Instructions to use codewithdark/latent-recurrent-depth-lm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use codewithdark/latent-recurrent-depth-lm with Transformers:

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

pipe = pipeline("text-generation", model="codewithdark/latent-recurrent-depth-lm", trust_remote_code=True)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("codewithdark/latent-recurrent-depth-lm", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use codewithdark/latent-recurrent-depth-lm with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/codewithdark/latent-recurrent-depth-lm

SGLang

How to use codewithdark/latent-recurrent-depth-lm 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 "codewithdark/latent-recurrent-depth-lm" \
    --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": "codewithdark/latent-recurrent-depth-lm",
		"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 "codewithdark/latent-recurrent-depth-lm" \
        --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": "codewithdark/latent-recurrent-depth-lm",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use codewithdark/latent-recurrent-depth-lm with Docker Model Runner:
```
docker model run hf.co/codewithdark/latent-recurrent-depth-lm
```

codewithdark commited on Feb 23, 2025

Commit

7191f6d

verified ·

1 Parent(s): adf4ac7

Update README.md

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README.md CHANGED Viewed

@@ -60,10 +60,10 @@ The model can be used for text generation via its integrated `generate()` method
 ### Example: Direct Inference
 ```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
 # Load the model and tokenizer from the hub
-model = LatentRecurrentDepthModel.from_pretrained("codewithdark/latent-recurrent-depth-lm")
 tokenizer = AutoTokenizer.from_pretrained("codewithdark/latent-recurrent-depth-lm")
 prompt = "In the realm of language modeling"
@@ -87,7 +87,7 @@ print(generated_text)
 from transformers import AutoTokenizer, AutoModelForCausalLM
 tokenizer = AutoTokenizer.from_pretrained("codewithdark/latent-recurrent-depth-lm")
-model = LatentRecurrentDepthModel.from_pretrained("codewithdark/latent-recurrent-depth-lm")
 prompt = "In the realm of language modeling"
 input_ids = tokenizer(prompt, return_tensors="pt").input_ids

 ### Example: Direct Inference
 ```python
+from transformers import AutoModelForCausalLM, AutoTokenizer, AutoModel
 # Load the model and tokenizer from the hub
+model = AutoModelForCausalLM.from_pretrained("codewithdark/latent-recurrent-depth-lm")
 tokenizer = AutoTokenizer.from_pretrained("codewithdark/latent-recurrent-depth-lm")
 prompt = "In the realm of language modeling"
 from transformers import AutoTokenizer, AutoModelForCausalLM
 tokenizer = AutoTokenizer.from_pretrained("codewithdark/latent-recurrent-depth-lm")
+model = AutoModelForCausalLM.from_pretrained("codewithdark/latent-recurrent-depth-lm")
 prompt = "In the realm of language modeling"
 input_ids = tokenizer(prompt, return_tensors="pt").input_ids