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", 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 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
```

lambda-1v-1B / README.md

mariusjabami

Update README.md

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	---
	base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
	library_name: transformers
	license: mit
	language:
	- en
	- pt
	metrics:
	- accuracy
	pipeline_tag: text-generation
	tags:
	- hf-inference
	- education
	- logic
	- math
	- low-resource
	- transformers
	- open-source
	- causal-lm
	- lxcorp
	---

	# 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`](https://huggingface.co/datasets/HuggingFaceH4/MATH) — subset: `number_theory`
	- Max Tokens per Sample: 512
	- Batch Size: 20 per device
	- Epochs: 3

	---

	## Example Usage (Python)

	```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.


	---

	---