managing-fireworks-ai

name	managing-fireworks-ai
description	Use when working with Fireworks Ai — fireworks AI inference platform management covering models, deployments, fine-tuning, and usage analytics. Use when monitoring API usage, analyzing model performance, reviewing fine-tuning jobs, checking available models, or troubleshooting Fireworks AI API issues.
connection_type	fireworks-ai
preload	false

Fireworks AI Management Skill

Manage and analyze Fireworks AI platform resources including models, deployments, and fine-tuning.

API Conventions

Authentication

All API calls use Bearer API key, injected automatically.

Base URL

https://api.fireworks.ai/inference/v1 (inference) and https://api.fireworks.ai/v1 (management)

Core Helper Function

#!/bin/bash

fireworks_api() {
    local method="$1"
    local endpoint="$2"
    local data="${3:-}"

    if [ -n "$data" ]; then
        curl -s -X "$method" \
            -H "Authorization: Bearer $FIREWORKS_API_KEY" \
            -H "Content-Type: application/json" \
            "https://api.fireworks.ai${endpoint}" \
            -d "$data"
    else
        curl -s -X "$method" \
            -H "Authorization: Bearer $FIREWORKS_API_KEY" \
            "https://api.fireworks.ai${endpoint}"
    fi
}

Output Rules

• Target ≤50 lines per script output
• Use jq to extract only needed fields
• Never dump full API responses

Phase 1: Discovery

#!/bin/bash
echo "=== Available Models ==="
fireworks_api GET "/inference/v1/models" \
    | jq -r '.data[] | "\(.id)\t\(.context_length // "N/A")"' \
    | head -25

echo ""
echo "=== Account Info ==="
fireworks_api GET "/v1/accounts/me" \
    | jq '{id: .id, name: .name, tier: .tier}'

echo ""
echo "=== Fine-Tuning Jobs ==="
fireworks_api GET "/v1/accounts/me/fineTuningJobs" \
    | jq -r '.jobs[] | "\(.id[0:16])\t\(.model[0:30])\t\(.status)\t\(.createdAt[0:10])"' \
    | column -t | head -10

echo ""
echo "=== Deployments ==="
fireworks_api GET "/v1/accounts/me/deployedModels" \
    | jq -r '.deployedModels[] | "\(.id[0:16])\t\(.model[0:30])\t\(.state)\t\(.createdAt[0:10])"' \
    | head -10

Phase 2: Analysis

Usage & Health

#!/bin/bash
echo "=== API Health Check ==="
RESULT=$(fireworks_api POST "/inference/v1/chat/completions" '{"model": "accounts/fireworks/models/llama-v3p1-8b-instruct", "messages": [{"role": "user", "content": "test"}], "max_tokens": 1}')
echo "$RESULT" | jq '{model: .model, usage: .usage}' 2>/dev/null || echo "Error: $(echo $RESULT | jq -r '.error.message // "unknown"')"

echo ""
echo "=== Model Categories ==="
fireworks_api GET "/inference/v1/models" \
    | jq -r '[.data[] | .owned_by // "fireworks"] | group_by(.) | map({(.[0]): length}) | add'

Fine-Tuning & Deployment Health

#!/bin/bash
echo "=== Fine-Tuning Status Summary ==="
fireworks_api GET "/v1/accounts/me/fineTuningJobs" \
    | jq -r '.jobs[] | .status' | sort | uniq -c | sort -rn

echo ""
echo "=== Active Deployments ==="
fireworks_api GET "/v1/accounts/me/deployedModels" \
    | jq -r '.deployedModels[] | select(.state == "DEPLOYED") | "\(.id[0:16])\t\(.model[0:30])\t\(.state)\t\(.replicas // 1) replicas"' \
    | head -10

echo ""
echo "=== Custom Models ==="
fireworks_api GET "/v1/accounts/me/models" \
    | jq -r '.models[] | "\(.id[0:20])\t\(.displayName // .id)\t\(.createdAt[0:10])"' \
    | head -10

Output Format

=== Fireworks AI Account: <name> | Tier: <tier> ===

--- Models ---
Available: <n>  Custom: <n>

--- Fine-Tuning ---
Active: <n>  Completed: <n>  Failed: <n>

--- Deployments ---
Active: <n>  Total Replicas: <n>

Anti-Hallucination Rules

1. NEVER assume resource names — always discover via CLI/API in Phase 1 before referencing in Phase 2.
2. NEVER fabricate metric names or dimensions — verify against the service documentation or --help output.
3. NEVER mix CLI commands between service versions — confirm which version/API you are targeting.
4. ALWAYS use the discovery → verify → analyze chain — every resource referenced must have been discovered first.
5. ALWAYS handle empty results gracefully — an empty response is valid data, not an error to retry.

Counter-Rationalizations

Shortcut	Counter	Why
"I'll skip discovery and check known resources"	Always run Phase 1 discovery first	Resource names change, new resources appear — assumed names cause errors
"The user only asked for a quick check"	Follow the full discovery → analysis flow	Quick checks miss critical issues; structured analysis catches silent failures
"Default configuration is probably fine"	Audit configuration explicitly	Defaults often leave logging, security, and optimization features disabled
"Metrics aren't needed for this"	Always check relevant metrics when available	API/CLI responses show current state; metrics reveal trends and intermittent issues
"I don't have access to that"	Try the command and report the actual error	Assumed permission failures prevent useful investigation; actual errors are informative

Common Pitfalls

• Model IDs: Use full path format (e.g., accounts/fireworks/models/llama-v3p1-8b-instruct)
• Two API bases: Inference endpoints use /inference/v1, management uses /v1
• OpenAI-compatible: Inference API follows OpenAI chat/completions format
• Rate limits: Vary by tier; check response headers