Instructions to use Jo1uck/mamba-11b-back with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jo1uck/mamba-11b-back with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Jo1uck/mamba-11b-back")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Jo1uck/mamba-11b-back") model = AutoModelForCausalLM.from_pretrained("Jo1uck/mamba-11b-back", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Jo1uck/mamba-11b-back with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Jo1uck/mamba-11b-back" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Jo1uck/mamba-11b-back", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Jo1uck/mamba-11b-back
- SGLang
How to use Jo1uck/mamba-11b-back 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 "Jo1uck/mamba-11b-back" \ --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": "Jo1uck/mamba-11b-back", "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 "Jo1uck/mamba-11b-back" \ --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": "Jo1uck/mamba-11b-back", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Jo1uck/mamba-11b-back with Docker Model Runner:
docker model run hf.co/Jo1uck/mamba-11b-back
Update README.md
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README.md
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@@ -47,8 +47,8 @@ from datasets import load_dataset
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from trl import SFTTrainer
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from peft import LoraConfig
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from transformers import AutoTokenizer, AutoModelForCausalLM, TrainingArguments
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tokenizer = AutoTokenizer.from_pretrained("
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model = AutoModelForCausalLM.from_pretrained("
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dataset = load_dataset("Abirate/english_quotes", split="train")
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training_args = TrainingArguments(
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output_dir="./results",
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from trl import SFTTrainer
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from peft import LoraConfig
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from transformers import AutoTokenizer, AutoModelForCausalLM, TrainingArguments
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tokenizer = AutoTokenizer.from_pretrained("Jo1uck/mamba-11b-back")
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model = AutoModelForCausalLM.from_pretrained("Jo1uck/mamba-11b-back")
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dataset = load_dataset("Abirate/english_quotes", split="train")
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training_args = TrainingArguments(
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output_dir="./results",
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