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Model: Nanthasit/sakthai-context-7b-merged Source: Original Platform
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eval/workbench-7b-endpoint-test.py
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218
eval/workbench-7b-endpoint-test.py
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#!/usr/bin/env python3
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"""Workbench test: 7B merged model via Inference Endpoint."""
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import json, time, os, sys
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import requests
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MODEL = "Nanthasit/sakthai-context-7b-merged"
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ENDPOINT_URL = None # Set dynamically after deployment
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# Read endpoint URL from args or env
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if len(sys.argv) > 1:
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ENDPOINT_URL = sys.argv[1]
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elif "ENDPOINT_URL" in os.environ:
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ENDPOINT_URL = os.environ["ENDPOINT_URL"]
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else:
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print("Usage: python3 sakthai-7b-workbench-test.py <endpoint_url>")
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print("Or set ENDPOINT_URL env var")
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sys.exit(1)
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TOKEN_PATH = "/opt/data/profiles/sakthai/home/.cache/huggingface/token"
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with open(TOKEN_PATH) as f:
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HF_TOKEN = f.read().strip()
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HEADERS = {
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"Authorization": f"Bearer {HF_TOKEN}",
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"Content-Type": "application/json"
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}
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tests = [
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{
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"name": "basic_greeting",
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"desc": "Say hello in one sentence",
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"messages": [
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{"role": "system", "content": "You are SakThai, a helpful assistant. Be concise."},
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{"role": "user", "content": "Say hello in one sentence."}
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],
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"checks": ["non_empty", "substantial"]
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},
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{
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"name": "tool_call_intent",
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"desc": "Tool-use intent",
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"messages": [
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{"role": "system", "content": "You are SakThai with tools: search(query), read_file(path), run_command(command)."},
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{"role": "user", "content": "Search for the latest AI news"}
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],
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"checks": ["non_empty", "substantial"]
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},
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{
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"name": "name_recall",
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"desc": "Remember name across 3 turns",
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"messages": [
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{"role": "system", "content": "You are SakThai."},
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{"role": "user", "content": "My name is Beer."},
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{"role": "assistant", "content": "Nice to meet you, Beer!"},
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{"role": "user", "content": "What's my name?"}
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],
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"checks": ["non_empty", "name_recall"]
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},
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{
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"name": "factual_qa",
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"desc": "Simple factual question",
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"messages": [
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{"role": "system", "content": "You are SakThai. Be concise."},
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{"role": "user", "content": "What is the capital of Japan?"}
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],
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"checks": ["non_empty", "correct"]
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},
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{
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"name": "json_output",
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"desc": "Structured JSON",
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"messages": [
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{"role": "system", "content": "You are SakThai. Only respond with valid JSON."},
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{"role": "user", "content": 'List 3 ML frameworks: {"frameworks": ["a","b","c"]}'}
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],
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"checks": ["non_empty", "valid_json"]
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},
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{
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"name": "instruction_following",
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"desc": "Follow formatting instruction",
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"messages": [
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{"role": "system", "content": "You are SakThai. Exactly one sentence."},
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{"role": "user", "content": "Explain what a transformer is."}
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],
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"checks": ["non_empty", "substantial"]
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},
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{
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"name": "multi_step_reasoning",
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"desc": "Multi-step reasoning",
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"messages": [
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{"role": "system", "content": "You are SakThai, a helpful assistant."},
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{"role": "user", "content": "If you have 3 apples and give away 1, then buy 5 more, how many do you have? Show your work."}
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],
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"checks": ["non_empty", "substantial"]
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},
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{
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"name": "context_window",
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"desc": "Longer context understanding",
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"messages": [
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{"role": "system", "content": "You are SakThai. Be concise."},
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{"role": "user", "content": "The transformer architecture introduced in 'Attention Is All You Need' revolutionized NLP by replacing recurrent layers with multi-head self-attention. It uses positional encodings, layer normalization, and feed-forward networks in an encoder-decoder structure. BERT, GPT, and T5 all build on this foundation. What year was the original transformer paper published?"}
