Instructions to use cstr/NeuDist-Ro-7B-laser with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use cstr/NeuDist-Ro-7B-laser with Transformers:

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

pipe = pipeline("text-generation", model="cstr/NeuDist-Ro-7B-laser")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("cstr/NeuDist-Ro-7B-laser")
model = AutoModelForCausalLM.from_pretrained("cstr/NeuDist-Ro-7B-laser", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.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(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use cstr/NeuDist-Ro-7B-laser with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "cstr/NeuDist-Ro-7B-laser"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "cstr/NeuDist-Ro-7B-laser",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/cstr/NeuDist-Ro-7B-laser

SGLang

How to use cstr/NeuDist-Ro-7B-laser 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 "cstr/NeuDist-Ro-7B-laser" \
    --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": "cstr/NeuDist-Ro-7B-laser",
		"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 "cstr/NeuDist-Ro-7B-laser" \
        --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": "cstr/NeuDist-Ro-7B-laser",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use cstr/NeuDist-Ro-7B-laser with Docker Model Runner:
```
docker model run hf.co/cstr/NeuDist-Ro-7B-laser
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

NeuDist-Ro-7B-laser

A LaserRMT modification of flemmingmiguel/NeuDist-Ro-7B.

LaserRMT (layer-selective rank reduction) alters the base model's existing weight matrices. It is not a format conversion and not a quantisation: the weights differ from the base in substance, not merely in numeric representation.

Note: this repository shares its base with cstr/NeuDistRo-a1-laser.


Base model	`flemmingmiguel/NeuDist-Ro-7B`
Licence	`apache-2.0`, inherited from the base
Architecture	MistralForCausalLM, 32 layers (unchanged from the base)
Modification	LaserRMT layer-selective rank reduction

Provenance and EU AI Act Art. 53 note

This card was written on 2026-08-02. The repository had carried no model card at all since it was created on 2024-03-18 — modified weights published with no attribution to the model they were derived from. That is the gap this card closes, and it is worth stating plainly rather than quietly backfilling.

The base model above was not guessed from the repository name. It is recorded in this repo's own config.json as _name_or_path, and the licence is the one the base declares on the Hub as of 2026-08-02.

Provider status. Most cstr/* repositories are format conversions, where the upstream research team remains the provider of the model under Regulation (EU) 2024/1689 and the conversion changes only the numeric representation. This repository is not one of those. Modifying weights places a new model on the market, so the obligations that survive the Art. 53(2) free-and-open-source exemption — Art. 53(1)(c) and 53(1)(d) — attach here.

Art. 53(1)(c) — copyright policy. This repository does not introduce any training corpus of its own, so no text or data mining was carried out here and no rights reservations under Art. 4(3) of Directive (EU) 2019/790 were engaged by this step. The modification operates on weights already published by the base model's authors under apache-2.0. Where the base model's own training raises copyright questions, those attach to the base model's provider, whose documentation is linked above. Any credible claim that this repository redistributes material it has no right to redistribute will be acted on — contact via the Community tab.

Art. 53(1)(d) — training content. No additional training corpus was introduced by this repository. The model's training content is that of flemmingmiguel/NeuDist-Ro-7B, and its documentation is the summary that applies. This repository does not restate it, because restating training data documentation second-hand is how inaccurate provenance claims propagate.

Known limitation. The precise LaserRMT configuration used in 2024 — which layers were reduced, and to what rank — is not recorded in this repository. It is not reconstructible from the weights alone with confidence, and it is not asserted here rather than being guessed at.

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Quantizations

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