GLM-5.3 is live. Run Z.ai's latest model on Friendli Model APIs. Try it today ➜
cyberalien
Available on FriendliAI
Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Model Details
Model Provider
cyberalien
Model Tree
Qwen/Qwen2.5-0.5B-Instruct
Input Modalities
Output Modalities
Supported Functionality
7ae557604adf67be50417f59c2c2f167def9a775.mesh_query_config.json.In CyMesh MeshLibrary → AI Models & Installation, choose this model's Download & Install. Choose Use for Semantic Search, then enable Library Settings → Asset Library → Search → Enable Semantic Search. The feature is optional and limited to MeshLibrary. Downloads are stored outside the plugin.
Existing integrations can use the downloaded folder as Library Query Model.
The contract remains cymesh-query-v1; mesh_query_prompt.txt must match its recorded SHA-256.
CPU and CUDA are supported by the host runtime. The default installer uses CPU PyTorch;
CUDA requires a compatible PyTorch runtime configured by the user.
python
import jsonimport torchfrom huggingface_hub import snapshot_downloadfrom pathlib import Pathfrom transformers import AutoModelForCausalLM, AutoTokenizer folder = snapshot_download("cyberalien/CySearchAssist-Mesh-0.5B") # Pin revision to a reviewed commit in production.tokenizer = AutoTokenizer.from_pretrained(folder, local_files_only=True, trust_remote_code=False)device = "cuda" if torch.cuda.is_available() else "cpu"model = AutoModelForCausalLM.from_pretrained( folder, torch_dtype=torch.float16 if device == "cuda" else torch.float32, local_files_only=True, trust_remote_code=False,).to(device).eval()messages = [ {"role": "system", "content": (Path(folder)/"mesh_query_prompt.txt").read_text("utf-8")}, {"role": "user", "content": "une chaise en bois sans accoudoirs"},]prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)inputs = tokenizer(prompt, return_tensors="pt").to(device)with torch.inference_mode(): output = model.generate(**inputs, max_new_tokens=256, do_sample=False, pad_token_id=tokenizer.eos_token_id)text = tokenizer.decode(output[0, inputs["input_ids"].shape[1]:], skip_special_tokens=True)print(text) # Parse AND validate; do not treat generated values as asset paths or commands.Expected keys: mesh_prompt, variations (exactly two), suggested_categories, suggested_tags, exclude.
Invalid output should fall back explicitly to the user's original text. Do not execute model output.
Local CPU benchmark: Ryzen 9 7900X, eight PyTorch threads, FP16 files loaded for FP32 CPU computation. Small technical test; target expectations came from asset names, not human relevance annotations. It does not establish general retrieval accuracy across arbitrary libraries.
| Measurement | 1.5B | 0.5B |
|---|---|---|
| Valid JSON contract, 37 queries | 35/37 | 30/37 |
| Named target in top five, 10 metadata queries | 10/10 | 7/10 |
| Median CPU generation with loaded model | 15.26 s | 5.23 s |
| Peak process RAM including load | 8.96 GiB | 3.09 GiB |
Negations, synonyms, multilingual ambiguity and materials can be misinterpreted. No guarantee of equivalent quality between the two sizes. These exports are not GGUF or 4-bit quantized. The initial CyMesh worker reloads the model per search, adding loading latency. The host should preserve manual categories and only return real paths from its own asset index.
import jsonimport torchfrom huggingface_hub import snapshot_downloadfrom pathlib import Pathfrom transformers import AutoModelForCausalLM, AutoTokenizer folder = snapshot_download("cyberalien/CySearchAssist-Mesh-0.5B") # Pin revision to a reviewed commit in production.tokenizer = AutoTokenizer.from_pretrained(folder, local_files_only=True, trust_remote_code=False)device = "cuda" if torch.cuda.is_available() else "cpu"model = AutoModelForCausalLM.from_pretrained( folder, torch_dtype=torch.float16 if device == "cuda" else torch.float32, local_files_only=True, trust_remote_code=False,).to(device).eval()messages = [ {"role": "system", "content": (Path(folder)/"mesh_query_prompt.txt").read_text("utf-8")}, {"role": "user", "content": "une chaise en bois sans accoudoirs"},]prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)inputs = tokenizer(prompt, return_tensors="pt").to(device)with torch.inference_mode(): output = model.generate(**inputs, max_new_tokens=256, do_sample=False, pad_token_id=tokenizer.eos_token_id)text = tokenizer.decode(output[0, inputs["input_ids"].shape[1]:], skip_special_tokens=True)print(text) # Parse AND validate; do not treat generated values as asset paths or commands.