Instructions to use lxcorp/lambda-1v-1B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use lxcorp/lambda-1v-1B with Transformers:

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

pipe = pipeline("text-generation", model="lxcorp/lambda-1v-1B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("lxcorp/lambda-1v-1B")
model = AutoModelForCausalLM.from_pretrained("lxcorp/lambda-1v-1B")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use lxcorp/lambda-1v-1B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "lxcorp/lambda-1v-1B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lxcorp/lambda-1v-1B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/lxcorp/lambda-1v-1B

SGLang

How to use lxcorp/lambda-1v-1B 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 "lxcorp/lambda-1v-1B" \
    --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": "lxcorp/lambda-1v-1B",
		"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 "lxcorp/lambda-1v-1B" \
        --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": "lxcorp/lambda-1v-1B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use lxcorp/lambda-1v-1B with Docker Model Runner:
```
docker model run hf.co/lxcorp/lambda-1v-1B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

lambda-1v-1b — Lightweight Math & Logic Reasoning Model

lambda-1v-1b is a compact, fine-tuned language model built on top of TinyLlama-1.1B-Chat-v1.0, designed for educational reasoning tasks in both Portuguese and English. It focuses on logic, number theory, and mathematics, delivering fast performance with minimal computational requirements.

Model Architecture

Base Model: TinyLlama-1.1B-Chat
Fine-Tuning Strategy: LoRA (applied to q_proj and v_proj)
Quantization: 8-bit (NF4 via bnb_config)
Dataset: HuggingFaceH4/MATH — subset: number_theory
Max Tokens per Sample: 512
Batch Size: 20 per device
Epochs: 3

Example Usage (Python)

from transformers import AutoTokenizer, AutoModelForCausalLM

model = AutoModelForCausalLM.from_pretrained("lxcorp/lambda-1v-1b")
tokenizer = AutoTokenizer.from_pretrained("lxcorp/lambda-1v-1b")

input_text = "Problema: Prove que 17 é um número primo."
inputs = tokenizer(input_text, return_tensors="pt")

output = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(output[0], skip_special_tokens=True))

About λχ Corp.

λχ Corp. is an indie tech corporation founded by Marius Jabami in Angola, focused on AI-driven educational tools, robotics, and lightweight software solutions. The lambdAI model is the first release in a planned series of educational LLMs optimized for reasoning, logic, and low-resource deployment.

Stay updated on the project at lxcorp.ai and huggingface.co/lxcorp.

Developed with care by Marius Jabami — Powered by ambition, faith, and open source.

Downloads last month: 623

Safetensors

Model size

1B params

Tensor type

F32

Model tree for lxcorp/lambda-1v-1B

Base model

TinyLlama/TinyLlama-1.1B-Chat-v1.0

Finetuned

(554)

this model

Quantizations

2 models

lxcorp
/

lambda-1v-1B

lambda-1v-1b — Lightweight Math & Logic Reasoning Model

Model Architecture

Example Usage (Python)

Model tree for lxcorp/lambda-1v-1B

Space using lxcorp/lambda-1v-1B 1