Instructions to use xxx777xxxASD/PrimaMonarch-EroSumika-2x10.7B-128k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use xxx777xxxASD/PrimaMonarch-EroSumika-2x10.7B-128k with Transformers:

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

pipe = pipeline("text-generation", model="xxx777xxxASD/PrimaMonarch-EroSumika-2x10.7B-128k")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("xxx777xxxASD/PrimaMonarch-EroSumika-2x10.7B-128k")
model = AutoModelForCausalLM.from_pretrained("xxx777xxxASD/PrimaMonarch-EroSumika-2x10.7B-128k", 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 xxx777xxxASD/PrimaMonarch-EroSumika-2x10.7B-128k with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "xxx777xxxASD/PrimaMonarch-EroSumika-2x10.7B-128k"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "xxx777xxxASD/PrimaMonarch-EroSumika-2x10.7B-128k",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/xxx777xxxASD/PrimaMonarch-EroSumika-2x10.7B-128k

SGLang

How to use xxx777xxxASD/PrimaMonarch-EroSumika-2x10.7B-128k 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 "xxx777xxxASD/PrimaMonarch-EroSumika-2x10.7B-128k" \
    --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": "xxx777xxxASD/PrimaMonarch-EroSumika-2x10.7B-128k",
		"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 "xxx777xxxASD/PrimaMonarch-EroSumika-2x10.7B-128k" \
        --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": "xxx777xxxASD/PrimaMonarch-EroSumika-2x10.7B-128k",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use xxx777xxxASD/PrimaMonarch-EroSumika-2x10.7B-128k with Docker Model Runner:
```
docker model run hf.co/xxx777xxxASD/PrimaMonarch-EroSumika-2x10.7B-128k
```

(Maybe i'll change the icon picture later.)

Experimental MoE, the idea is to have more active parameters than 7xX model would have and keep it's size lower than 20B.

This model has ~19.2B parameters.

Exl2, 4.0 bpw (Fits in 12GB VRAM/16k context/4-bit cache)

Exl2, 6.0 bpw

GGUF

Base model (self merge)

slices:
  - sources:
    - model: MistralInstruct-v0.2-128k
      layer_range: [0, 24]
  - sources:
    - model: MistralInstruct-v0.2-128k
      layer_range: [8, 24]
  - sources:
    - model: MistralInstruct-v0.2-128k
      layer_range: [24, 32]
merge_method: passthrough
dtype: bfloat16

First expert ("sandwich" merge)

xxx777xxxASD/PrimaSumika-10.7B-128k

slices:
  - sources:
    - model: EroSumika-128k
      layer_range: [0, 24]
  - sources:
    - model: Prima-Lelantacles-128k
      layer_range: [8, 24]
  - sources:
    - model: EroSumika-128k
      layer_range: [24, 32]
merge_method: passthrough
dtype: bfloat16

Second expert ("sandwich" merge)

slices:
  - sources:
    - model: AlphaMonarch-7B-128k
      layer_range: [0, 24]
  - sources:
    - model: NeuralHuman-128k
      layer_range: [8, 24]
  - sources:
    - model: AlphaMonarch-7B-128k
      layer_range: [24, 32]
merge_method: passthrough
dtype: bfloat16

Each 128k model is a slerp merge with Epiculous/Fett-uccine-Long-Noodle-7B-120k-Context

Models used

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PrimaMonarch-EroSumika-2x10.7B

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OUTDATED • 4 items • Updated Jul 27, 2024 • 1