformers:

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

pipe = pipeline("text-generation", model="aduncan94/EnhancAR-Sorted")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("aduncan94/EnhancAR-Sorted")
model = AutoModelForCausalLM.from_pretrained("aduncan94/EnhancAR-Sorted")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use aduncan94/EnhancAR-Sorted with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "aduncan94/EnhancAR-Sorted"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "aduncan94/EnhancAR-Sorted",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/aduncan94/EnhancAR-Sorted

SGLang

How to use aduncan94/EnhancAR-Sorted 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 "aduncan94/EnhancAR-Sorted" \
    --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": "aduncan94/EnhancAR-Sorted",
		"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 "aduncan94/EnhancAR-Sorted" \
        --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": "aduncan94/EnhancAR-Sorted",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use aduncan94/EnhancAR-Sorted with Docker Model Runner:
```
docker model run hf.co/aduncan94/EnhancAR-Sorted
```

EnhancAR-Sorted

File size: 1,002 Bytes

4bf9a40

{
  "architectures": [
    "JambaForCausalLM"
  ],
  "attention_dropout": 0.0,
  "attn_layer_offset": 4,
  "attn_layer_period": 8,
  "bos_token_id": 9,
  "eos_token_id": 7,
  "expert_layer_offset": 1,
  "expert_layer_period": 2,
  "hidden_act": "silu",
  "hidden_size": 256,
  "initializer_range": 0.02,
  "intermediate_size": 1024,
  "mamba_conv_bias": true,
  "mamba_d_conv": 4,
  "mamba_d_state": 16,
  "mamba_dt_rank": 16,
  "mamba_expand": 2,
  "mamba_proj_bias": false,
  "max_position_embeddings": 262144,
  "model_type": "jamba",
  "num_attention_heads": 16,
  "num_experts": 16,
  "num_experts_per_tok": 2,
  "num_hidden_layers": 24,
  "num_key_value_heads": 8,
  "num_logits_to_keep": 1,
  "output_router_logits": true,
  "pad_token_id": 6,
  "rms_norm_eps": 1e-06,
  "router_aux_loss_coef": 0.001,
  "sliding_window": null,
  "tie_word_embeddings": false,
  "torch_dtype": "float32",
  "transformers_version": "4.48.2",
  "use_cache": false,
  "use_mamba_kernels": true,
  "vocab_size": 16
}