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", 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 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 May 11

Commit

31dd967

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1 Parent(s): 828baf0

Update README.md

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@@ -169,4 +169,7 @@ No, not yet, but that's one of future plans.
 ### Is the reasoning performative?
 It's hard to tell exactly, it definitely has some elements of it, but it also was trainded with some specific constraints, that force causality between thinking blocks and answer. So I would say that it's at least a hybrid. Any further improvements require RL training.
 </details>

 ### Is the reasoning performative?
 It's hard to tell exactly, it definitely has some elements of it, but it also was trainded with some specific constraints, that force causality between thinking blocks and answer. So I would say that it's at least a hybrid. Any further improvements require RL training.
+### How much samples did you train on?
+Only 451 sample, but they are all manually crafted and refined using (score-samples])[https://github.com/Retreatcost/score-samples] tool.
 </details>