GLM-5.3 is live. Run Z.ai's latest model on Friendli Model APIs. Try it today ➜
GestaltLabs
Available on FriendliAI
Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Model Details
Model Provider
GestaltLabs
Model Tree
GestaltLabs/Ornstein-3.5-9B-V1.5
Input Modalities
Output Modalities
Supported Functionality
Evaluated on the Gestalt Benchmark Suite (GBS, STANDARD-200) — a held-out, contamination-controlled reasoning + coding suite — paired on identical items with greedy decoding. The Qwen3.5-9B-Base and Ornstein V1.5 columns are the published references.
| Qwen3.5-9B-Base | Ornstein V1.5 | Ornstein V2 | |
|---|---|---|---|
| Overall | 0.725 | 0.850 | 0.825 |
| Reasoning | 0.68 | 0.90 | 1.00 |
| GPQA (graduate-level science) | 0.36 | 0.80 | 1.00 |
| Coding | 0.77 | 0.80 | 0.65 |
On this pod's fresh, same-seed GBS-200 run, V2 matched V1.5 overall (0.825 vs 0.825) while lifting reasoning to 1.00 and GPQA to 1.00 — both at the suite ceiling and far above the Qwen3.5-9B-Base (0.68 reasoning, 0.36 GPQA). The RL run traded some coding (0.80 → 0.65); a coding-focused variant is planned as a follow-up.
mmproj): GestaltLabs/Ornstein-3.5-9B-V2-GGUF--spec-type draft-mtp): ornstein-v2-bf16.ggufI'm a PhD student in visual neuroscience at the University of Toronto who also happens to spend way too much time fine-tuning, merging, and quantizing open-weight models on rented H100s and a local DGX Spark. All training compute is self-funded — balancing GPU costs against a student budget. If my uploads have been useful to you, consider buying a PhD student a coffee. It goes a long way toward keeping these experiments running.
python
from transformers import AutoModelForCausalLM, AutoTokenizerimport torchmodel_id = "GestaltLabs/Ornstein-3.5-9B-V2"tok = AutoTokenizer.from_pretrained(model_id)model = AutoModelForCausalLM.from_pretrained(model_id, dtype=torch.bfloat16, device_map="auto")messages = [{"role": "user", "content": "Derive the variance of a sum of two correlated random variables."}]inputs = tok.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)out = model.generate(inputs, max_new_tokens=1024)print(tok.decode(out[0][inputs.shape[-1]:], skip_special_tokens=True))
Reasoning-heavy tasks, AI-research assistance, technical and scientific problem-solving, and general conversation.
Apache 2.0 — inherited from the Qwen 3.5 9B base release.