Instructions to use smcleish/Recurrent-Llama-3.2-train-recurrence-8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use smcleish/Recurrent-Llama-3.2-train-recurrence-8 with Transformers:

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

pipe = pipeline("text-generation", model="smcleish/Recurrent-Llama-3.2-train-recurrence-8", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("smcleish/Recurrent-Llama-3.2-train-recurrence-8", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use smcleish/Recurrent-Llama-3.2-train-recurrence-8 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "smcleish/Recurrent-Llama-3.2-train-recurrence-8"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "smcleish/Recurrent-Llama-3.2-train-recurrence-8",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/smcleish/Recurrent-Llama-3.2-train-recurrence-8

SGLang

How to use smcleish/Recurrent-Llama-3.2-train-recurrence-8 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 "smcleish/Recurrent-Llama-3.2-train-recurrence-8" \
    --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": "smcleish/Recurrent-Llama-3.2-train-recurrence-8",
		"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 "smcleish/Recurrent-Llama-3.2-train-recurrence-8" \
        --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": "smcleish/Recurrent-Llama-3.2-train-recurrence-8",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use smcleish/Recurrent-Llama-3.2-train-recurrence-8 with Docker Model Runner:
```
docker model run hf.co/smcleish/Recurrent-Llama-3.2-train-recurrence-8
```

smcleish commited on Nov 8, 2025

Commit

ca32c19

verified ·

1 Parent(s): b27afc7

Upload config.json with huggingface_hub

Browse files

Files changed (1) hide show

config.json +1 -2

config.json CHANGED Viewed

@@ -58,8 +58,7 @@
   "test_time_noise": 0,
   "test_time_noise_type": "fixed",
   "tie_embeddings": false,
-  "tie_word_embeddings": false,
   "torch_dtype": "float32",
   "transformers_version": "4.53.1",
   "vocab_size": 128256
-}

   "test_time_noise": 0,
   "test_time_noise_type": "fixed",
   "tie_embeddings": false,
   "torch_dtype": "float32",
   "transformers_version": "4.53.1",
   "vocab_size": 128256
+}