haylanandClaude-Bot
100fed4274
Qwen3 spends output tokens on reasoning_content before writing content. Open WebUI's default chat request doesn't set max_tokens, so it fell through to llama.cpp's low default and the model ran out mid-thought, returning finish_reason=length with empty content — no reply shown in Open WebUI. Confirmed via a manual /v1/chat/completions call: works with max_tokens=2000, fails without it. litellm_params.max_tokens is a default, not a cap — any caller (or Open WebUI's per-model Advanced Params) that sets its own max_tokens still overrides it. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Q2CR8yawSf7pwAVYnjwFea
35 lines
1.7 KiB
YAML
35 lines
1.7 KiB
YAML
model_list:
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- model_name: qwen3.8-27b-local
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litellm_params:
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# Static name — llama.cpp serves whatever model it loaded regardless of
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# what's requested here; this string isn't shell-expanded (this file
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# isn't docker-compose.yml, .env vars don't reach it).
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model: openai/qwen3.8-27b-local
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api_base: http://llama-server:8080/v1
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api_key: local
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# Qwen3 is a reasoning model — it spends output tokens on
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# reasoning_content before ever writing content. Callers that don't
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# set their own max_tokens (Open WebUI's default request didn't) hit
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# llama.cpp's low default, so the model runs out mid-thought and
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# content comes back empty. This is a floor, not a cap — any caller
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# that passes its own max_tokens still overrides it.
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max_tokens: 4096
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model_info:
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# Shadow cloud-cost estimate — priced against Claude Sonnet 5's published
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# rate, not real spend (this proxy only ever routes to the local model).
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# Source: https://platform.claude.com/docs/en/about-claude/pricing,
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# checked 2026-08-25. Update these two numbers if that page changes.
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input_cost_per_token: 0.000002 # $2 / MTok
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output_cost_per_token: 0.00001 # $10 / MTok
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router_settings:
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# ponytail: LiteLLM's request-prioritization scheduler is beta (see
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# docs/proxy-request-priority.md) — exact settings key/shape must be
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# confirmed against LiteLLM's current docs and smoke-tested against
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# llama.cpp before workloads depend on it. Single-instance deployment,
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# no Redis configured — add one only if the scheduler turns out to need it.
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enable_priority_scheduling: true
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general_settings:
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master_key: os.environ/LITELLM_MASTER_KEY
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