Instructions to use Cran-May/tempemotacilla-miscii0218-0302 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Cran-May/tempemotacilla-miscii0218-0302 with Transformers:

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

pipe = pipeline("text-generation", model="Cran-May/tempemotacilla-miscii0218-0302")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Cran-May/tempemotacilla-miscii0218-0302")
model = AutoModelForCausalLM.from_pretrained("Cran-May/tempemotacilla-miscii0218-0302", 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 Cran-May/tempemotacilla-miscii0218-0302 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Cran-May/tempemotacilla-miscii0218-0302"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Cran-May/tempemotacilla-miscii0218-0302",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Cran-May/tempemotacilla-miscii0218-0302

SGLang

How to use Cran-May/tempemotacilla-miscii0218-0302 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 "Cran-May/tempemotacilla-miscii0218-0302" \
    --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": "Cran-May/tempemotacilla-miscii0218-0302",
		"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 "Cran-May/tempemotacilla-miscii0218-0302" \
        --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": "Cran-May/tempemotacilla-miscii0218-0302",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Cran-May/tempemotacilla-miscii0218-0302 with Docker Model Runner:
```
docker model run hf.co/Cran-May/tempemotacilla-miscii0218-0302
```

miscii-14b-0218

Image source: The Angel’s Message

This is a merge of pre-trained language models created using mergekit.

Merge Details

Merge Method

This model was merged using the Model Stock merge method using /Users/sthenno/models/tempesthenno-ppo-enchanted as a base.

Models Merged

The following models were included in the merge:

/Users/sthenno/models/tempesthenno-sft-0218-stage2-ckpt40
/Users/sthenno/models/tempesthenno-sft-0218-stage2-ckpt50
/Users/sthenno/models/tempesthenno-sft-0218-stage2-ckpt60
/Users/sthenno/models/tempesthenno-sft-0218-ckpt60
/Users/sthenno/models/tempesthenno-sft-0218-ckpt80

Configuration

The following YAML configuration was used to produce this model:

name: tempesthenno-ms-0218
merge_method: model_stock
base_model: /Users/sthenno/models/tempesthenno-ppo-enchanted
tokenizer:
  source: base
dtype: float32
out_dtype: bfloat16
parameters:
  int8_mask: true
  normalize: true
  rescale: false
models:
  - model: /Users/sthenno/models/tempesthenno-sft-0218-ckpt60
  - model: /Users/sthenno/models/tempesthenno-sft-0218-ckpt80
  - model: /Users/sthenno/models/tempesthenno-sft-0218-stage2-ckpt40
  - model: /Users/sthenno/models/tempesthenno-sft-0218-stage2-ckpt50
  - model: /Users/sthenno/models/tempesthenno-sft-0218-stage2-ckpt60