Text Generation
Transformers
Safetensors
English
Portuguese
llama
hf-inference
education
logic
math
low-resource
open-source
causal-lm
lxcorp
conversational
text-generation-inference
Instructions to use lxcorp/lambda-1v-1B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
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license: mit
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datasets:
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- HuggingFaceH4/MATH
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language:
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- en
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tags:
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inference:
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parameters:
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max_new_tokens: 256
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temperature: 0.7
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# lambdai · TinyLlama-1.1B finetuned on Number Theory
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[](https://huggingface.co/lambdaindie)
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**lambdai** é o primeiro modelo oficial da organização **Lambda (Λ)** — uma startup solo angolana de pesquisa em IA liderada por Marius Jabami.
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Esse modelo foi finetunado a partir do [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) usando **LoRA + quantização em 8 bits**, com foco em **raciocínio matemático simbólico**, especialmente **teoria dos números**.
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## Dataset
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**Parâmetros LoRA**:
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- `r=8`, `alpha=16`
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- `target_modules=["q_proj", "v_proj"]`
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- Quantização 8-bit (QLoRA)
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**Formato de entrada:**
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```text
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Problem: <descrição do problema>
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Solution:
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Exemplo de uso
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model = AutoModelForCausalLM.from_pretrained("lambdaindie/lambdai")
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inputs = tokenizer(prompt, return_tensors="pt")
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outputs = model.generate(**inputs, max_new_tokens=256)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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Aplicações
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IA explicativa para matemática
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Tutores autônomos com raciocínio passo a passo
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Assistência em resolução simbólica
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Agentes educacionais
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Treinamento de reasoning agents
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Sobre a Lambda
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Λ Lambda é uma startup indie fundada por Marius Jabami, com foco em IA educacional, modelos compactos e agentes autônomos. lambdai é parte do ΛCore, núcleo de pesquisa e experimentação em LLMs e raciocínio simbólico.
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Links
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Lambda Indie @ Hugging Face
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TinyLlama Base Model
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Dataset: HuggingFaceH4/MATH
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Licença
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MIT License — uso livre para fins educacionais, de pesquisa ou pessoais.
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license: mit
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datasets:
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language:
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metrics:
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- bertscore
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- accuracy
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new_version: lambdaindie/lambdai
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pipeline_tag: text-generation
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library_name: transformers
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tags:
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- code
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# lambdai
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**lambdai** é o primeiro modelo oficial da organização [lambdaindie](https://huggingface.co/lambdaindie).
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Ele foi treinado com foco em **raciocínio matemático**, usando o subset `number_theory` do dataset [HuggingFaceH4/MATH](https://huggingface.co/datasets/HuggingFaceH4/MATH).
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Esse modelo é baseado no [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) e foi finetunado com **LoRA** e **quantização de 8 bits**, otimizando para dispositivos com pouca memória.
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## Exemplo de uso
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model = AutoModelForCausalLM.from_pretrained("lambdaindie/lambdai")
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inputs = tokenizer(prompt, return_tensors="pt")
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outputs = model.generate(**inputs, max_new_tokens=256)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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