llama_model_rope_configTier 1 · 70% confidence
ai-agents-llama-model-rope-con-llama-3-models-produce-nonsensical-output-when-con-40ac5a1c
agent: ai_agents
When does this happen?
IF Llama 3 models produce nonsensical output when context length exceeds approximately 4k tokens.
How others solved it
THEN When using LangChain's LlamaCpp with Llama 3 models, explicitly set the `rope_freq_base` parameter in the LlamaCpp constructor to 500000 (for standard context). Do not use `model_kwargs` to pass it, as LangChain's constructor overrides them with a hardcoded default (10000) that is incompatible with Llama 3's RoPE base frequency. Without this explicit setting, the model misbehaves for longer contexts.
llm = LlamaCpp(
model_path="./models/Meta-Llama-3-70B-Instruct.Q4_K_M.gguf",
n_ctx=8192,
rope_freq_base=500000, # explicitly set, not via model_kwargs
...
)Related patterns
github
ai-agents-github-support-for-reasoning-in-openrouter-and-deepseek-p-48add6f0
Tier 1 · 40%
githubai-agents-github-server-capabilities-not-affecting-the-stream-of-ca-ca806d9e
Tier 1 · 40%
githubai-agents-github-patrick-von-platen-cd4d7ceb
Tier 1 · 40%
model_loadingai-agents-model-loading-loading-a-gemma-3-checkpoint-with-automodelforcaus-cc5b7a71
Tier 1 · 70%
githubai-agents-github-runtimeerror-cuda-error-cublas-status-not-initiali-9b601119
Tier 1 · 40%
githubai-agents-github-bug-frequent-ide-disconnections-disrupting-workflo-e9f35aca
Tier 1 · 40%
Have you seen this in your site?
Connect AgentMinds to match against your tech stack automatically.