Instructions to use Multi-Domain-Expert-Learning/falcon1b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Multi-Domain-Expert-Learning/falcon1b with Transformers:

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

pipe = pipeline("text-generation", model="Multi-Domain-Expert-Learning/falcon1b", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("Multi-Domain-Expert-Learning/falcon1b", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Multi-Domain-Expert-Learning/falcon1b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Multi-Domain-Expert-Learning/falcon1b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Multi-Domain-Expert-Learning/falcon1b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Multi-Domain-Expert-Learning/falcon1b

SGLang

How to use Multi-Domain-Expert-Learning/falcon1b 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 "Multi-Domain-Expert-Learning/falcon1b" \
    --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": "Multi-Domain-Expert-Learning/falcon1b",
		"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 "Multi-Domain-Expert-Learning/falcon1b" \
        --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": "Multi-Domain-Expert-Learning/falcon1b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Multi-Domain-Expert-Learning/falcon1b with Docker Model Runner:
```
docker model run hf.co/Multi-Domain-Expert-Learning/falcon1b
```

falcon1b / config.json

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Update config.json

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	{
	"_name_or_path": "student1/",
	"alibi": false,
	"apply_residual_connection_post_layernorm": false,
	"architectures": [
	"RWForCausalLM"
	],
	"attention_dropout": 0.0,
	"auto_map": {
	"AutoConfig": "tiiuae/falcon-7b--configuration_falcon.FalconConfig",
	"AutoModel": "tiiuae/falcon-7b--modeling_falcon.FalconModel",
	"AutoModelForCausalLM": "tiiuae/falcon-7b--modeling_falcon.FalconForCausalLM",
	"AutoModelForQuestionAnswering": "tiiuae/falcon-7b--modeling_falcon.FalconForQuestionAnswering",
	"AutoModelForSequenceClassification": "tiiuae/falcon-7b--modeling_falcon.FalconForSequenceClassification",
	"AutoModelForTokenClassification": "tiiuae/falcon-7b--modeling_falcon.FalconForTokenClassification"
	},
	"bias": false,
	"bos_token_id": 11,
	"eos_token_id": 11,
	"hidden_dropout": 0.0,
	"hidden_size": 4544,
	"init_metadata": {
	"copied_decoder_layers": [
	0,
	4,
	8,
	12,
	16,
	20,
	24,
	31
	],
	"teacher_type": "RefinedWebModel"
	},
	"initializer_range": 0.02,
	"layer_norm_epsilon": 1e-05,
	"model_type": "RefinedWebModel",
	"multi_query": true,
	"n_head": 71,
	"n_layer": 8,
	"output_hidden_states": true,
	"parallel_attn": true,
	"torch_dtype": "bfloat16",
	"transformers_version": "4.30.1",
	"use_cache": true,
	"vocab_size": 65024
	}