Text Generation
Transformers
Safetensors
English
Korean
mistral
mergekit
Merge
text-generation-inference
Not-For-All-Audiences
conversational
Eval Results (legacy)
Instructions to use bamec66557/MISCHIEVOUS-12B-Mix_0.1v with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bamec66557/MISCHIEVOUS-12B-Mix_0.1v with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="bamec66557/MISCHIEVOUS-12B-Mix_0.1v") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("bamec66557/MISCHIEVOUS-12B-Mix_0.1v") model = AutoModelForCausalLM.from_pretrained("bamec66557/MISCHIEVOUS-12B-Mix_0.1v", 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 bamec66557/MISCHIEVOUS-12B-Mix_0.1v with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "bamec66557/MISCHIEVOUS-12B-Mix_0.1v" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "bamec66557/MISCHIEVOUS-12B-Mix_0.1v", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/bamec66557/MISCHIEVOUS-12B-Mix_0.1v
- SGLang
How to use bamec66557/MISCHIEVOUS-12B-Mix_0.1v 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 "bamec66557/MISCHIEVOUS-12B-Mix_0.1v" \ --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": "bamec66557/MISCHIEVOUS-12B-Mix_0.1v", "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 "bamec66557/MISCHIEVOUS-12B-Mix_0.1v" \ --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": "bamec66557/MISCHIEVOUS-12B-Mix_0.1v", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use bamec66557/MISCHIEVOUS-12B-Mix_0.1v with Docker Model Runner:
docker model run hf.co/bamec66557/MISCHIEVOUS-12B-Mix_0.1v
| slices: | |
| - Sources: | |
| - model: bamec66557/MNRP_0.5 | |
| layer_range: [0, 40] # Merge layer range for MNRP_0.5 model | |
| - model: bamec66557/MISCHIEVOUS-12B | |
| layer_range: [0, 40] # Merge layer range for MISCHIEVOUS-12B model. | |
| # Adjust the merge ratio per layer to drive smoother integration | |
| # Each filter affects a specific mechanism within the model | |
| parameters: | |
| t: | |
| - Filter: self_attn | |
| value: [0.2, 0.4, 0.6, 0.8, 1.0] # Progressive merging of self-attention layers | |
| - filter: mlp | |
| value: [0.8, 0.6, 0.4, 0.2, 0.0] # Merge MLP layers with opposite proportions | |
| - filter: layer_norm | |
| value: [0.5, 0.5, 0.5, 0.5, 0.5, 0.5] # Layer Normalisation should be merged uniformly | |
| - value: 0.7 # Default | |
| merge_method: slerp # change merge method to slerp | |
| base_model: bamec66557/MISCHIEVOUS-12B # base model for merge | |
| dtype: bfloat16 # data type for efficient and fast operations when merging | |
| # Additional available options | |
| regularisation: | |
| - method: l2_norm # Stabilise merged model weights with L2 normalisation | |
| scale: 0.01 | |
| postprocessing: | |
| - operation: smoothing # Smooth the weights after merging | |
| kernel_size: 3 | |
| - operation: normalise # normalise the overall weights | |