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CertainLogicAI
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Model Details
Model Provider
CertainLogicAI
Model Tree
Qwen/Qwen3.6-35B-A3B
Input Modalities
Output Modalities
Supported Functionality
| Benchmark | Base Model | +Adapter | Δ |
|---|---|---|---|
| Coder Quick (10-task) | 70.0% | 80.0% | +10.0 pp |
Internal benchmark — pass requires keyword and structural correctness. HumanEval+ results forthcoming.
The adapter was trained across 6 curated data sources (1,685 records, 1 epoch, template-generated for quality consistency):
| Capability | Description |
|---|---|
| Code Generation | Function completion, algorithm implementation, bug fixing, full solutions from signatures |
| Code Evaluation (Judge) | Read code or output, detect errors, classify quality, recommend accept/reject/fix/escalate |
| Tool Calling | Function-call format selection, argument construction, tool routing (BFCL-compatible) |
| Refusal / Safety | Recognize invalid, unsafe, or incomplete requests and refuse appropriately. Knows its limits. |
| Audit & Escalation | Identify when a task needs human review vs automated handling. Structured incident documentation. |
| Scope & Planning | Break down tasks before execution. Evaluate feasibility before committing to a solution. |
These are not pipeline traces or raw agent logs — they are template-generated records validated for structural quality and data cleanliness.
This is a generic recommendation for getting started. You may customize it for your use case.
text
You are an AI assistant specialized in code generation, code evaluation, and task orchestration.Your capabilities:- Generate working code from descriptions, stubs, or function signatures- Evaluate code quality, detect errors, classify outputs- Use function calling and tool selection when appropriate- Refuse requests that are unsafe, invalid, or outside your scope- Escalate tasks to human review when confidence is lowRules:- Return working code with correct edge-case handling- Be honest about what you don't know- If you cannot complete a request safely, explain why- When evaluating, be specific about what is wrong and how to fix it
bash
pip install torch transformers peft accelerate
python
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizerfrom peft import PeftModelbase_model_name = "Qwen/Qwen3.6-35B-A3B"adapter_path = "CertainLogic/qwen3.6-35b-a3b-the-judge-code-edition"# Load base modelmodel = AutoModelForCausalLM.from_pretrained(base_model_name,torch_dtype=torch.bfloat16,device_map="auto",)tokenizer = AutoTokenizer.from_pretrained(base_model_name)# Apply adaptermodel = PeftModel.from_pretrained(model, adapter_path)# Generateprompt = "def fibonacci(n):\n \"\"\"Return the nth Fibonacci number.\"\"\""inputs = tokenizer(prompt, return_tensors="pt").to("cuda")outputs = model.generate(**inputs, max_new_tokens=512)print(tokenizer.decode(outputs[0], skip_special_tokens=True))
python
prompt = """Evaluate this code for correctness:def divide(a, b):return a / bDoes this code handle edge cases properly? If not, what is wrong and how would you fix it?"""inputs = tokenizer(prompt, return_tensors="pt").to("cuda")outputs = model.generate(**inputs, max_new_tokens=512)print(tokenizer.decode(outputs[0], skip_special_tokens=True))
| Parameter | Value |
|---|---|
| Base model | Qwen/Qwen3.6-35B-A3B (34.7B params, 8/32 active MoE) |
| Method | LoRA (r=8, alpha=16, dropout=0.05) |
| Target modules | q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj |
| Training records | 1,685 train / 81 validation |
| Data sources | 6 curated template-generated sources |
| Compute | Single H100 (80GB), ~61 minutes |
| Precision | BF16 mixed precision |
| Sequence length | 2048 (unpacked) |
Apache 2.0. The base model (Qwen3.6-35B-A3B) carries its own license terms.
Built by CertainLogic. We build deterministic AI tools for agents that execute real work.
The training dataset is proprietary and not included.
| Epochs |
| 1 |
| Eval loss | 0.5793 |