Instructions to use malvavisc0/OxCoder-9B-gptq-int4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use malvavisc0/OxCoder-9B-gptq-int4 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="malvavisc0/OxCoder-9B-gptq-int4")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("malvavisc0/OxCoder-9B-gptq-int4")
model = AutoModelForMultimodalLM.from_pretrained("malvavisc0/OxCoder-9B-gptq-int4", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use malvavisc0/OxCoder-9B-gptq-int4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "malvavisc0/OxCoder-9B-gptq-int4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "malvavisc0/OxCoder-9B-gptq-int4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/malvavisc0/OxCoder-9B-gptq-int4

SGLang

How to use malvavisc0/OxCoder-9B-gptq-int4 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "malvavisc0/OxCoder-9B-gptq-int4" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "malvavisc0/OxCoder-9B-gptq-int4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "malvavisc0/OxCoder-9B-gptq-int4" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "malvavisc0/OxCoder-9B-gptq-int4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use malvavisc0/OxCoder-9B-gptq-int4 with Docker Model Runner:
```
docker model run hf.co/malvavisc0/OxCoder-9B-gptq-int4
```

OxCoder-9B — GPTQ Int4

Agentic coding, distilled to 9B, compressed to 8.5 GB.

OrionLLM/OxCoder-9B is a lightweight coding model for long-horizon agentic tasks — distilled from Fable-5.1 and GLM-5.3 trajectories across Claude Code, OpenCode and Codex — with 262K native context, SWE-bench Verified 73.5, Terminal-Bench 2.1 (Claude Code) 50.8. This artifact is a faithful GPTQ Int4 quantization: the full text stack at group size 128, desc_act ordering, calibrated on real code-reasoning and agentic tool-use traces — not generic web text.

Why this artifact

Runs where the BF16 original can't. 17.6 GB BF16 → 8.5 GB Int4 fits a 12 GB GPU with room for KV cache. Same weights, same behavior, a third of the memory.
Proven recipe, third shipped artifact on this hybrid qwen3_5 architecture (GatedDeltaNet + attention): 200/200 text modules quantized, mean per-layer quant error 2.3e-5 (worst 1.1e-4) — better than the 4.2e-5 baseline of the first artifact in the series. Vision tower skipped by design (text-only calibration).
Calibrated on serving traffic. 256 rows: 128 OpenCodeReasoning CoT traces + 128 Nemotron-Agentic interactive tool-use episodes. The model sees, during calibration, the same kind of prompts it sees in production — code reasoning with think blocks, multi-step tool calls.
Ships with the froggeric v22.5 chat template (froggeric/Qwen-Fixed-Chat-Templates): XML tool calls, preserved think blocks, KV-cache-safe history, no empty-think poisoning. Calibrated and served through the same template, so serving matches calibration exactly.

Serving (vLLM)

vllm serve malvavisc0/OxCoder-9B-gptq-int4 \
  --quantization gptq_marlin \
  --max-num-seqs 10 \
  --chat-template chat_template.jinja \
  --enable-auto-tool-choice \
  --tool-call-parser qwen3_xml

Notes:

--max-num-seqs 10 — the hybrid GDN/Mamba cache corrupts state at high concurrency; 10 is the proven setting on this architecture.
Pass --chat-template chat_template.jinja explicitly — vLLM does not auto-load the sibling file from the model dir in current releases.
Sampling: temperature=0.6, top_p=0.95, top_k=20 (thinking mode). Greedy decoding causes repetition loops on this model class.

Hardware guidance


Min GPU memory	12 GB (Int4 weights + KV headroom)
Verified	RTX A5000 16 GB — 5/5 behavioral probes; 45 tok/s decode (short coding), 40 tok/s at 31k context
Kernels	`gptq_marlin` — any Marlin-capable CUDA GPU (sm_80+)
Context	262K native; clamp `--max-model-len` on <16 GB cards

Quantization details


Format	GPTQ Int4, group size 128, `desc_act`
Calibration	256 rows (128 OpenCodeReasoning + 128 Nemotron-Agentic), seq len 4096
Chat template	froggeric v22.5 (calibration + shipped)
Coverage	200/200 text modules; vision tower skipped (`--allow-partial-coverage`)
Provenance	`aft_provenance.json` (pinned source revision, seeds, config)
Per-layer log	`quant_log.csv`

Toolchain: quantized with aft (the Aria Finetuner pipeline) driving gptqmodel 7.3.4 / torch 2.13.0+cu130 / transformers 5.15.0 on an NVIDIA GB10 (DGX Spark, aarch64, 128 GB unified). Quant error: mean 2.3e-5, worst 1.1e-4 (200 text modules).

Verified on consumer hardware: RTX A5000 16 GB (sm_86, plain CUDA — no GB10 dependency) with vLLM 0.28.0: 5/5 behavioral probes (math with closed think blocks, exec-verified Kadane's incl. the all-negative case, parsed XML tool call), 45 tok/s decode on a short coding probe, 40 tok/s at 31k context, 0.06 s TTFT.

Downloads last month: 52

Safetensors

Model size

9B params

Tensor type

BF16

I32

Model tree for malvavisc0/OxCoder-9B-gptq-int4

Base model

Qwen/Qwen3.5-9B-Base

Finetuned

Qwen/Qwen3.5-9B

Finetuned

OrionLLM/OxCoder-9B

Quantized

(8)

this model