FRIDAY-35B

A reasoning-enhanced 35B parameter Mixture-of-Experts model fine-tuned for senior software engineering. Built on Qwen/Qwen3.6-35B-A3B (256 experts, 8 active per token, ~3B active parameters per forward pass).

FRIDAY reasons at a staff+ engineer level — architectural thinking, tradeoff analysis, and code review with root-cause depth.

What FRIDAY Does

• Code review: Identifies concurrency bugs, data consistency issues, and architectural anti-patterns
• System design: Diagnosis → root causes → short-term/long-term solutions
• Architectural reasoning: Evaluates tradeoffs rather than prescribing a single answer
• Multi-language: Rust, Python, TypeScript, C++, Go, Java

Eval

Buggy async Python checkout service with 10 planted bugs:

	FRIDAY-35B	Competitor (API)
Bugs found	10/10	7/10
Time	19.5s	53.2s
Tokens out	3,156	4,226
Throughput	~162 tok/s	~79 tok/s

FRIDAY found all 10 bugs across both runs. The competitor missed 3: lock TTL expiration during slow payments, null product row dereference, and Redis type mismatch on lpush. FRIDAY also flagged the Redis distributed lock as architecturally redundant given proper DB-level locking.

Training

Base model	Qwen/Qwen3.6-35B-A3B
Architecture	MoE — 256 experts, 8 active/token, GDN hybrid attention
Method	Full fine-tune SFT
Training data	2,472 reasoning traces
Sequence length	8,192 tokens
Epochs	3
Learning rate	2e-5, cosine schedule
Precision	BF16 + TF32
Framework	TRL SFTTrainer + DeepSpeed ZeRO-3
Hardware	8× A100 80GB

Usage

With SGLang

python -m sglang.launch_server \
  --model dangell7/Friday-35B \
  --dtype bfloat16 \
  --tp 8 \
  --trust-remote-code

With Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "dangell7/Friday-35B",
    torch_dtype="bfloat16",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("dangell7/Friday-35B")

Limitations

• Autoregressive LLM; may hallucinate technical details
• MoE architecture requires significant VRAM (~8× A100 or equivalent)
• Not a substitute for human code review in production systems

Acknowledgements

• Qwen team for Qwen3.6-35B-A3B
• SGLang for high-performance MoE serving
• TRL and DeepSpeed for training infrastructure

Citation

@misc{Friday_35B,
  title        = {FRIDAY-35B},
  author       = {dangell7},
  year         = {2026},
  publisher    = {Hugging Face},
  howpublished = {\url{https://huggingface.co/dangell7/Friday-35B}}
}