Instructions to use ethicalabs/xLSTM-7b-Polymath with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ethicalabs/xLSTM-7b-Polymath with Transformers:

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

pipe = pipeline("text-generation", model="ethicalabs/xLSTM-7b-Polymath")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ethicalabs/xLSTM-7b-Polymath")
model = AutoModelForCausalLM.from_pretrained("ethicalabs/xLSTM-7b-Polymath", 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 ethicalabs/xLSTM-7b-Polymath with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/ethicalabs/xLSTM-7b-Polymath

SGLang

How to use ethicalabs/xLSTM-7b-Polymath 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 "ethicalabs/xLSTM-7b-Polymath" \
    --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": "ethicalabs/xLSTM-7b-Polymath",
		"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 "ethicalabs/xLSTM-7b-Polymath" \
        --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": "ethicalabs/xLSTM-7b-Polymath",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ethicalabs/xLSTM-7b-Polymath with Docker Model Runner:
```
docker model run hf.co/ethicalabs/xLSTM-7b-Polymath
```

mrs83 commited on Oct 20, 2025

Commit

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@@ -12,19 +12,19 @@ pipeline_tag: text-generation
 # Model Card for xlstm-7b-instruct-phase-2
-This model is a fine-tuned version of [ethicalabs/xLSTM-7b-Instruct](https://huggingface.co/ethicalabs/xLSTM-7b-Instruct).
-It has been trained using [TRL](https://github.com/huggingface/trl).
-## Quick start
-```python
-from transformers import pipeline
-question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
-generator = pipeline("text-generation", model="None", device="cuda")
-output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
-print(output["generated_text"])
-```
 ## Training procedure

 # Model Card for xlstm-7b-instruct-phase-2
+This model is a fine-tuned version of [ethicalabs/xLSTM-7b-Instruct](https://huggingface.co/ethicalabs/xLSTM-7b-Instruct) for task alignment.
+It has been trained using [TRL](https://github.com/huggingface/trl) using SFT on assistant-only tokens.
+The `k_proj` and `v_proj` matrices have been frozen to isolate and preserve the model's pre-trained knowledge base.
+This fine-tuning focused only on the `q_proj` (query) and FFN matrices, adapting the model's reasoning and query-retrieval mechanisms without overwriting its core, frozen knowledge.
+This experiment was designed to test the hypothesis that the model's reasoning capabilities (`q_proj`) could be specialized for math/code while its knowledge (`k_proj`, `v_proj`) remained intact.
+## Quick start
+Work in Progress!
 ## Training procedure