Instructions to use axolotl-ai-co/Falcon-E-1.2-3B-Exp-prequantized 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-prequantized 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-prequantized")
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-prequantized")
model = AutoModelForCausalLM.from_pretrained("axolotl-ai-co/Falcon-E-1.2-3B-Exp-prequantized")
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

vLLM

How to use axolotl-ai-co/Falcon-E-1.2-3B-Exp-prequantized 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-prequantized"
# 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-prequantized",
		"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-prequantized

SGLang

How to use axolotl-ai-co/Falcon-E-1.2-3B-Exp-prequantized 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-prequantized" \
    --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-prequantized",
		"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-prequantized" \
        --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-prequantized",
		"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-prequantized with Docker Model Runner:
```
docker model run hf.co/axolotl-ai-co/Falcon-E-1.2-3B-Exp-prequantized
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Falcon-E-1.2-3B-Exp-prequantized

This is the model card of Falcon-E-1.2-3B-Exp, a ternary (1.58bits) language model trained on SFT agentic, and STEM data using axolotl framework combined with onebitllm library.

The model has been trained starting from tiiuae/Falcon-E-3B-Base-prequantized checkpoint using full-finetuning for 3 epochs. Below are the hyper-parameters used for fine-tuning:

This model is the prequantized version of axolotl-ai-co/Falcon-E-1.2-3B-Exp that can be used for further fine-tuning.

micro_batch_size: 1
num_epochs: 3
optimizer: adamw_torch
lr_scheduler: cosine
learning_rate: 8.0e-4
# adamw hyperparams
adam_beta1: 0.9
adam_beta2: 0.95
warmup_steps: 128

And we used a context parallel size of 8.

Usage

The model uses think mode by default, this can be disabled and switched to non-thiking mode. You can use the model with different frameworks such as HF transformers, llama.cpp or mlx-lm

transformers

transformers chat axolotl-ai-co/Falcon-E-1.2-3B-Exp

llama.cpp

# thinking mode
llama-cli -m axolotl-ai-co/Falcon-E-1.2-3B-Exp-GGUF:TQ2_0 --reasoning-format auto --temp 0.2 -cnv

# non thinking mode
llama-cli -m axolotl-ai-co/Falcon-E-1.2-3B-Exp-GGUF:TQ2_0 --reasoning-format auto --temp 0.2 -cnv --reasoning-budget 0.0

mlx-lm

mlx_lm.chat axolotl-ai-co/Falcon-E-1.2-3B-Exp --temperature 0.2

Further fine-tuning the model

You can further fine-tune this model, or the base model using their prequantized version. Refer to the axolotl config to get started on fine-tuning these models: