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deepseek

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R1 Distill Llama 70b

DeepSeek R1 Distill Llama 70B: reasoning on demand

A tradução deste card ainda não está disponível; parte do conteúdo aparece em inglês.

Janela de contexto (tokens): 8.192
Inteligência: 7.9
Velocidade: —–
Custo/tarefa: $0.010

Sobre este modelo

DeepSeek R1 Distill Llama 70B is a distilled reasoning model designed to think through problems before answering. It excels at tasks that require logical deduction, chain-of-thought explanations, and careful planning — making it ideal for technical writing, tutoring, code analysis, and decision support. Unlike standard language models that jump to conclusions, this model often shows its intermediate reasoning steps, giving you insight into how it arrived at an answer. Use it whenever you need depth over speed, or when a problem benefits from deliberate analysis.

Melhores casos de uso

Step-by-step math and physics problems
Debugging and explaining code
Crafting logical arguments or essays
Structured decision-making and planning
Multi-step reasoning tasks like puzzles and proofs

Pontos fortes

Deep reasoning chain

Produces visible step-by-step logic, ideal for learning and verification.

Distilled efficiency

Packs advanced reasoning into a model that runs faster than its full-size base.

Versatile reasoning

Handles everything from math to creative writing with consistent logical flow.

Dicas

Use clear, step-by-step prompts to get the best reasoning output.
If the answer seems too complex, ask for a simpler explanation.
Specify 'show your reasoning' or 'explain step by step' to unlock its full strength.
Combine with creative prompts for structured story ideas or arguments.

Limitações

May over-explain simple questions, adding unnecessary complexity.
Can be slower compared to direct answer models due to reasoning steps.
Occasionally shows false confidence in flawed reasoning chains.

Exemplos de geração

Exemplo em breve

Scientific reasoning challenge

If a ball is dropped from a height of 100 meters, ignoring air resistance, calculate its velocity just before hitting the ground. Show every formula and step.

Exemplo em breve

Ethical dilemma analysis

Analyze the trolley problem using utilitarian and deontological ethics. Compare reasoning chains.

Exemplo em breve

Multi-step planning

Plan a two-week itinerary for a trip to Japan, considering budget of $3000, food preferences, and must-see cultural sites. Explain your decisions.

Prompts de exemplo

Math problem solving

Solve this step by step: A train leaves station A at 50 mph. Another train leaves station B at 70 mph. If they are 300 miles apart and heading toward each other, when do they meet?

Code debugging

Explain why this Python function fails and fix it: def add(a, b): return a + b but it returns None.

Logical argument

Argue both sides of the statement: 'Social media does more harm than good.' Show reasoning for each.

Parâmetros

seed

Fixes the random generation for reproducible outputs.

top_p

Nucleus sampling: sets a probability threshold for word choices; 1 means most random, 0.1 very focused.

max_tokens

Maximum length of the generated answer; adjust for short or long responses.

temperature

Controls randomness: lower for precise logic, higher for creative variety.

presence_penalty

Encourages the model to talk about new topics by penalizing repeated concepts.

frequency_penalty

Reduces repetitive phrases by penalizing words that appear often.

Preços

Preço sob consulta

Os preços exibidos são o que você paga na Muitto. Sem surpresas.

Perguntas frequentes

How is this different from a regular language model?

It explicitly reasons through problems step by step, often showing its work, making it better for logic-heavy tasks.

Can it handle creative writing?

Yes, but its strength is structured creativity like plotting, outlines, or argument-driven prose.

Does it always show its reasoning?

Not always — you can encourage it by asking for step-by-step explanations in your prompt.