llm.create_chat_completion(
messages = "No input example has been defined for this model task."
)YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
Quantization made by Richard Erkhov.
ML4Science-meditron - GGUF
- Model creator: https://huggingface.co/Basticooler/
- Original model: https://huggingface.co/Basticooler/ML4Science-meditron/
| Name | Quant method | Size |
|---|---|---|
| ML4Science-meditron.Q2_K.gguf | Q2_K | 2.96GB |
| ML4Science-meditron.IQ3_XS.gguf | IQ3_XS | 3.28GB |
| ML4Science-meditron.IQ3_S.gguf | IQ3_S | 3.43GB |
| ML4Science-meditron.Q3_K_S.gguf | Q3_K_S | 3.41GB |
| ML4Science-meditron.IQ3_M.gguf | IQ3_M | 3.52GB |
| ML4Science-meditron.Q3_K.gguf | Q3_K | 3.74GB |
| ML4Science-meditron.Q3_K_M.gguf | Q3_K_M | 3.74GB |
| ML4Science-meditron.Q3_K_L.gguf | Q3_K_L | 4.03GB |
| ML4Science-meditron.IQ4_XS.gguf | IQ4_XS | 4.18GB |
| ML4Science-meditron.Q4_0.gguf | Q4_0 | 4.34GB |
| ML4Science-meditron.IQ4_NL.gguf | IQ4_NL | 4.38GB |
| ML4Science-meditron.Q4_K_S.gguf | Q4_K_S | 4.37GB |
| ML4Science-meditron.Q4_K.gguf | Q4_K | 4.58GB |
| ML4Science-meditron.Q4_K_M.gguf | Q4_K_M | 4.58GB |
| ML4Science-meditron.Q4_1.gguf | Q4_1 | 4.78GB |
| ML4Science-meditron.Q5_0.gguf | Q5_0 | 5.21GB |
| ML4Science-meditron.Q5_K_S.gguf | Q5_K_S | 5.21GB |
| ML4Science-meditron.Q5_K.gguf | Q5_K | 5.34GB |
| ML4Science-meditron.Q5_K_M.gguf | Q5_K_M | 5.34GB |
| ML4Science-meditron.Q5_1.gguf | Q5_1 | 5.65GB |
| ML4Science-meditron.Q6_K.gguf | Q6_K | 6.14GB |
| ML4Science-meditron.Q8_0.gguf | Q8_0 | 7.95GB |
Original model description:
library_name: transformers base_model: OpenMeditron/Meditron3-8B tags: - generated_from_trainer model-index: - name: mloscratch/homes/bbernath/meditron_instruct/instruction_tuned_model_with_ml4science_data results: []
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
- 3
Hardware compatibility
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# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="RichardErkhov/Basticooler_-_ML4Science-meditron-gguf", filename="", )