likhonsheikh/anthropic-compatible-api

# Set environment variables
export ANTHROPIC_API_KEY="any-key"
export ANTHROPIC_BASE_URL="https://likhonsheikh-anthropic-compatible-api.hf.space/anthropic"

# Run Claude Code
claude "Write a Python script that reads a CSV file"

# Or with explicit model
claude --model qwen2.5-coder-7b "Explain this code"

# Anthropic-Compatible API Configuration
export ANTHROPIC_API_KEY="any-key"
export ANTHROPIC_BASE_URL="https://likhonsheikh-anthropic-compatible-api.hf.space/anthropic"

import anthropic

client = anthropic.Anthropic(
    api_key="any-key",
    base_url="https://likhonsheikh-anthropic-compatible-api.hf.space/anthropic"
)

# Basic message
message = client.messages.create(
    model="qwen2.5-coder-7b",
    max_tokens=1024,
    messages=[{"role": "user", "content": "Hello! Write a hello world in Python."}]
)
print(message.content[0].text)

# With system prompt
message = client.messages.create(
    model="qwen2.5-coder-7b",
    max_tokens=1024,
    system="You are a helpful coding assistant. Always include comments in your code.",
    messages=[{"role": "user", "content": "Write a function to calculate factorial"}]
)
print(message.content[0].text)

import anthropic

client = anthropic.Anthropic(
    api_key="any-key",
    base_url="https://likhonsheikh-anthropic-compatible-api.hf.space/anthropic"
)

with client.messages.stream(
    model="qwen2.5-coder-7b",
    max_tokens=1024,
    messages=[{"role": "user", "content": "Write a detailed explanation of recursion"}]
) as stream:
    for text in stream.text_stream:
        print(text, end="", flush=True)

import anthropic
import json

client = anthropic.Anthropic(
    api_key="any-key",
    base_url="https://likhonsheikh-anthropic-compatible-api.hf.space/anthropic"
)

tools = [
    {
        "name": "get_weather",
        "description": "Get the current weather for a location",
        "input_schema": {
            "type": "object",
            "properties": {
                "location": {"type": "string", "description": "City name"},
                "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}
            },
            "required": ["location"]
        }
    }
]

message = client.messages.create(
    model="qwen2.5-coder-7b",
    max_tokens=1024,
    tools=tools,
    messages=[{"role": "user", "content": "What's the weather in Tokyo?"}]
)

if message.stop_reason == "tool_use":
    for block in message.content:
        if block.type == "tool_use":
            print(f"Tool: {block.name}")
            print(f"Input: {json.dumps(block.input, indent=2)}")

import anthropic

client = anthropic.Anthropic(
    api_key="any-key",
    base_url="https://likhonsheikh-anthropic-compatible-api.hf.space/anthropic"
)

message = client.messages.create(
    model="qwen2.5-coder-7b",
    max_tokens=2048,
    thinking={"type": "enabled", "budget_tokens": 1024},
    messages=[{"role": "user", "content": "Solve step by step: What is 15% of 240?"}]
)

for block in message.content:
    if block.type == "thinking":
        print("=== THINKING ===")
        print(block.thinking)
    elif block.type == "text":
        print("=== ANSWER ===")
        print(block.text)

import Anthropic from '@anthropic-ai/sdk';

const client = new Anthropic({
  apiKey: 'any-key',
  baseURL: 'https://likhonsheikh-anthropic-compatible-api.hf.space/anthropic'
});

const message = await client.messages.create({
  model: 'qwen2.5-coder-7b',
  max_tokens: 1024,
  messages: [{ role: 'user', content: 'Hello!' }]
});

console.log(message.content[0].text);

curl -X POST "https://likhonsheikh-anthropic-compatible-api.hf.space/anthropic/v1/messages" \
  -H "Content-Type: application/json" \
  -H "x-api-key: any-key" \
  -H "anthropic-version: 2023-06-01" \
  -d '{
    "model": "qwen2.5-coder-7b",
    "max_tokens": 256,
    "messages": [{"role": "user", "content": "Hello!"}]
  }'

from openai import OpenAI

client = OpenAI(
    api_key="any-key",
    base_url="https://likhonsheikh-anthropic-compatible-api.hf.space/v1"
)

response = client.chat.completions.create(
    model="qwen2.5-coder-7b",
    messages=[{"role": "user", "content": "Hello!"}],
    max_tokens=1024
)
print(response.choices[0].message.content)

{
  "model": "qwen2.5-coder-7b",
  "max_tokens": 1024,
  "messages": [{"role": "user", "content": "Hello!"}],
  "system": "You are a helpful assistant.",
  "temperature": 0.7,
  "stream": false,
  "tools": [...],
  "thinking": {"type": "enabled", "budget_tokens": 1024}
}

{
  "id": "msg_abc123",
  "type": "message",
  "role": "assistant",
  "content": [{"type": "text", "text": "Hello!"}],
  "model": "qwen2.5-coder-7b",
  "stop_reason": "end_turn",
  "usage": {"input_tokens": 10, "output_tokens": 25}
}