Instructions to use tiny-random/falcon-h1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tiny-random/falcon-h1 with Transformers:

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

pipe = pipeline("text-generation", model="tiny-random/falcon-h1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("tiny-random/falcon-h1")
model = AutoModelForCausalLM.from_pretrained("tiny-random/falcon-h1", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use tiny-random/falcon-h1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tiny-random/falcon-h1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tiny-random/falcon-h1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tiny-random/falcon-h1

SGLang

How to use tiny-random/falcon-h1 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 "tiny-random/falcon-h1" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tiny-random/falcon-h1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "tiny-random/falcon-h1" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tiny-random/falcon-h1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use tiny-random/falcon-h1 with Docker Model Runner:
```
docker model run hf.co/tiny-random/falcon-h1
```

falcon-h1

File size: 1,587 Bytes

25df695

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    "FalconH1ForCausalLM"
  ],
  "attention_bias": false,
  "attention_dropout": 0.0,
  "attention_in_multiplier": 1.0,
  "attention_out_multiplier": 0.0375,
  "attn_layer_indices": null,
  "bos_token_id": 1,
  "embedding_multiplier": 5.656854249492381,
  "eos_token_id": 11,
  "head_dim": 32,
  "hidden_act": "silu",
  "hidden_size": 8,
  "initializer_range": 0.02,
  "intermediate_size": 64,
  "key_multiplier": 0.011048543456039804,
  "lm_head_multiplier": 0.0078125,
  "mamba_chunk_size": 128,
  "mamba_conv_bias": true,
  "mamba_d_conv": 4,
  "mamba_d_head": 32,
  "mamba_d_ssm": 256,
  "mamba_d_state": 32,
  "mamba_expand": 32,
  "mamba_n_groups": 2,
  "mamba_n_heads": 8,
  "mamba_norm_before_gate": false,
  "mamba_proj_bias": false,
  "mamba_rms_norm": true,
  "mamba_use_mlp": true,
  "max_position_embeddings": 262144,
  "mlp_bias": false,
  "mlp_expansion_factor": 8,
  "mlp_multipliers": [
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    0.011160714285714284
  ],
  "model_type": "falcon_h1",
  "num_attention_heads": 8,
  "num_hidden_layers": 2,
  "num_key_value_heads": 4,
  "num_logits_to_keep": 1,
  "pad_token_id": 0,
  "projectors_bias": false,
  "rms_norm_eps": 1e-05,
  "rope_scaling": null,
  "rope_theta": 100000000000.0,
  "ssm_in_multiplier": 0.25,
  "ssm_multipliers": [
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    0.5,
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  ],
  "ssm_out_multiplier": 0.08838834764831845,
  "tie_word_embeddings": true,
  "torch_dtype": "bfloat16",
  "transformers_version": "4.52.0.dev0",
  "use_cache": true,
  "vocab_size": 261120
}