Instructions to use ce-lery/mistral-2b-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ce-lery/mistral-2b-base with Transformers:

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

pipe = pipeline("text-generation", model="ce-lery/mistral-2b-base")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("ce-lery/mistral-2b-base")
model = AutoModelForMultimodalLM.from_pretrained("ce-lery/mistral-2b-base")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ce-lery/mistral-2b-base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ce-lery/mistral-2b-base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ce-lery/mistral-2b-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ce-lery/mistral-2b-base

SGLang

How to use ce-lery/mistral-2b-base 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 "ce-lery/mistral-2b-base" \
    --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": "ce-lery/mistral-2b-base",
		"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 "ce-lery/mistral-2b-base" \
        --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": "ce-lery/mistral-2b-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ce-lery/mistral-2b-base with Docker Model Runner:
```
docker model run hf.co/ce-lery/mistral-2b-base
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

mistral-2b-base

Welcome to my model card!

This Model feature is ...

trained by japanese
trained in two stages: patch level and token level
Suppression of unknown word generation by using byte fallback in SentencePiece tokenizer and conversion to huggingface Tokenizers format
Use of Mistral 2B

Yukkuri shite ittene!

How to use the model

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_path = "ce-lery/mistral-2b-base"
torch.set_float32_matmul_precision('high')

device = "cuda"
if (device != "cuda" and device != "cpu"):
    device = "cpu"

tokenizer = AutoTokenizer.from_pretrained(model_path,use_fast=False)
model = AutoModelForCausalLM.from_pretrained(model_path,
                                             trust_remote_code=True,
                                             ).to(device)

prompt = "自然言語処理とは、"
inputs = tokenizer(prompt,
                   add_special_tokens=True,
                   return_tensors="pt").to(model.device)
with torch.no_grad():
    outputs = model.generate(
        inputs["input_ids"],
        max_new_tokens=4096,
        do_sample=True,
        early_stopping=False,
        top_p=0.95,
        top_k=50,
        temperature=0.7,
        no_repeat_ngram_size=2,
        num_beams=3
    )

print(outputs.tolist()[0])
outputs_txt = tokenizer.decode(outputs[0])
print(outputs_txt)

Training and evaluation data

40B token. The contents are following.

Wikipedia
Wikibooks
Wikiversity
CC-100
OSCAR2109
mC4 (head 150GB)

Training procedure

Please refer ce-lery/mistral-2b-recipe.
The Guide for this repository is published here. It is written in Japanese.

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0002
train_batch_size: 2
eval_batch_size: 2
seed: 42
gradient_accumulation_steps: 128
total_train_batch_size: 256
optimizer: Use adamw_bnb_8bit with betas=(0.9,0.95) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine_with_min_lr
lr_scheduler_warmup_steps: 1000
num_epochs: 1.0

Training results

Please refer here.

Framework versions

Transformers 4.46.2
Pytorch 2.4.0a0+f70bd71a48.nv24.06
Datasets 2.20.0
Tokenizers 0.20.3

Downloads last month: 115

Safetensors

Model size

2B params

Tensor type

BF16

Model tree for ce-lery/mistral-2b-base

Quantizations

2 models