Instructions to use cstr/Flora_7B-laser with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use cstr/Flora_7B-laser with Transformers:

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

pipe = pipeline("text-generation", model="cstr/Flora_7B-laser")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("cstr/Flora_7B-laser")
model = AutoModelForCausalLM.from_pretrained("cstr/Flora_7B-laser", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use cstr/Flora_7B-laser with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "cstr/Flora_7B-laser"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "cstr/Flora_7B-laser",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/cstr/Flora_7B-laser

SGLang

How to use cstr/Flora_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/Flora_7B-laser" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "cstr/Flora_7B-laser",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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/Flora_7B-laser" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "cstr/Flora_7B-laser",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use cstr/Flora_7B-laser with Docker Model Runner:
```
docker model run hf.co/cstr/Flora_7B-laser
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Flora_7B-laser

A LaserRMT modification of ResplendentAI/Flora_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.


Base model	`ResplendentAI/Flora_7B`
Licence	`cc-by-sa-4.0`, 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 2026-08-03. The repository had carried only a single-line description since it was created on 2024-03-18 — modified weights published with no licence reasoning, no architecture record, and no statement of who provides the model. 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 2026-08-03 — cc-by-sa-4.0 on both sides, so this repository redistributes under the same terms it received.

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 cc-by-sa-4.0. 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 ResplendentAI/Flora_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.

A note on the chain. ResplendentAI/Flora_7B is itself a merge — it ships a mergekit_config.yml — so the training content that ultimately applies is that of its constituent models rather than of a single training run. Reconstructing that chain is the base model provider's documentation to give, not this repository's to assert.

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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Model tree for cstr/Flora_7B-laser

Base model

jeiku/FloraBase

Finetuned

ResplendentAI/Flora_7B

Finetuned

(1)

this model

Quantizations

1 model