Instructions to use ce-lery/mistral-300m-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ce-lery/mistral-300m-base with Transformers:

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

pipe = pipeline("text-generation", model="ce-lery/mistral-300m-base")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ce-lery/mistral-300m-base")
model = AutoModelForCausalLM.from_pretrained("ce-lery/mistral-300m-base", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ce-lery/mistral-300m-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-300m-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-300m-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ce-lery/mistral-300m-base

SGLang

How to use ce-lery/mistral-300m-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-300m-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-300m-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-300m-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-300m-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ce-lery/mistral-300m-base with Docker Model Runner:
```
docker model run hf.co/ce-lery/mistral-300m-base
```

mistral-300m-base

Overview

Welcome to my model card!

This Model feature is ...

Suppression of unknown word generation by using byte fallback in SentencePiece tokenizer and conversion to huggingface Tokenizers format
Pretrained by wikipedia dataset and cc100 dataset
Use of Mistral 300M

Yukkuri shite ittene!

How to use the model

from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
import torch

MODEL_NAME = "ce-lery/mistral-300m-base"
torch.set_float32_matmul_precision('high')

DEVICE = "cuda"
if torch.cuda.is_available():
    print("cuda")
    DEVICE = "cuda"
else:
    print("cpu")
    DEVICE = "cpu"

tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME,use_fast=False)
model = AutoModelForCausalLM.from_pretrained(
    MODEL_NAME,
    trust_remote_code=True,
).to(DEVICE)

# streamer = TextStreamer(tokenizer)

prompt = "自然言語処理とは、"

inputs = tokenizer(prompt, add_special_tokens=False,return_tensors="pt").to(model.device)
with torch.no_grad():

    outputs = model.generate(
        inputs["input_ids"],
        max_new_tokens=1024,
        do_sample=True,
        early_stopping=False,
        top_p=0.95,
        top_k=50,
        temperature=0.1,
        # streamer=streamer,
        no_repeat_ngram_size=2,
        num_beams=3
    )

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

Receipe

If you want to restruct this model, you can refer this Github repository.

If you find my mistake,error,...etc, please create issue. If you create pulreqest, I'm very happy!

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0003
train_batch_size: 12
eval_batch_size: 12
seed: 42
gradient_accumulation_steps: 20
total_train_batch_size: 240
optimizer: Use OptimizerNames.ADAMW_BNB 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

Training Loss	Epoch	Step	Validation Loss
3.7969	0.2212	10000	3.4418
3.659	0.4424	20000	3.2704
3.5721	0.6635	30000	3.1969
3.5678	0.8847	40000	3.1757

Framework versions

Transformers 4.55.2
Pytorch 2.8.0+cu128
Datasets 4.0.0
Tokenizers 0.21.4

Downloads last month: 15

Safetensors

Model size

0.3B params

Tensor type

BF16

Model tree for ce-lery/mistral-300m-base

Adapters

1 model