Text Generation
Transformers
Safetensors
mistral
Generated from Trainer
Eval Results (legacy)
text-generation-inference
Instructions to use nilq/mistral-1L-mini with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nilq/mistral-1L-mini with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="nilq/mistral-1L-mini")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("nilq/mistral-1L-mini") model = AutoModelForCausalLM.from_pretrained("nilq/mistral-1L-mini", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use nilq/mistral-1L-mini with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "nilq/mistral-1L-mini" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "nilq/mistral-1L-mini", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/nilq/mistral-1L-mini
- SGLang
How to use nilq/mistral-1L-mini 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 "nilq/mistral-1L-mini" \ --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": "nilq/mistral-1L-mini", "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 "nilq/mistral-1L-mini" \ --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": "nilq/mistral-1L-mini", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use nilq/mistral-1L-mini with Docker Model Runner:
docker model run hf.co/nilq/mistral-1L-mini
Commit History
-
End of training
Verified signature5da5904 -
Model save
Verified signature2c78d6a -
Model save
Verified signature26d6123 -
Training in progress, step 90000
Verified signature0f33b7d -
Training in progress, step 85000
Verified signature14ed24f -
Training in progress, step 80000
Verified signature74765ed -
Training in progress, step 75000
Verified signature79e6952 -
Training in progress, step 70000
Verified signaturedbd7b81 -
Training in progress, step 65000
Verified signature2a0d19c -
Training in progress, step 60000
Verified signature5dcede0 -
Training in progress, step 55000
Verified signature02e0eb6 -
Training in progress, step 50000
Verified signaturefa25239 -
Training in progress, step 45000
Verified signature368a213 -
Training in progress, step 40000
Verified signaturecf4c8e3 -
Training in progress, step 35000
Verified signature93a3111 -
Training in progress, step 30000
Verified signature247cd8f -
Training in progress, step 25000
Verified signature7f61279 -
Training in progress, step 20000
Verified signature7e20172 -
Training in progress, step 15000
Verified signaturefadc32a -
Training in progress, step 10000
Verified signature965fa4d -
Training in progress, step 5000
Verified signaturee79a9b7 -
initial commit
Verified signatured235043