Instructions to use sthenno-com/miscii-14b-1225 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sthenno-com/miscii-14b-1225 with Transformers:

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

pipe = pipeline("text-generation", model="sthenno-com/miscii-14b-1225")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("sthenno-com/miscii-14b-1225")
model = AutoModelForCausalLM.from_pretrained("sthenno-com/miscii-14b-1225")
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 sthenno-com/miscii-14b-1225 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sthenno-com/miscii-14b-1225"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sthenno-com/miscii-14b-1225",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/sthenno-com/miscii-14b-1225

SGLang

How to use sthenno-com/miscii-14b-1225 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 "sthenno-com/miscii-14b-1225" \
    --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": "sthenno-com/miscii-14b-1225",
		"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 "sthenno-com/miscii-14b-1225" \
        --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": "sthenno-com/miscii-14b-1225",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use sthenno-com/miscii-14b-1225 with Docker Model Runner:
```
docker model run hf.co/sthenno-com/miscii-14b-1225
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

miscii-14b-1225

Image source: Rrhar'il | Phigros

Prompting & Usage

See miscii-14b-1028 for more details.

Training Details

Coming soon

Merge Details

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

Open LLM Leaderboard Evaluation Results

Congratulations to the miscii series models for surpassing 40 points for the first time! As of December 25, 2024, this should be the best-performing 14B model in the tests, right?