Instructions to use Basticooler/ML4Science-meditron with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Basticooler/ML4Science-meditron with Transformers:

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

pipe = pipeline("text-generation", model="Basticooler/ML4Science-meditron")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Basticooler/ML4Science-meditron")
model = AutoModelForCausalLM.from_pretrained("Basticooler/ML4Science-meditron")
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

vLLM

How to use Basticooler/ML4Science-meditron with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Basticooler/ML4Science-meditron"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Basticooler/ML4Science-meditron",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Basticooler/ML4Science-meditron

SGLang

How to use Basticooler/ML4Science-meditron 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 "Basticooler/ML4Science-meditron" \
    --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": "Basticooler/ML4Science-meditron",
		"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 "Basticooler/ML4Science-meditron" \
        --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": "Basticooler/ML4Science-meditron",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Basticooler/ML4Science-meditron with Docker Model Runner:
```
docker model run hf.co/Basticooler/ML4Science-meditron
```

See axolotl config

axolotl version: 0.4.0

base_model: OpenMeditron/Meditron3-8B
bf16: auto
output_dir: /mloscratch/homes/bbernath/meditron_instruct/instruction_tuned_model_with_ml4science_data
chat_template: llama3
datasets:
  - path: /mloscratch/homes/bbernath/meditron_instruct/datasets/mixtures/Instruction_tuning_mixture.jsonl
    type: chat_template
    ds_type: json
    split: train
    field_messages: conversations
    message_field_role: from
    message_field_content: value
  - path: /mloscratch/homes/bbernath/meditron_instruct/datasets/replay_data/datasets/pubmed_3B.jsonl
    type: completion
    ds_type: json
    field: text
    sample_ratio: 0.1
  - path: /mloscratch/homes/bbernath/meditron_instruct/datasets/replay_data/datasets/amboss_article.jsonl
    type: completion
    ds_type: json
    field: text
  - path: /mloscratch/homes/bbernath/meditron_instruct/datasets/replay_data/datasets/medmcqa.jsonl
    type: chat_template
    ds_type: json
    split: train
    field_messages: conversations
    message_field_role: from
    message_field_content: value
    sample_ratio: 0.5
  - path: /mloscratch/homes/bbernath/meditron_instruct/datasets/replay_data/datasets/pubmedqa.jsonl
    type: chat_template
    ds_type: json
    split: train
    field_messages: conversations
    message_field_role: from
    message_field_content: value
    sample_ratio: 0.2
  - path: /mloscratch/homes/bbernath/meditron_instruct/datasets/replay_data/datasets/medqa.jsonl
    type: chat_template
    ds_type: json
    split: train
    field_messages: conversations
    message_field_role: from
    message_field_content: value
shuffle_merged_datasets: true
dataset_processes: 64
flash_attention: true
sequence_len: 8192
gradient_accumulation_steps: 2
micro_batch_size: 2
train_on_inputs: false
group_by_length: false
pad_to_sequence_len: true
sample_packing: true
optimizer: adamw_torch
optim_args:
  fused: true
cosine_min_lr_ratio: 0.1
learning_rate: 1.0e-5
warmup_ratio: 0.0
weight_decay: 0.05
gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
load_in_4bit: false
load_in_8bit: false
logging_steps: 1
num_epochs: 1
# saves_per_epoch: 1
# evals_per_epoch: 2
eval_set_size: 0.0
eval_table_size: null
lr_scheduler: cosine
max_grad_norm: 1.0
resume_from_checkpoint: null
special_tokens:
  pad_token: <|end_of_text|>
tf32: false
tokenizer_type: AutoTokenizer
type: LlamaForCausalLM
seed: 42
flash_attn_rms_norm: true
flash_attn_fuse_qkv: false
early_stopping_patience: 0
eval_steps: 3000
save_steps: 3000
load_best_model_at_end: true
xformers_attention: null
distributed:
  world_size: 5
  backend: nccl
deepspeed: /mloscratch/homes/bbernath/meditron_instruct/axolotl_config/ds_config.json

wandb_project: Meditron DDX
wandb_entity: alexs-team     
wandb_name: Instruction_tune_Meditron_8b_with_ML4Science_dataset_10000_first_try

mloscratch/homes/bbernath/meditron_instruct/instruction_tuned_model_with_ml4science_data

This model is a fine-tuned version of OpenMeditron/Meditron3-8B 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: 2
eval_batch_size: 2
seed: 42
distributed_type: multi-GPU
num_devices: 6
gradient_accumulation_steps: 2
total_train_batch_size: 24
total_eval_batch_size: 12
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=fused=True
lr_scheduler_type: cosine
num_epochs: 1

Training results

Framework versions

Transformers 4.46.1
Pytorch 2.5.1+cu124
Datasets 3.0.1
Tokenizers 0.20.3

Downloads last month: 4

Safetensors

Model size

8B params

Tensor type

BF16

Model tree for Basticooler/ML4Science-meditron

Base model

EPFLiGHT/Meditron3-8B

Finetuned

(3)

this model