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

Libraries

How to use cstr/Phoenix-laser with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("cstr/Phoenix-laser")
model = AutoModelForCausalLM.from_pretrained("cstr/Phoenix-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/Phoenix-laser with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "cstr/Phoenix-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/Phoenix-laser",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

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

Docker Model Runner
How to use cstr/Phoenix-laser with Docker Model Runner:
```
docker model run hf.co/cstr/Phoenix-laser
```

cstr commited on about 1 month ago

Commit

1c9f30b

verified ·

1 Parent(s): 1e8ef08

docs: add model card — base model, licence, Art. 53 provenance

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README.md +67 -0

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	@@ -0,0 +1,67 @@

+---
+license: apache-2.0
+base_model: DRXD1000/Phoenix-7B
+base_model_relation: finetune
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+  - laserRMT
+  - mistral
+---
+# Phoenix-laser
+A **LaserRMT modification** of [`DRXD1000/Phoenix-7B`](https://huggingface.co/DRXD1000/Phoenix-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 the base was published as `DRXD1000/Phoenix` and has since been renamed `DRXD1000/Phoenix-7B`.
+| | |
+|---|---|
+| Base model | [`DRXD1000/Phoenix-7B`](https://huggingface.co/DRXD1000/Phoenix-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
+[`DRXD1000/Phoenix-7B`](https://huggingface.co/DRXD1000/Phoenix-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.