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Model: ksjpswaroop/zindango-slm
Source: Original Platform

scripts/test_zindango_gguf.py

@@ -0,0 +1,94 @@
+#!/usr/bin/env python3
+"""
+Test zindango-slm: GGUF (llama-cpp-python) or HF (transformers) fallback.
+Runs a single prompt to verify the model loads and generates.
+"""
+import os
+import sys
+
+
+def test_gguf(gguf_path: str) -> bool:
+    """Test via llama-cpp-python if available."""
+    try:
+        from llama_cpp import Llama
+    except ImportError:
+        return False
+
+    print("Loading zindango-slm (GGUF) with llama-cpp-python...")
+    llm = Llama(
+        model_path=gguf_path,
+        n_ctx=512,
+        n_threads=os.cpu_count() or 4,
+        chat_format="chatml",
+        verbose=False,
+    )
+    messages = [
+        {"role": "system", "content": "You are a helpful assistant. Reply briefly."},
+        {"role": "user", "content": "Who are you? One sentence only."},
+    ]
+    out = llm.create_chat_completion(messages=messages, max_tokens=64, temperature=0.7)
+    reply = out["choices"][0]["message"]["content"]
+    print("Reply:", reply)
+    return bool(reply.strip())
+
+
+def test_transformers(local_path: str | None = None) -> bool:
+    """Test via transformers (HF model) as fallback when GGUF/llama.cpp unavailable."""
+    try:
+        from transformers import AutoModelForCausalLM, AutoTokenizer
+    except ImportError:
+        print("transformers not installed: pip install transformers torch")
+        return False
+
+    model_id = local_path if local_path and os.path.isdir(local_path) else "ksjpswaroop/zindango-slm"
+    print(f"Testing zindango-slm (transformers) - fallback when llama-cpp unavailable...")
+    tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
+    model = AutoModelForCausalLM.from_pretrained(
+        model_id,
+        trust_remote_code=True,
+        torch_dtype="auto",
+        low_cpu_mem_usage=True,
+    )
+    messages = [{"role": "user", "content": "Who are you? One sentence only."}]
+    text = tokenizer.apply_chat_template(
+        messages, tokenize=False, add_generation_prompt=True
+    )
+    inputs = tokenizer(text, return_tensors="pt")
+    out = model.generate(
+        **inputs, max_new_tokens=64, pad_token_id=tokenizer.pad_token_id or tokenizer.eos_token_id
+    )
+    reply = tokenizer.decode(
+        out[0][inputs["input_ids"].shape[1] :], skip_special_tokens=True
+    )
+    print("Reply:", reply)
+    return bool(reply.strip())
+
+
+def main():
+    script_dir = os.path.dirname(os.path.abspath(__file__))
+    project_root = os.path.dirname(script_dir)
+    model_dir = os.path.join(project_root, "models", "zindango-slm")
+
+    # Prefer Q8_0, then f16
+    for name in ("zindango-slm-Q8_0.gguf", "zindango-slm-f16.gguf"):
+        gguf_path = os.path.join(model_dir, name)
+        if os.path.isfile(gguf_path):
+            print(f"Trying GGUF: {gguf_path}")
+            if test_gguf(gguf_path):
+                print("\n[OK] zindango-slm GGUF test passed.")
+                return 0
+            break
+
+    print("\nllama-cpp-python unavailable or failed. Using transformers fallback...")
+    local_hf = os.path.join(project_root, "models", "zindango-slm-hf")
+    if test_transformers(local_hf):
+        print("\n[OK] zindango-slm transformers test passed.")
+        return 0
+
+    print("\n[FAIL] No working backend. Install: pip install transformers torch")
+    print("For GGUF: pip install llama-cpp-python")
+    return 1
+
+
+if __name__ == "__main__":
+    sys.exit(main())