Instructions to use dotan1111/BetaInfer_Configuration3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dotan1111/BetaInfer_Configuration3 with Transformers:

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

pipe = pipeline("text-generation", model="dotan1111/BetaInfer_Configuration3")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("dotan1111/BetaInfer_Configuration3")
model = AutoModelForCausalLM.from_pretrained("dotan1111/BetaInfer_Configuration3", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use dotan1111/BetaInfer_Configuration3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "dotan1111/BetaInfer_Configuration3"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dotan1111/BetaInfer_Configuration3",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/dotan1111/BetaInfer_Configuration3

SGLang

How to use dotan1111/BetaInfer_Configuration3 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 "dotan1111/BetaInfer_Configuration3" \
    --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": "dotan1111/BetaInfer_Configuration3",
		"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 "dotan1111/BetaInfer_Configuration3" \
        --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": "dotan1111/BetaInfer_Configuration3",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use dotan1111/BetaInfer_Configuration3 with Docker Model Runner:
```
docker model run hf.co/dotan1111/BetaInfer_Configuration3
```

BetaInfer_Configuration3

File size: 1,367 Bytes

d87678b

{
  "_name_or_path": "/groups/pupko/edodotan/BetaAlignPart3/test_architecture/mix_training_state_space_models_10_15_0p200_phylogenetic_trees/ZAMBA/layers_12_d_1408_e-4_ZAMBA/checkpoint-20480/",
  "architectures": [
    "ZambaForCausalLM"
  ],
  "attention_dropout": 0.0,
  "attention_head_dim": 176,
  "attention_hidden_size": 2816,
  "attn_layer_offset": 4,
  "attn_layer_period": 6,
  "bos_token_id": 1,
  "eos_token_id": 4,
  "hidden_act": "gelu",
  "hidden_mamba_act": "silu",
  "hidden_size": 1408,
  "initializer_range": 0.02,
  "intermediate_size": 4928,
  "layers_block_type": [
    "mamba",
    "mamba",
    "hybrid",
    "mamba",
    "mamba",
    "mamba",
    "mamba",
    "hybrid",
    "mamba",
    "mamba",
    "mamba",
    "mamba"
  ],
  "mamba_conv_bias": true,
  "mamba_d_conv": 4,
  "mamba_d_state": 16,
  "mamba_dt_rank": 88,
  "mamba_expand": 2,
  "mamba_proj_bias": false,
  "max_position_embeddings": 2048,
  "model_type": "zamba",
  "n_mamba_heads": 2,
  "num_attention_heads": 16,
  "num_heads": 44,
  "num_hidden_layers": 12,
  "num_key_value_heads": 16,
  "num_logits_to_keep": 1,
  "pad_token_id": 3,
  "rms_norm_eps": 1e-05,
  "time_step_floor": 0.0001,
  "time_step_max": 0.1,
  "time_step_min": 0.001,
  "torch_dtype": "float32",
  "transformers_version": "4.48.1",
  "use_cache": true,
  "use_mamba_kernels": true,
  "vocab_size": 6400
}