Instructions to use NovoCode/Mistral-NeuralDPO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use NovoCode/Mistral-NeuralDPO with Transformers:

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

pipe = pipeline("text-generation", model="NovoCode/Mistral-NeuralDPO")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("NovoCode/Mistral-NeuralDPO")
model = AutoModelForCausalLM.from_pretrained("NovoCode/Mistral-NeuralDPO")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use NovoCode/Mistral-NeuralDPO with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/NovoCode/Mistral-NeuralDPO

SGLang

How to use NovoCode/Mistral-NeuralDPO 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 "NovoCode/Mistral-NeuralDPO" \
    --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": "NovoCode/Mistral-NeuralDPO",
		"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 "NovoCode/Mistral-NeuralDPO" \
        --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": "NovoCode/Mistral-NeuralDPO",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use NovoCode/Mistral-NeuralDPO with Docker Model Runner:
```
docker model run hf.co/NovoCode/Mistral-NeuralDPO
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

See axolotl config

axolotl version: 0.4.0

base_model: mistralai/Mistral-7B-v0.1
model_type: MistralForCausalLM
tokenizer_type: LlamaTokenizer
is_mistral_derived_model: true

load_in_8bit: false
load_in_4bit: false
strict: false

rl: dpo
datasets:
  - path: NeuralNovel/Neural-DPO
    split: train
    type: chatml.intel
    format: "[INST] {instruction} [/INST]"
    no_input_format: "[INST] {instruction} [/INST]"

dataset_prepared_path:
val_set_size: 0.05
output_dir: ./out

sequence_len: 8192
sample_packing: false
pad_to_sequence_len: true
eval_sample_packing: false

wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:

gradient_accumulation_steps: 4
micro_batch_size: 2
num_epochs: 1
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.000005

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

gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true

warmup_steps: 10
evals_per_epoch: 4
eval_table_size:
eval_max_new_tokens: 128
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<s>"
  eos_token: "</s>"
  unk_token: "<unk>"

out

This model is a fine-tuned version of mistralai/Mistral-7B-v0.1 on the Neural-DPO 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: 5e-06
train_batch_size: 2
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 8
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 10
training_steps: 134

Training results

Framework versions

Transformers 4.38.0.dev0
Pytorch 2.2.0+cu121
Datasets 2.17.0
Tokenizers 0.15.0

Downloads last month: 11

Safetensors

Model size

7B params

Tensor type

BF16

Model tree for NovoCode/Mistral-NeuralDPO

Base model

mistralai/Mistral-7B-v0.1

Finetuned

(1030)

this model

Quantizations

1 model

NovoCode
/

Mistral-NeuralDPO

out

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Model tree for NovoCode/Mistral-NeuralDPO

Dataset used to train NovoCode/Mistral-NeuralDPO