Instructions to use Kolyadual/MIXdevAI-llama with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Kolyadual/MIXdevAI-llama with Transformers:

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

pipe = pipeline("text-generation", model="Kolyadual/MIXdevAI-llama")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Kolyadual/MIXdevAI-llama")
model = AutoModelForCausalLM.from_pretrained("Kolyadual/MIXdevAI-llama")
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 Kolyadual/MIXdevAI-llama with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Kolyadual/MIXdevAI-llama

SGLang

How to use Kolyadual/MIXdevAI-llama 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 "Kolyadual/MIXdevAI-llama" \
    --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": "Kolyadual/MIXdevAI-llama",
		"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 "Kolyadual/MIXdevAI-llama" \
        --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": "Kolyadual/MIXdevAI-llama",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Kolyadual/MIXdevAI-llama with Docker Model Runner:
```
docker model run hf.co/Kolyadual/MIXdevAI-llama
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

MIXdevAI-llama

Легковестная ИИ, на основе Llama 3 из семейства Newton bot, созданная Kolyadual

Модели, которые были слиты:

meta-llama/Llama-3.2-1B-Instruct
KingNish/Reasoning-Llama-1b-v0.1

Конфигурации

Для создания данной модели использовалась следующая конфигурация YAML:

# merge_config.yaml
slices:
  - sources:
      - model: /home/kolyadual/newton-pocket/llama3
        layer_range: [0, 16]
      - model: /home/kolyadual/newton-pocket/llama-reasoning
        layer_range: [0, 16]
merge_method: slerp
base_model: /home/kolyadual/newton-pocket/llama3
parameters:
  t:
    - filter: self_attn
      value: [0, 0.5, 0.3, 0.7, 1]
    - filter: mlp
      value: [1, 0.5, 0.7, 0.3, 0]
    - value: 0.5
dtype: bfloat16