Instructions to use MNC-LLM/Mistral-7B-NWS-u2k-Marcoroni-prompt-found-LaAdMoAl-ep4lr5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MNC-LLM/Mistral-7B-NWS-u2k-Marcoroni-prompt-found-LaAdMoAl-ep4lr5 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="MNC-LLM/Mistral-7B-NWS-u2k-Marcoroni-prompt-found-LaAdMoAl-ep4lr5")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("MNC-LLM/Mistral-7B-NWS-u2k-Marcoroni-prompt-found-LaAdMoAl-ep4lr5")
model = AutoModelForCausalLM.from_pretrained("MNC-LLM/Mistral-7B-NWS-u2k-Marcoroni-prompt-found-LaAdMoAl-ep4lr5")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use MNC-LLM/Mistral-7B-NWS-u2k-Marcoroni-prompt-found-LaAdMoAl-ep4lr5 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MNC-LLM/Mistral-7B-NWS-u2k-Marcoroni-prompt-found-LaAdMoAl-ep4lr5"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MNC-LLM/Mistral-7B-NWS-u2k-Marcoroni-prompt-found-LaAdMoAl-ep4lr5",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/MNC-LLM/Mistral-7B-NWS-u2k-Marcoroni-prompt-found-LaAdMoAl-ep4lr5

SGLang

How to use MNC-LLM/Mistral-7B-NWS-u2k-Marcoroni-prompt-found-LaAdMoAl-ep4lr5 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 "MNC-LLM/Mistral-7B-NWS-u2k-Marcoroni-prompt-found-LaAdMoAl-ep4lr5" \
    --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": "MNC-LLM/Mistral-7B-NWS-u2k-Marcoroni-prompt-found-LaAdMoAl-ep4lr5",
		"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 "MNC-LLM/Mistral-7B-NWS-u2k-Marcoroni-prompt-found-LaAdMoAl-ep4lr5" \
        --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": "MNC-LLM/Mistral-7B-NWS-u2k-Marcoroni-prompt-found-LaAdMoAl-ep4lr5",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use MNC-LLM/Mistral-7B-NWS-u2k-Marcoroni-prompt-found-LaAdMoAl-ep4lr5 with Docker Model Runner:
```
docker model run hf.co/MNC-LLM/Mistral-7B-NWS-u2k-Marcoroni-prompt-found-LaAdMoAl-ep4lr5
```

batch1_epochs4_lr1e-05_paged_adamw_32bit_cosine_length2048_warmup_0.05_max_grad1.0_grad_accu32

This model is a fine-tuned version of MNC-LLM/Mistral-7B-NWS-u2k-merge-Marcoroni on the None dataset.

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 1e-05
train_batch_size: 1
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 32
total_train_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.05
num_epochs: 4

Training results

Framework versions

Transformers 4.34.0
Pytorch 2.1.0
Datasets 2.14.7
Tokenizers 0.14.1

Downloads last month: 5

Model tree for MNC-LLM/Mistral-7B-NWS-u2k-Marcoroni-prompt-found-LaAdMoAl-ep4lr5

Base model

MNC-LLM/Mistral-7B-NWS-u2k-merge-Marcoroni

Finetuned

(2)

this model