Text Generation
Transformers
Safetensors
qwen3_5_moe
image-text-to-text
qwen3.6
nex
gptq
gptq-pro
foem
Mixture of Experts
marlin
vllm
int4
quantized
long-context
tool-use
function-calling
terminal-bench
non-mtp
multimodal-tensors-preserved
conversational
4-bit precision
Instructions to use XReyRobert/Nex-N2-mini-GPTQ-Pro with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use XReyRobert/Nex-N2-mini-GPTQ-Pro with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="XReyRobert/Nex-N2-mini-GPTQ-Pro") 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("XReyRobert/Nex-N2-mini-GPTQ-Pro") model = AutoModelForMultimodalLM.from_pretrained("XReyRobert/Nex-N2-mini-GPTQ-Pro", 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 XReyRobert/Nex-N2-mini-GPTQ-Pro with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "XReyRobert/Nex-N2-mini-GPTQ-Pro" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "XReyRobert/Nex-N2-mini-GPTQ-Pro", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/XReyRobert/Nex-N2-mini-GPTQ-Pro
- SGLang
How to use XReyRobert/Nex-N2-mini-GPTQ-Pro 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 "XReyRobert/Nex-N2-mini-GPTQ-Pro" \ --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": "XReyRobert/Nex-N2-mini-GPTQ-Pro", "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 "XReyRobert/Nex-N2-mini-GPTQ-Pro" \ --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": "XReyRobert/Nex-N2-mini-GPTQ-Pro", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use XReyRobert/Nex-N2-mini-GPTQ-Pro with Docker Model Runner:
docker model run hf.co/XReyRobert/Nex-N2-mini-GPTQ-Pro
File size: 1,435 Bytes
a7e8115 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 | # Terminal-Bench 2.0 Smoke24 Task List
This file documents the fixed 24-task Smoke24 corpus used for the model-card
summary results. It intentionally lists the task names and harness shape only;
it does not publish per-task outcomes, model-by-model traces, or aggregate CSV
details.
## Corpus
- Benchmark: Terminal-Bench 2.0
- Corpus ID: `tb20-coder-smoke24-fast-success-failure-20260616`
- Created: 2026-06-16
- Size: 24 tasks
- Selection policy: 12 shortest prior successes and 12 shortest prior failures
from a recovery-corrected Qwopus3.6-27B-v2-GPTQ-Pro-v1 aggregate.
## Harness Shape
- Agent: `terminus-2`
- Concurrency: `1`
- Sandbox: 32 CPU / 48 GiB RAM
- Task timeout: 30 minutes
- Max output: 40k tokens
- Thinking token budget: 32768
- Sampling: temperature `1.0`, top-p `0.95`, top-k `20`
## Tasks
### Prior Success Group
- `git-leak-recovery`
- `prove-plus-comm`
- `fix-git`
- `modernize-scientific-stack`
- `kv-store-grpc`
- `openssl-selfsigned-cert`
- `headless-terminal`
- `multi-source-data-merger`
- `fix-code-vulnerability`
- `nginx-request-logging`
- `build-pmars`
- `code-from-image`
### Prior Failure Group
- `feal-differential-cryptanalysis`
- `raman-fitting`
- `log-summary-date-ranges`
- `video-processing`
- `sqlite-with-gcov`
- `sanitize-git-repo`
- `torch-pipeline-parallelism`
- `count-dataset-tokens`
- `configure-git-webserver`
- `cancel-async-tasks`
- `mteb-retrieve`
- `hf-model-inference`
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