Instructions to use axolotl-ai-co/Falcon-E-1.2-3B-Exp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use axolotl-ai-co/Falcon-E-1.2-3B-Exp with Transformers:

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

pipe = pipeline("text-generation", model="axolotl-ai-co/Falcon-E-1.2-3B-Exp")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("axolotl-ai-co/Falcon-E-1.2-3B-Exp")
model = AutoModelForCausalLM.from_pretrained("axolotl-ai-co/Falcon-E-1.2-3B-Exp", 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 axolotl-ai-co/Falcon-E-1.2-3B-Exp with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "axolotl-ai-co/Falcon-E-1.2-3B-Exp"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "axolotl-ai-co/Falcon-E-1.2-3B-Exp",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/axolotl-ai-co/Falcon-E-1.2-3B-Exp

SGLang

How to use axolotl-ai-co/Falcon-E-1.2-3B-Exp 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 "axolotl-ai-co/Falcon-E-1.2-3B-Exp" \
    --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": "axolotl-ai-co/Falcon-E-1.2-3B-Exp",
		"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 "axolotl-ai-co/Falcon-E-1.2-3B-Exp" \
        --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": "axolotl-ai-co/Falcon-E-1.2-3B-Exp",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use axolotl-ai-co/Falcon-E-1.2-3B-Exp with Docker Model Runner:
```
docker model run hf.co/axolotl-ai-co/Falcon-E-1.2-3B-Exp
```

ybelkada commited on Apr 30

Commit

66cef3e

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@@ -4,6 +4,7 @@ tags:
 - axolotl
 - edge
 - bitnet
 ---
 # Falcon-E-1.2-3B-Exp
@@ -60,4 +61,8 @@ You can further fine-tune this model, or the base model using their prequantized
 - [Base prequantized model](https://huggingface.co/tiiuae/Falcon-E-3B-Base-prequantized)
 - [`Falcon-E-1.2-3B-Exp` base model](https://huggingface.co/axolotl-ai/Falcon-E-1.2-3B-Exp-prequantized)
-- [`DPO fine-tuning config`](https://github.com/axolotl-ai-cloud/axolotl-ai-co/tree/main/examples/falcon-e/falcon-e-3b-dpo.yaml)

 - axolotl
 - edge
 - bitnet
+license: apache-2.0
 ---
 # Falcon-E-1.2-3B-Exp
 - [Base prequantized model](https://huggingface.co/tiiuae/Falcon-E-3B-Base-prequantized)
 - [`Falcon-E-1.2-3B-Exp` base model](https://huggingface.co/axolotl-ai/Falcon-E-1.2-3B-Exp-prequantized)
+- [`DPO fine-tuning config`](https://github.com/axolotl-ai-cloud/axolotl-ai-co/tree/main/examples/falcon-e/falcon-e-3b-dpo.yaml)
+## Aknowledgement
+Falcon-E-Chat-Exp models are built using Falcon LLM technology from the [Technology Innovation Institute](https://huggingface.co/tiiuae).