change llm model and slight prompt adjustment
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2 changed files with 4 additions and 4 deletions
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@ -22,7 +22,7 @@ spec:
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- name: ollama-storage
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mountPath: /root/.ollama
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command: ["/bin/sh", "-c"]
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args: ["ollama serve & sleep 5 && ollama pull qwen2.5:14b-instruct"]
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args: ["ollama serve & sleep 5 && ollama pull qwen2.5:32b-instruct"]
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- name: ollama-sidecar
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@ -9,10 +9,10 @@ data:
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DB_USER: "news_app"
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DB_PORT: "5432"
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LLM_URL: "http://localhost:11434"
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MODEL_NAME: "qwen2.5:14b-instruct"
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MODEL_NAME: "qwen2.5:32b-instruct"
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SUMMARY_PROMPT: |
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SUMMARY_PROMPT: |
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You are Sirius Media Trading Brief — a fast, Telegram-optimized news & trading insight generator.
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You are Sirius Media a fast, Telegram-optimized news & trading insight generator.
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Your ONLY source of information is the FINAL INPUT DATA below.
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You MUST NOT use any prior training data, external knowledge, recalled events, or assumptions.
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@ -29,7 +29,7 @@ data:
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• For weather-related news (storms, droughts, floods, hurricanes, freezes, heatwaves, etc.), always note potential effects on futures (corn, wheat, soy, coffee, natural gas, crude oil, etc.).
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• Identify 3–6 most impactful developments. For each: 4–6 sentences covering key facts, context, potential market consequences, and strategic/trading relevance.
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• List 4–7 unusual or standout stories (3–4 sentences each).
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• At the end, give 4–8 trade ideas (stocks, futures contracts, forex pairs) that could be influenced by the news.
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• At the end, give 4–8 trade ideas (stocks, futures contracts, forex pairs) that could be influenced by the news. This is the only time past training data can be used.
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- Format: Ticker / Symbol – Direction (Long/Short/Neutral) – Rationale (1–2 sentences) – Risk level (Low/Med/High)
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- Use real tickers/symbols only if clearly inferable from the data (e.g. company names mentioned).
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• Keep total output 600–1100 words. Be concise yet insightful.
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