Instructions to use Nexus-Walker/Reson with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Nexus-Walker/Reson with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-chat-hf")
model = PeftModel.from_pretrained(base_model, "Nexus-Walker/Reson")

Transformers

How to use Nexus-Walker/Reson with Transformers:

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

pipe = pipeline("text-generation", model="Nexus-Walker/Reson")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Nexus-Walker/Reson", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Nexus-Walker/Reson with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Nexus-Walker/Reson

SGLang

How to use Nexus-Walker/Reson 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 "Nexus-Walker/Reson" \
    --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": "Nexus-Walker/Reson",
		"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 "Nexus-Walker/Reson" \
        --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": "Nexus-Walker/Reson",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Nexus-Walker/Reson with Docker Model Runner:
```
docker model run hf.co/Nexus-Walker/Reson
```

Reson / README.md

Nexus-Walker

Clarify Reson licensing and model documentation

aca599b verified 29 days ago

preview code

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---
base_model: meta-llama/Llama-2-7b-chat-hf
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:meta-llama/Llama-2-7b-chat-hf
- lora
- transformers
license: llama2
---
# Reson

Reson is an inference-only LoRA adapter for `meta-llama/Llama-2-7b-chat-hf`. It was trained on approximately 11,000 instruction/response pairs to explore reflective reasoning and strategy revision. The training data is not included here.

The adapter weights in this repository are preserved as published. They have not been merged into the base model or converted.

## Files

- `adapter_model.safetensors` and `adapter_config.json`: the published adapter.
- `tokenizer.*` and `chat_template.jinja`: the tokenizer assets and chat format published with this adapter.
- `training_logs/`: historical training checkpoints. The benchmark uses only the top-level adapter.

## Use

The base model is gated on Hugging Face. Request access from Meta and authenticate before loading it.

```python
import torch
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
base = "meta-llama/Llama-2-7b-chat-hf"
adapter = "Nexus-Walker/Reson"
tokenizer = AutoTokenizer.from_pretrained(base)
quantization = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_quant_type="nf4")
model = AutoModelForCausalLM.from_pretrained(
    base,
    quantization_config=quantization,
    torch_dtype=torch.float16,
    device_map="auto",
)
model = PeftModel.from_pretrained(model, adapter)
```

## License

Reson-authored documentation and helper code in this repository are MIT-licensed; see `LICENSE`. The adapter depends on Llama 2 and remains subject to Meta's Llama 2 Community License. `LICENSE-LLAMA2` and its required attribution in `NOTICE` are included. Obtain the gated base model separately from Meta.

The model metadata identifies the adapter's Llama 2 license. No benchmark results are published here yet.

	---
	base_model: meta-llama/Llama-2-7b-chat-hf
	library_name: peft
	pipeline_tag: text-generation
	tags:
	- base_model:adapter:meta-llama/Llama-2-7b-chat-hf
	- lora
	- transformers
	license: llama2
	---

	# Reson

	Reson is an inference-only LoRA adapter for `meta-llama/Llama-2-7b-chat-hf`. It was trained on approximately 11,000 instruction/response pairs to explore reflective reasoning and strategy revision. The training data is not included here.

	The adapter weights in this repository are preserved as published. They have not been merged into the base model or converted.

	## Files

	- `adapter_model.safetensors` and `adapter_config.json`: the published adapter.
	- `tokenizer.*` and `chat_template.jinja`: the tokenizer assets and chat format published with this adapter.
	- `training_logs/`: historical training checkpoints. The benchmark uses only the top-level adapter.

	## Use

	The base model is gated on Hugging Face. Request access from Meta and authenticate before loading it.

	```python
	import torch
	from peft import PeftModel
	from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig

	base = "meta-llama/Llama-2-7b-chat-hf"
	adapter = "Nexus-Walker/Reson"
	tokenizer = AutoTokenizer.from_pretrained(base)
	quantization = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_quant_type="nf4")
	model = AutoModelForCausalLM.from_pretrained(
	base,
	quantization_config=quantization,
	torch_dtype=torch.float16,
	device_map="auto",
	)
	model = PeftModel.from_pretrained(model, adapter)
	```

	## License

	Reson-authored documentation and helper code in this repository are MIT-licensed; see `LICENSE`. The adapter depends on Llama 2 and remains subject to Meta's Llama 2 Community License. `LICENSE-LLAMA2` and its required attribution in `NOTICE` are included. Obtain the gated base model separately from Meta.

	The model metadata identifies the adapter's Llama 2 license. No benchmark results are published here yet.