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While trying to access the llama-3.3-70b-versatile model via groq encountered this error.
Minimal reproducible example
fromlangchain_groqimportChatGroqfromlangchain.promptsimportPromptTemplatefromlangchain.chainsimportLLMChainload_dotenv()
GROQ_API_KEY=os.getenv("GROQ_API_KEY")
client=Groq(api_key=GROQ_API_KEY)
MODEL_NAME="llama-3.3-70b-versatile"# prompt templateprompt_template=PromptTemplate(
input_variables=["question"],
template="""You are a helpful assistant. Answer the following question clearly and concisely:Question: {question}Answer:"""
)
# Initialize the LLaMA model via Groqllm=ChatGroq(
api_key=GROQ_API_KEY,
model=MODEL_NAME,
temperature=0.2,
max_tokens=2048
)
# Wrap it into an LLMChain with the promptllm_chain=LLMChain(
llm=llm,
prompt=prompt_template,
verbose=True
)
# Example usageuser_question="What is the capital of Maharashtra?"response=llm_chain.run(user_question)
print("Response:")
print(response)
Output
Runtime Environment
Model: llama-3.3-70b-versatile
Using via huggingface?: No
Platform: Streamlit
Additional context
The llama-3.3-70b-versatile model was being used via groq API (free version). This error occured between 12:00 IST and 12:30 ISt on Friday 13th June 2025. Mistake in outputs was also noticed such as chinese alphabets in between a marathi language answer.
A custom PromptTemplate was used while inferencing and was followed accurately until before this error showed up. The quality of results seem to have taken a hit after this.
Unexpected Behaviour
While trying to access the
llama-3.3-70b-versatilemodel via groq encountered this error.Minimal reproducible example
Output
Runtime Environment
llama-3.3-70b-versatileAdditional context
The
llama-3.3-70b-versatilemodel was being used via groq API (free version). This error occured between 12:00 IST and 12:30 ISt on Friday 13th June 2025. Mistake in outputs was also noticed such as chinese alphabets in between a marathi language answer.A custom PromptTemplate was used while inferencing and was followed accurately until before this error showed up. The quality of results seem to have taken a hit after this.