Instructions to use SparseLLM/ReluFalcon-40B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SparseLLM/ReluFalcon-40B with Transformers:

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

pipe = pipeline("text-generation", model="SparseLLM/ReluFalcon-40B", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("SparseLLM/ReluFalcon-40B", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("SparseLLM/ReluFalcon-40B", trust_remote_code=True)

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use SparseLLM/ReluFalcon-40B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SparseLLM/ReluFalcon-40B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SparseLLM/ReluFalcon-40B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/SparseLLM/ReluFalcon-40B

SGLang

How to use SparseLLM/ReluFalcon-40B 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 "SparseLLM/ReluFalcon-40B" \
    --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": "SparseLLM/ReluFalcon-40B",
		"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 "SparseLLM/ReluFalcon-40B" \
        --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": "SparseLLM/ReluFalcon-40B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use SparseLLM/ReluFalcon-40B with Docker Model Runner:
```
docker model run hf.co/SparseLLM/ReluFalcon-40B
```

ReluFalcon-40B

File size: 1,229 Bytes

6723d12

{
    "_name_or_path": "/mnt/hwfile/share_data/songyixin/hf/huggingface/falcon-relu",
    "alibi": false,
    "apply_residual_connection_post_layernorm": false,
    "architectures": [
        "FalconForCausalLM"
    ],
    "attention_dropout": 0.0,
    "auto_map": {
        "AutoConfig": "configuration_falcon.FalconConfig",
        "AutoModel": "modeling_falcon.FalconModel",
        "AutoModelForCausalLM": "modeling_falcon.FalconForCausalLM",
        "AutoModelForQuestionAnswering": "modeling_falcon.FalconForQuestionAnswering",
        "AutoModelForSequenceClassification": "modeling_falcon.FalconForSequenceClassification",
        "AutoModelForTokenClassification": "modeling_falcon.FalconForTokenClassification"
    },
    "bias": false,
    "bos_token_id": 11,
    "eos_token_id": 11,
    "hidden_dropout": 0.0,
    "hidden_size": 8192,
    "initializer_range": 0.02,
    "layer_norm_epsilon": 1e-05,
    "model_type": "falcon",
    "multi_query": true,
    "new_decoder_architecture": true,
    "num_attention_heads": 128,
    "num_hidden_layers": 60,
    "num_kv_heads": 8,
    "parallel_attn": true,
    "torch_dtype": "float32",
    "transformers_version": "4.33.2",
    "use_cache": true,
    "vocab_size": 65024
}