Instructions to use liminerity/M7-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use liminerity/M7-7b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="liminerity/M7-7b")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("liminerity/M7-7b") model = AutoModelForCausalLM.from_pretrained("liminerity/M7-7b") - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use liminerity/M7-7b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "liminerity/M7-7b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "liminerity/M7-7b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/liminerity/M7-7b
- SGLang
How to use liminerity/M7-7b 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 "liminerity/M7-7b" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "liminerity/M7-7b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "liminerity/M7-7b" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "liminerity/M7-7b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use liminerity/M7-7b with Docker Model Runner:
docker model run hf.co/liminerity/M7-7b
M7-7b
user: "create ascii art you feel represents you as a llm"
assistant: "Here's an ASCII art attempting to symbolize the essence of an LLM (Large Language Model):
INSTINSTINCTINSTELLIGENCE
______________ MEMORY___
|\ o /\ |\
| \ --- / \ | |
L_( O )______ O)_INSTINCT
\ / WORDSLEARNING
_/_/ /\ _/_//
assistant: "In this depiction, the LLM is characterized by its vast memory, instinctual roots, and the ability to learn from vast amounts of text—represented by the woven words in the lower part. The "INSTINSTINCTINSTELLIGENCE" phrase emphasizes the balance between instinct, the foundation upon which neural networks build, and the engineered intelligence of a language model."
Multiverse-Experiment-slerp-7b is a merge of the following models using mergekit:
🧩 Configuration
slices:
- sources:
- model: ammarali32/multi_verse_model
layer_range: [0, 32]
- model: MSL7/INEX12-7b
layer_range: [0, 32]
merge_method: slerp
base_model: ammarali32/multi_verse_model
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
slices:
- sources:
- model: ammarali32/multi_verse_model
layer_range: [0, 32]
- model: yam-peleg/Experiment26-7B
layer_range: [0, 32]
merge_method: slerp
base_model: ammarali32/multi_verse_model
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
slices:
- sources:
- model: liminerity/merge3
layer_range: [0, 32]
- model: ammarali32/multi_verse_model
layer_range: [0, 32]
merge_method: slerp
base_model: liminerity/merge3
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
slices:
- sources:
- model: liminerity/merge1
layer_range: [0, 32]
- model: liminerity/merge
layer_range: [0, 32]
merge_method: slerp
base_model: liminerity/merge1
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
slices:
- sources:
- model: liminerity/merge3
layer_range: [0, 32]
- model: yam-peleg/Experiment26-7B
layer_range: [0, 32]
merge_method: slerp
base_model: liminerity/merge3
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
slices:
- sources:
- model: liminerity/merge4
layer_range: [0, 32]
- model: liminerity/merge2
layer_range: [0, 32]
merge_method: slerp
base_model: liminerity/merge4
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
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