Instructions to use Cran-May/PCB-NRSheared-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Cran-May/PCB-NRSheared-1 with Transformers:

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

pipe = pipeline("text-generation", model="Cran-May/PCB-NRSheared-1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Cran-May/PCB-NRSheared-1")
model = AutoModelForCausalLM.from_pretrained("Cran-May/PCB-NRSheared-1")
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 Cran-May/PCB-NRSheared-1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Cran-May/PCB-NRSheared-1"
# 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/PCB-NRSheared-1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Cran-May/PCB-NRSheared-1

SGLang

How to use Cran-May/PCB-NRSheared-1 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/PCB-NRSheared-1" \
    --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/PCB-NRSheared-1",
		"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/PCB-NRSheared-1" \
        --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/PCB-NRSheared-1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Cran-May/PCB-NRSheared-1 with Docker Model Runner:
```
docker model run hf.co/Cran-May/PCB-NRSheared-1
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

merge

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

Merge Details

Merge Method

This model was merged using the DELLA merge method using huihui-ai/Mistral-Small-24B-Instruct-2501-abliterated as a base.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

# 文件名: pcb_della_merge.yaml
merge_method: della  # 基于DELLA的自适应剪裁
base_model: huihui-ai/Mistral-Small-24B-Instruct-2501-abliterated
models:
  - model: trashpanda-org/MS-24B-Instruct-Mullein-v0
    parameters:
      weight: 1.0  # 添加默认权重
      # PCB策略：限制层影响范围 + 动态竞争平衡
      layers:
        - layers: "8-16"
          parameter_name: density
          value: 0.4
        - layers: "8-16"
          parameter_name: epsilon
          value: 0.15
        - layers: "8-16"
          parameter_name: lambda
          value: 1.5
        - layers: "17-24"
          parameter_name: density
          value: 0.2
      variance_threshold: 0.3

  - model: AlSamCur123/Mistral-Small3-24B-InstructContinuedFine
    parameters:
      weight: 1.0  # 添加默认权重
      # 强化指令理解层
      layers:
        - layers: "0-12"
          parameter_name: density
          value: 0.7
        - layers: "0-12"
          parameter_name: epsilon
          value: 0.05
        - layers: "0-12"
          parameter_name: lambda
          value: 2.0
      variance_threshold: 0.25

  - model: huihui-ai/Mistral-Small-24B-Instruct-2501-abliterated
    parameters:
      weight: 1.0  # 添加默认权重
      # 基模型参数保护策略
      density: 0.9          # 全局密度 (可能需要单独处理)
      layers:
        - layers: "12-24"
          parameter_name: density
          value: 1.0

parameters:
  global_density: 0.55      # 全局剪裁密度（PCB平衡点）
  intra_balance: true
  variance_threshold: 0.2
  epsilon_range: [0.1, 0.2]

tokenizer:
  source: base

# 参数压缩设置（实现12-13B目标）
architecture:
  hidden_size: 4096
  intermediate_size: 11008
  num_attention_heads: 32
  num_hidden_layers: 30