Instructions to use 0xSero/NousCoder-14B-Tools with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use 0xSero/NousCoder-14B-Tools with Transformers:

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

pipe = pipeline("text-generation", model="0xSero/NousCoder-14B-Tools")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("0xSero/NousCoder-14B-Tools", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use 0xSero/NousCoder-14B-Tools with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "0xSero/NousCoder-14B-Tools"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "0xSero/NousCoder-14B-Tools",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/0xSero/NousCoder-14B-Tools

SGLang

How to use 0xSero/NousCoder-14B-Tools 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 "0xSero/NousCoder-14B-Tools" \
    --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": "0xSero/NousCoder-14B-Tools",
		"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 "0xSero/NousCoder-14B-Tools" \
        --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": "0xSero/NousCoder-14B-Tools",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use 0xSero/NousCoder-14B-Tools with Docker Model Runner:
```
docker model run hf.co/0xSero/NousCoder-14B-Tools
```

NousCoder-14B-Tools / README.md

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	---
	library_name: transformers
	tags:
	- nouscoder
	- tools
	base_model:
	- NousResearch/Hermes-3-Llama-3.1-8B
	license: mit
	pipeline_tag: text-generation
	base_model_relation: finetune
	---

	> [!TIP]
	> [Support this work →](https://donate.sybilsolutions.ai) · [X](https://x.com/0xsero) · [GitHub](https://github.com/0xsero) · [REAP paper](https://arxiv.org/abs/2510.13999) · [Cerebras REAP](https://huggingface.co/collections/cerebras/cerebras-reap)

	# NousCoder-14B-Tools

	Tools fine-tune of [NousResearch/Hermes-3-Llama-3.1-8B](https://huggingface.co/NousResearch/Hermes-3-Llama-3.1-8B).

	## At a glance

	\| \| \|
	\|---\|---\|
	\| Base model \| [NousResearch/Hermes-3-Llama-3.1-8B](https://huggingface.co/NousResearch/Hermes-3-Llama-3.1-8B) \|
	\| Format \| Tools \|
	\| Total params \| 14B \|
	\| Active / token \| — \|
	\| Experts / layer \| — \|
	\| Layers \| — \|
	\| Hidden size \| — \|
	\| Context \| — \|
	\| On-disk size \| 1 GB \|

	## Which variant should I pick?

	\| Variant \| Format \| Link \|
	\|---\|---\|---\|
	\| `NousCoder-14B-SFT` \| SFT \| [link](https://huggingface.co/0xSero/NousCoder-14B-SFT) \|
	\| `NousCoder-14B-SFT-Tools` \| SFT \| [link](https://huggingface.co/0xSero/NousCoder-14B-SFT-Tools) \|
	\| `NousCoder-14B-Tools` (this) \| Tools \| [link](https://huggingface.co/0xSero/NousCoder-14B-Tools) \|

	<!-- Provide a quick summary of what the model is/does. -->



	## Model Details

	### Model Description

	<!-- Provide a longer summary of what this model is. -->

	This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

	- Developed by: [More Information Needed]
	- Funded by [optional]: [More Information Needed]
	- Shared by [optional]: [More Information Needed]
	- Model type: [More Information Needed]
	- Language(s) (NLP): [More Information Needed]
	- License: [More Information Needed]
	- Finetuned from model [optional]: [More Information Needed]

	### Model Sources [optional]

	<!-- Provide the basic links for the model. -->

	- Repository: [More Information Needed]
	- Paper [optional]: [More Information Needed]
	- Demo [optional]: [More Information Needed]

	## Uses

	<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->

	### Direct Use

	<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->

	[More Information Needed]

	### Downstream Use [optional]

	<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->

	[More Information Needed]

	### Out-of-Scope Use

	<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->

	[More Information Needed]

	## Bias, Risks, and Limitations

	<!-- This section is meant to convey both technical and sociotechnical limitations. -->

	[More Information Needed]

	### Recommendations

	<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

	Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

	## How to Get Started with the Model

	Use the code below to get started with the model.

	[More Information Needed]

	## Training Details

	### Training Data

	<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->

	[More Information Needed]

	### Training Procedure

	<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->

	#### Preprocessing [optional]

	[More Information Needed]


	#### Training Hyperparameters

	- Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

	#### Speeds, Sizes, Times [optional]

	<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->

	[More Information Needed]

	## Evaluation

	<!-- This section describes the evaluation protocols and provides the results. -->

	### Testing Data, Factors & Metrics

	#### Testing Data

	<!-- This should link to a Dataset Card if possible. -->

	[More Information Needed]

	#### Factors

	<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->

	[More Information Needed]

	#### Metrics

	<!-- These are the evaluation metrics being used, ideally with a description of why. -->

	[More Information Needed]

	### Results

	[More Information Needed]

	#### Summary



	## Model Examination [optional]

	<!-- Relevant interpretability work for the model goes here -->

	[More Information Needed]

	## Environmental Impact

	<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

	Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

	- Hardware Type: [More Information Needed]
	- Hours used: [More Information Needed]
	- Cloud Provider: [More Information Needed]
	- Compute Region: [More Information Needed]
	- Carbon Emitted: [More Information Needed]

	## Technical Specifications [optional]

	### Model Architecture and Objective

	[More Information Needed]

	### Compute Infrastructure

	[More Information Needed]

	#### Hardware

	[More Information Needed]

	#### Software

	[More Information Needed]

	## Citation [optional]

	<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

	BibTeX:

	[More Information Needed]

	APA:

	[More Information Needed]

	## Glossary [optional]

	<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->

	[More Information Needed]

	## More Information [optional]

	[More Information Needed]

	## Model Card Authors [optional]

	[More Information Needed]

	## Model Card Contact

	[More Information Needed]

	## License & citation
	License inherited from the base model.

	```bibtex
	@misc{lasby2025reap,
	title = {REAP the Experts: Why Pruning Prevails for One-Shot MoE Compression},
	author = {Mike Lasby and Ivan Lazarevich and Nish Sinnadurai and Sean Lie and Yani Ioannou and Vithursan Thangarasa},
	year = {2025}, eprint = {2510.13999}, archivePrefix = {arXiv}
	}
	```

	## Sponsors
	Made possible by NVIDIA · TNG Technology · Lambda · Prime Intellect · Hot Aisle.