Qwen3.5-DeltaCoder-9B-GGUF

v1.1-DPO — Now with DPO alignment for improved code correctness and self-verification. If you downloaded before March 28, 2026, please re-pull to get v1.1-DPO.

GGUF quantizations of Qwen3.5-DeltaCoder-9B for use with llama.cpp, Ollama, LM Studio, and other GGUF-compatible inference engines.

What's New in v1.1-DPO

• DPO alignment on 4,519 preference pairs from AceCode-V2-122K
• Self-correcting behavior — model now detects and fixes its own bugs rather than submitting incorrect code
• Improved code correctness — trained to prefer passing solutions over failing ones
• Same tool-call reliability as v1 — SFT improvements preserved through two-stage merge

Available Quantizations

File	Quant	Size	Notes
DeltaCoder-9B-v1.1-DPO-Q2_K.gguf	Q2_K	~3.6 GB	Smallest, lowest quality
DeltaCoder-9B-v1.1-DPO-Q3_K_S.gguf	Q3_K_S	~4.0 GB
DeltaCoder-9B-v1.1-DPO-Q3_K_M.gguf	Q3_K_M	~4.4 GB
DeltaCoder-9B-v1.1-DPO-Q3_K_L.gguf	Q3_K_L	~4.6 GB
DeltaCoder-9B-v1.1-DPO-Q4_0.gguf	Q4_0	~3.2 GB
DeltaCoder-9B-v1.1-DPO-Q4_K_S.gguf	Q4_K_S	~5.0 GB
DeltaCoder-9B-v1.1-DPO-Q4_K_M.gguf	Q4_K_M	~5.5 GB	Recommended
DeltaCoder-9B-v1.1-DPO-Q5_K_S.gguf	Q5_K_S	~6.1 GB
DeltaCoder-9B-v1.1-DPO-Q5_0.gguf	Q5_0	~6.1 GB
DeltaCoder-9B-v1.1-DPO-Q5_K_M.gguf	Q5_K_M	~6.5 GB
DeltaCoder-9B-v1.1-DPO-Q6_K.gguf	Q6_K	~7.3 GB
DeltaCoder-9B-v1.1-DPO-Q8_0.gguf	Q8_0	~9.4 GB	Near-lossless
DeltaCoder-9B-v1.1-DPO-BF16.gguf	BF16	~17.9 GB	Full precision

Recommended Quant

• Low VRAM (8GB): Q4_K_M
• Mid VRAM (12GB): Q5_K_M or Q6_K
• High VRAM (16GB+): Q8_0
• Full precision: BF16

Training Lineage

Qwen/Qwen3.5-9B-Base
 └─ Qwen/Qwen3.5-9B  (instruction tuned)
     └─ Jackrong/Qwen3.5-9B-Claude-4.6-Opus-Reasoning-Distilled-v2
         (SFT on Claude 4.6 Opus reasoning traces)
         └─ danielcherubini/Qwen3.5-DeltaCoder-9B  (v1 SFT — tool-call reliability)
             (LoRA SFT on CoderForge-Preview)
             └─ danielcherubini/Qwen3.5-DeltaCoder-9B v1.1-DPO  ← this model
                 (DPO on AceCode-V2-122K preference pairs)

Recommended Sampling Settings

Parameter	Value
temperature	0.6
top_k	20
top_p	0.95
min_p	0.0
presence_penalty	0.0
repeat_penalty	1.0

Do not use temperature below 0.5 — low temperatures cause deterministic looping in multi-turn agentic use.

KV Cache Quantization

Context Length	KV Cache	VRAM (Q4_K_M)	Generation Speed
102,400	f16/q4_0	~8.5 GB	~111 tok/s
131,072	f16/q4_0	~9.1 GB	~110 tok/s

# llama.cpp / ik_llama.cpp flags
-ctk f16 -ctv q4_0

Usage

Ollama

ollama create deltacoder -f Modelfile

Example Modelfile:

FROM ./DeltaCoder-9B-v1.1-DPO-Q5_K_M.gguf

llama.cpp

./llama-server -m DeltaCoder-9B-v1.1-DPO-Q5_K_M.gguf -ngl 999 -c 131072 -ctk f16 -ctv q4_0 -fa 1 --jinja

LM Studio

Download any GGUF file and load it directly in LM Studio.

Benchmarks

Model	HumanEval	HumanEval+	Terminal-Bench Easy
Jackrong Qwen3.5-9B-v2 (base)	53.7%	—	—
DeltaCoder-9B v1 (temp=0.6)	50.6%	49.4%	2/4 (50%)
DeltaCoder-9B v1.1-DPO (temp=0.6)	TBD	TBD	2/4 (50%)*

*v1.1-DPO timed out on 2 tasks that v1 answered incorrectly — behavioral improvement confirmed, running with extended timeout.

Acknowledgements

• Unsloth for Qwen3.5 training support
• Together AI for the CoderForge dataset
• TIGER Lab for AceCode-V2-122K
• Jackrong for the reasoning distillation
• Qwen for the base model