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

Libraries

How to use NeTSlab/hawk_mopbf_en_nl_zh_equal with Transformers:

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

pipe = pipeline("text-generation", model="NeTSlab/hawk_mopbf_en_nl_zh_equal", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("NeTSlab/hawk_mopbf_en_nl_zh_equal", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use NeTSlab/hawk_mopbf_en_nl_zh_equal with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/NeTSlab/hawk_mopbf_en_nl_zh_equal

SGLang

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

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

hawk_mopbf_en_nl_zh_equal

File size: 687 Bytes

{
  "architectures": [
    "HawkForCausalLM"
  ],
  "auto_map": {
    "AutoConfig": "modeling_hawk.HawkConfig",
    "AutoModelForCausalLM": "modeling_hawk.HawkForCausalLM",
    "AutoModelForSequenceClassification": "modeling_hawk.HawkForSequenceClassification",
    "AutoModel": "modeling_hawk.HawkModel"
  },
  "bos_token_id": 1,
  "conv_kernel": 4,
  "dtype": "float32",
  "eos_token_id": 2,
  "max_position_embeddings": 1024,
  "mlp_expansion": 3,
  "model_type": "hawk_rglru",
  "n_embd": 704,
  "n_layer": 12,
  "pad_token_id": 3,
  "rglru_c": 8.0,
  "rmsnorm_eps": 1e-06,
  "rnn_width": 768,
  "tie_word_embeddings": true,
  "transformers_version": "5.3.0",
  "vocab_size": 39953
}