Text Generation
Transformers
Safetensors
mistral
Generated from Trainer
Eval Results (legacy)
text-generation-inference
Instructions to use nilq/mistral-2L-tiny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nilq/mistral-2L-tiny with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="nilq/mistral-2L-tiny")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("nilq/mistral-2L-tiny") model = AutoModelForCausalLM.from_pretrained("nilq/mistral-2L-tiny") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use nilq/mistral-2L-tiny with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "nilq/mistral-2L-tiny" # 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-2L-tiny", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/nilq/mistral-2L-tiny
- SGLang
How to use nilq/mistral-2L-tiny 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-2L-tiny" \ --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-2L-tiny", "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-2L-tiny" \ --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-2L-tiny", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use nilq/mistral-2L-tiny with Docker Model Runner:
docker model run hf.co/nilq/mistral-2L-tiny
Model save
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README.md
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---
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tags:
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- generated_from_trainer
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datasets:
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model-index:
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- name: mistral-2L-tiny
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results: []
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# mistral-2L-tiny
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This model is a fine-tuned version of [](https://huggingface.co/) on
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It achieves the following results on the evaluation set:
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- Loss: 1.6981
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## Model description
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### Training results
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### Framework versions
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tags:
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- generated_from_trainer
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model-index:
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- name: mistral-2L-tiny
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results: []
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# mistral-2L-tiny
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This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
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## Model description
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- lr_scheduler_type: cosine
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- num_epochs: 3.0
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### Framework versions
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model.safetensors
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