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],
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"checks": ["non_empty", "correct_answer"]
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}
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]
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print(f"🧪 WORKBENCH TEST — SakThai Context 7B")
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print(f" Endpoint: {ENDPOINT_URL}")
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print(f" Model: {MODEL}")
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print(f" Time: {time.strftime('%Y-%m-%dT%H:%M:%SZ', time.gmtime())}")
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print()
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results = []
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for i, test in enumerate(tests):
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print(f"{'─'*60}")
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print(f"TEST {i+1}: {test['name']} — {test['desc']}")
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print(f" Turns: {len(test['messages'])}", flush=True)
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try:
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t0 = time.time()
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resp = requests.post(
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f"{ENDPOINT_URL}/v1/chat/completions",
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headers=HEADERS,
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json={
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"model": "tgi",
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"messages": test["messages"],
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"max_tokens": 256,
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"temperature": 0.1,
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},
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timeout=120
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)
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elapsed = time.time() - t0
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if resp.status_code != 200:
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raise Exception(f"HTTP {resp.status_code}: {resp.text[:200]}")
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data = resp.json()
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choice = data["choices"][0]
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content = choice["message"]["content"].strip()
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finish = choice.get("finish_reason", "")
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usage = data.get("usage", {})
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# Run quality checks
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checks = []
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if len(content) > 0:
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checks.append("non_empty")
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if len(content) > 10:
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checks.append("substantial")
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if "beer" in content.lower() and test["name"] == "name_recall":
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checks.append("name_recall")
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if "tokyo" in content.lower() and test["name"] == "factual_qa":
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checks.append("correct")
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if "2017" in content and test["name"] == "context_window":
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checks.append("correct_answer")
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if test["name"] == "json_output":
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try:
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json.loads(content)
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checks.append("valid_json")
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except:
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pass
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result = {
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"name": test["name"],
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"passed": len(checks) > 0,
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"response_preview": content[:200],
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"response_length": len(content),
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"latency_seconds": round(elapsed, 2),
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"prompt_tokens": usage.get("prompt_tokens"),
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"completion_tokens": usage.get("completion_tokens"),
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"finish_reason": finish,
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"checks": checks
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}
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print(f" {'✅' if result['passed'] else '❌'} Response: {content[:150]}")
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print(f" ⏱ {elapsed:.2f}s | ✅ {checks} | 🔚 {finish}")
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if result.get("prompt_tokens"):
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print(f" 📝 {result['prompt_tokens']}→{result['completion_tokens']}")
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except Exception as e:
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result = {
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"name": test["name"],
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"passed": False,
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"error": str(e)[:300]
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}
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print(f" ❌ FAIL: {e}")
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results.append(result)
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sys.stdout.flush()
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# Summary
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print(f"\n{'='*60}")
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passed = sum(1 for r in results if r.get("passed"))
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total = len(results)
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print(f"📊 WORKBENCH SUMMARY — 7B ({MODEL})")
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print(f"\nResults: {passed}/{total} passed")
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print()
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for r in results:
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status = "✅" if r.get("passed") else "❌"
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name = r["name"].ljust(22)
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lat = f"{r.get('latency_seconds', 0):.1f}s" if r.get("passed") else " - "
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detail = str(r.get("checks", r.get("error", "?")[:60]))
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print(f" {status} {name} ⏱ {lat} {detail}")
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# Save record
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record = {
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"test_run": f"workbench-{time.strftime('%Y-%m-%dT%H:%M:%SZ', time.gmtime())}",
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"model": MODEL,
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"endpoint_url": ENDPOINT_URL,
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"timestamp": time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()),
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"results": results,
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"summary": f"{passed}/{total} passed"
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}
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output_path = "/opt/data/sakthai-7b-workbench-test-record.json"
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with open(output_path, "w") as f:
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json.dump(record, f, indent=2)
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print(f"\n💾 Saved: {output_path}")
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print("🏁 Done.")
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