Instructions to use mayacinka/West-Ramen-7Bx4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mayacinka/West-Ramen-7Bx4 with Transformers:

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

pipe = pipeline("text-generation", model="mayacinka/West-Ramen-7Bx4")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mayacinka/West-Ramen-7Bx4")
model = AutoModelForCausalLM.from_pretrained("mayacinka/West-Ramen-7Bx4")
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 mayacinka/West-Ramen-7Bx4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mayacinka/West-Ramen-7Bx4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mayacinka/West-Ramen-7Bx4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mayacinka/West-Ramen-7Bx4

SGLang

How to use mayacinka/West-Ramen-7Bx4 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 "mayacinka/West-Ramen-7Bx4" \
    --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": "mayacinka/West-Ramen-7Bx4",
		"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 "mayacinka/West-Ramen-7Bx4" \
        --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": "mayacinka/West-Ramen-7Bx4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use mayacinka/West-Ramen-7Bx4 with Docker Model Runner:
```
docker model run hf.co/mayacinka/West-Ramen-7Bx4
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

🧩 Configuration

base_model: /home/Ubuntu/Desktop/mergekit/models/Mistral-7B-Instruct-v0.2
gate_mode: hidden 
dtype: bfloat16 
experts:
  - source_model: /home/Ubuntu/Desktop/mergekit/models/Mistral-7B-Instruct-v0.2
    positive_prompts:
      - "instructions"
      - "concise"
      - "straightforward"
      - "helpful"
      - "assistant"
    negative_prompts:
      - "vague"
      - "inaccurate"
      - "verbose"
      - "complicated"
      - "speculative"
  - source_model: /home/Ubuntu/Desktop/mergekit/models/NeuralOmniWestBeaglake-7B
    positive_prompts:
      - "storytelling"
      - "role play"
      - "imagine"
      - "artistic"
      - "narrative"
  - source_model: /home/Ubuntu/Desktop/mergekit/models/Kunoichi-DPO-v2-7B
    positive_prompts:
      - "reason"
      - "think step by step"
      - "logic"
      - "knowledge"
    negative_prompts:
      - "artistic"
      - "speculative"
      - "playful"
  - source_model: /home/Ubuntu/Desktop/mergekit/models/Starling-LM-7B-alpha
    positive_prompts:
      - "code"
      - "python"
      - "javascript"
      - "react"
      - "clear"
      - "programming"
    negative_prompts:
      - "error"
      - "art"
      - "role play"

💻 Usage

!pip install -qU transformers bitsandbytes accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "mayacinka/West-Ramen-7Bx4"

tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    model_kwargs={"torch_dtype": torch.float16, "load_in_4bit": True},
)

messages = [{"role": "user", "content": "Explain what a Mixture of Experts is in less than 100 words."}]
prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	69.33
AI2 Reasoning Challenge (25-Shot)	67.58
HellaSwag (10-Shot)	85.52
MMLU (5-Shot)	62.69
TruthfulQA (0-shot)	61.00
Winogrande (5-shot)	81.22
GSM8k (5-shot)	58.00

Downloads last month: 65

Safetensors

Model size

24B params

Tensor type

BF16

Model tree for mayacinka/West-Ramen-7Bx4

Quantizations

2 models

Evaluation results

normalized accuracy on AI2 Reasoning Challenge (25-Shot)
test set Open LLM Leaderboard

67.580
normalized accuracy on HellaSwag (10-Shot)
validation set Open LLM Leaderboard

85.520
accuracy on MMLU (5-Shot)
test set Open LLM Leaderboard

62.690
mc2 on TruthfulQA (0-shot)
validation set Open LLM Leaderboard

61.000
accuracy on Winogrande (5-shot)
validation set Open LLM Leaderboard

81.220
accuracy on GSM8k (5-shot)
test set Open LLM Leaderboard

58.000