Instructions to use Retreatcost/Evertide-RX-12B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Retreatcost/Evertide-RX-12B with Transformers:

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

pipe = pipeline("text-generation", model="Retreatcost/Evertide-RX-12B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Retreatcost/Evertide-RX-12B")
model = AutoModelForCausalLM.from_pretrained("Retreatcost/Evertide-RX-12B")
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

vLLM

How to use Retreatcost/Evertide-RX-12B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Retreatcost/Evertide-RX-12B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Retreatcost/Evertide-RX-12B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Retreatcost/Evertide-RX-12B

SGLang

How to use Retreatcost/Evertide-RX-12B 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 "Retreatcost/Evertide-RX-12B" \
    --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": "Retreatcost/Evertide-RX-12B",
		"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 "Retreatcost/Evertide-RX-12B" \
        --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": "Retreatcost/Evertide-RX-12B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Retreatcost/Evertide-RX-12B with Docker Model Runner:
```
docker model run hf.co/Retreatcost/Evertide-RX-12B
```

Retreatcost commited on 12 days ago

Commit

3b0812d

verified ·

1 Parent(s): 5cc39f5

Update README.md

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@@ -47,7 +47,7 @@ For absolute consistency you can prefill model responses with "< think >\n" (thi
 - General conversations, chatting.
 - Co-writing, brainstorming.
-- Short roleplaying
 ## Inference Tips
@@ -63,7 +63,7 @@ For absolute consistency you can prefill model responses with "< think >\n" (thi
 I haven't really tested or trained the model for long context, so it will probably break earlier than regular models.
 You can set a higher context, for example 16K, 24K or 32K, but I don't guarantee how it will behave.
-### Training details
 <details>
   <summary>Spoiler warning</summary>
@@ -134,3 +134,20 @@ dtype: bfloat16
 ```
 </details>

 - General conversations, chatting.
 - Co-writing, brainstorming.
+- Short roleplaying.
 ## Inference Tips
 I haven't really tested or trained the model for long context, so it will probably break earlier than regular models.
 You can set a higher context, for example 16K, 24K or 32K, but I don't guarantee how it will behave.
+## Training details
 <details>
   <summary>Spoiler warning</summary>
 ```
 </details>
+## FAQ
+### Is this model better than X model?
+Probably not.
+### Is it an NSFW model?
+Not exactly. With some prompting it is definitely capable to output something, but it's not designed to be an ERP model in the first place. I would rate it 4/10 in this department, it's by design.
+### Is it an uncensored model?
+The same as above, it will absolutely refuse some of your more unhinged prompts. You can try to abliterate it, tho.
+### Why isn't it NSFW/uncensored by default?
+For this model achieving ERP capabilities wasn't the goal, so I'm happy with current state.
+### RP/ERP model when?
+Soon™.