Instructions to use kinokokoro/cyberagent-mistral-nemo-webnovels with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kinokokoro/cyberagent-mistral-nemo-webnovels with Transformers:

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

pipe = pipeline("text-generation", model="kinokokoro/cyberagent-mistral-nemo-webnovels")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("kinokokoro/cyberagent-mistral-nemo-webnovels")
model = AutoModelForCausalLM.from_pretrained("kinokokoro/cyberagent-mistral-nemo-webnovels", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use kinokokoro/cyberagent-mistral-nemo-webnovels with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kinokokoro/cyberagent-mistral-nemo-webnovels"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kinokokoro/cyberagent-mistral-nemo-webnovels",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/kinokokoro/cyberagent-mistral-nemo-webnovels

SGLang

How to use kinokokoro/cyberagent-mistral-nemo-webnovels 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 "kinokokoro/cyberagent-mistral-nemo-webnovels" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kinokokoro/cyberagent-mistral-nemo-webnovels",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "kinokokoro/cyberagent-mistral-nemo-webnovels" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kinokokoro/cyberagent-mistral-nemo-webnovels",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use kinokokoro/cyberagent-mistral-nemo-webnovels with Docker Model Runner:
```
docker model run hf.co/kinokokoro/cyberagent-mistral-nemo-webnovels
```

See axolotl config

axolotl version: 0.4.1

base_model: cyberagent/Mistral-Nemo-Japanese-Instruct-2408
tokenizer_type: AutoTokenizer


load_in_8bit: false
load_in_4bit: false
strict: false


chat_template: chatml
datasets:
  - path: falche/paradox_test_set_200k_sharegpt
    type: sharegpt
dataset_prepared_path: last_run_prepared
val_set_size: 0.05
output_dir: ./outputs/mistral-nemo-webnovels

sequence_len: 8192
sample_packing: true
pad_to_sequence_len: true

use_wandb: true
wandb_project: mistral-nemo-webnovels
wandb_entity: augmxnt
wandb_name: mi300x-cyberagent_mistral_nemo_webnovels-fft-dsz3

gradient_accumulation_steps: 1
micro_batch_size: 8
num_epochs: 3
optimizer: paged_adamw_8bit
lr_scheduler: linear
learning_rate: 8e-6

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
xformers_attention:
flash_attention: true

warmup_steps: 100
evals_per_epoch: 2
eval_table_size:
saves_per_epoch: 1
debug:
deepspeed: axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  pad_token: <|end_of_text|>

outputs/mistral-nemo-webnovels

This model is a fine-tuned version of cyberagent/Mistral-Nemo-Japanese-Instruct-2408 on the None dataset. It achieves the following results on the evaluation set:

Loss: 1.6891

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: 8e-06
train_batch_size: 8
eval_batch_size: 8
seed: 42
distributed_type: multi-GPU
num_devices: 8
total_train_batch_size: 64
total_eval_batch_size: 64
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 100
num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss
2.6086	0.0008	1	2.5794
1.8703	0.5	615	1.8224
1.7873	1.0	1230	1.7534
1.6708	1.4976	1845	1.7214
1.6567	1.9976	2460	1.6919
1.501	2.4951	3075	1.6984
1.5237	2.9951	3690	1.6891

Framework versions

Transformers 4.45.2
Pytorch 2.5.0+rocm6.2
Datasets 3.0.1
Tokenizers 0.20.1

Training Infra

Compute sponsored by []HotAisle](https://huggingface.co/hotaisle) on an 8 x MI300X node. See the WandB Run Logs for additional details.

Downloads last month: 5

Safetensors

Model size

12B params

Tensor type

BF16

Model tree for kinokokoro/cyberagent-mistral-nemo-webnovels

Base model

mistralai/Mistral-Nemo-Base-2407

Finetuned

cyberagent/Mistral-Nemo-Japanese-Instruct-2408

Finetuned

(7)

this model