How do I fix a 401 Unauthorized error from abliteration.ai?

Check that your API key is set and sent as a Bearer token.

How do I fix a 404 Not Found error from abliteration.ai?

Make sure the base URL ends with /v1 and you call /chat/completions.

How do I fix a 400 Bad Request error from abliteration.ai?

Verify the model id and that messages are an array of { role, content } objects.

How do I fix a 429 Rate limit error from abliteration.ai?

Back off and retry. Use the Retry-After header for pacing.

Docs

Tool calling (function calling)

Let models call your functions via the OpenAI-compatible tools parameter. Works with existing SDKs — just switch the base URL.

Quick start

Base URL

curl https://api.abliteration.ai/v1/chat/completions \
  -H "Authorization: Bearer $ABLIT_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "abliterated-model",
    "messages": [
      { "role": "user", "content": "Look up the status of order 123." }
    ],
    "tools": [
      {
        "type": "function",
        "function": {
          "name": "get_order_status",
          "description": "Lookup an order status by id.",
          "parameters": {
            "type": "object",
            "properties": { "order_id": { "type": "string" } },
            "required": ["order_id"]
          }
        }
      }
    ]
  }'

Service notes

Pricing model: Usage-based pricing (~$5 per 1M tokens) billed on total tokens (input + output). See the API pricing page for current plans.
Data retention: No prompt/output retention by default. Operational telemetry (token counts, timestamps, error codes) is retained for billing and reliability.
Compatibility: OpenAI-style /v1/chat/completions request and response format with a base URL switch.
Latency: Depends on model size, prompt length, and load. Streaming reduces time-to-first-token.
Throughput: Team plans include priority throughput. Actual throughput varies with demand.
Rate limits: Limits vary by plan and load. Handle 429s with backoff and respect any Retry-After header.

How tool calling works

Tool calling follows a multi-step loop between your application and the model.

1. Define tools — Describe available functions with a JSON Schema in the tools array.
2. Send request — The model analyzes the user message and decides whether to call a tool.
3. Receive tool_calls — If the model invokes a tool, the response contains a tool_calls array instead of plain content.
4. Execute function — Your code runs the function with the provided arguments.
5. Return result — Send the function result back in a message with role: "tool" and the matching tool_call_id.
6. Get final answer — The model incorporates the tool result and returns a natural language response.

Tool definition schema

Each tool must specify type: "function" and a function object with name, description, and parameters (JSON Schema).

Tool definition

{
  "type": "function",
  "function": {
    "name": "get_weather",
    "description": "Get the current weather for a given location.",
    "parameters": {
      "type": "object",
      "properties": {
        "location": {
          "type": "string",
          "description": "City and state, e.g. San Francisco, CA"
        },
        "unit": {
          "type": "string",
          "enum": ["celsius", "fahrenheit"]
        }
      },
      "required": ["location"]
    }
  }
}

Response with tool_calls

When the model invokes a tool, the response contains a tool_calls array with the function name and arguments as a JSON string.

Example response

{
  "choices": [
    {
      "message": {
        "role": "assistant",
        "content": null,
        "tool_calls": [
          {
            "id": "call_abc123",
            "type": "function",
            "function": {
              "name": "get_order_status",
              "arguments": "{\"order_id\": \"123\"}"
            }
          }
        ]
      },
      "finish_reason": "tool_calls"
    }
  ]
}

Sending tool results back

After executing the function, append a tool-role message with the result. The tool_call_id must match the id from the original tool call.

curl https://api.abliteration.ai/v1/chat/completions \
  -H "Authorization: Bearer $ABLIT_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "abliterated-model",
    "messages": [
      { "role": "user", "content": "Look up the status of order 123." },
      {
        "role": "assistant",
        "content": null,
        "tool_calls": [{
          "id": "call_abc123",
          "type": "function",
          "function": {
            "name": "get_order_status",
            "arguments": "{\"order_id\": \"123\"}"
          }
        }]
      },
      {
        "role": "tool",
        "tool_call_id": "call_abc123",
        "content": "{\"status\": \"shipped\", \"tracking\": \"1Z999AA10\"}"
      }
    ]
  }'

Multiple tools

Define multiple tools in one request. The model may call one, several, or none. Execute each and return all results before the next call.

Multiple tools example

{
  "model": "abliterated-model",
  "messages": [
    { "role": "user", "content": "What's the weather in NYC and order status for 456?" }
  ],
  "tools": [
    {
      "type": "function",
      "function": {
        "name": "get_weather",
        "description": "Get current weather for a location.",
        "parameters": {
          "type": "object",
          "properties": {
            "location": { "type": "string" }
          },
          "required": ["location"]
        }
      }
    },
    {
      "type": "function",
      "function": {
        "name": "get_order_status",
        "description": "Lookup an order status by id.",
        "parameters": {
          "type": "object",
          "properties": {
            "order_id": { "type": "string" }
          },
          "required": ["order_id"]
        }
      }
    }
  ]
}

Forcing a specific tool call

Use tool_choice to control invocation: "auto" (default), "none", or force a specific function.

tool_choice example

{
  "model": "abliterated-model",
  "messages": [
    { "role": "user", "content": "Tell me about order 789." }
  ],
  "tools": [ ... ],
  "tool_choice": {
    "type": "function",
    "function": { "name": "get_order_status" }
  }
}

Best practices

Guidelines for reliable tool calling.

Write clear, specific description fields so the model knows when to call each function.
Use required in your JSON Schema to mark mandatory parameters.
Always validate and parse arguments as JSON before executing the function — the model may produce malformed output in edge cases.
Return structured JSON in tool results so the model can interpret them consistently.
Keep tool result payloads concise. Large payloads consume tokens and may degrade response quality.
Use tool_choice: "none" when you want the model to respond without calling any tools.
Handle the case where tool_calls is empty or absent — the model may answer directly without invoking a tool.

Common errors & fixes

400 Bad Request: Verify that each tool has type: "function" and a valid JSON Schema in parameters.
400 Invalid tool_call_id: The tool_call_id in the tool-role message must match the id returned in the assistant's tool_calls.
401 Unauthorized: Check that your API key is set and sent as a Bearer token.
429 Rate limit: Back off and retry. Use the Retry-After header for pacing.