Instructions to use joseagmz/olmo-7B-Tinybook-epochs-1-lr-0002 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use joseagmz/olmo-7B-Tinybook-epochs-1-lr-0002 with Transformers:

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

pipe = pipeline("text-generation", model="joseagmz/olmo-7B-Tinybook-epochs-1-lr-0002", trust_remote_code=True)

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

tokenizer = AutoTokenizer.from_pretrained("joseagmz/olmo-7B-Tinybook-epochs-1-lr-0002", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("joseagmz/olmo-7B-Tinybook-epochs-1-lr-0002", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use joseagmz/olmo-7B-Tinybook-epochs-1-lr-0002 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "joseagmz/olmo-7B-Tinybook-epochs-1-lr-0002"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "joseagmz/olmo-7B-Tinybook-epochs-1-lr-0002",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/joseagmz/olmo-7B-Tinybook-epochs-1-lr-0002

SGLang

How to use joseagmz/olmo-7B-Tinybook-epochs-1-lr-0002 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 "joseagmz/olmo-7B-Tinybook-epochs-1-lr-0002" \
    --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": "joseagmz/olmo-7B-Tinybook-epochs-1-lr-0002",
		"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 "joseagmz/olmo-7B-Tinybook-epochs-1-lr-0002" \
        --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": "joseagmz/olmo-7B-Tinybook-epochs-1-lr-0002",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use joseagmz/olmo-7B-Tinybook-epochs-1-lr-0002 with Docker Model Runner:
```
docker model run hf.co/joseagmz/olmo-7B-Tinybook-epochs-1-lr-0002
```

Configuration Parsing Warning:In UNKNOWN_FILENAME: "auto_map.AutoTokenizer" must be a string

See axolotl config

axolotl version: 0.4.0

base_model: allenai/OLMo-7B
tokenizer_type: AutoTokenizer
model_type: AutoModelForCausalLM
trust_remote_code: true

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: utrgvseniorproject/Tinybook
    type: completion
dataset_prepared_path: /home/josegomez15/med-llm/last_run_prepared
val_set_size: 0.05
output_dir: ./ollama-7B-Tinybook-epochs-1-lr-0002

sequence_len: 4096
sample_packing: false
pad_to_sequence_len: true

wandb_project: olmo-7B-Tinybook
wandb_entity: utrgvmedai
wandb_watch:
wandb_name: olmo-7B-Tinybook-epochs-1-lr-0002
wandb_log_model:

gradient_accumulation_steps: 1
micro_batch_size: 1
num_epochs: 1
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.0002

train_on_inputs: True # make sure you have this on True
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: false #olmo doesn't support
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
flash_attn_cross_entropy: false
flash_attn_rms_norm: true
flash_attn_fuse_qkv: false
flash_attn_fuse_mlp: true

warmup_steps: 100
evals_per_epoch: 4
eval_table_size:
eval_sample_packing:
saves_per_epoch: 1
debug:
deepspeed: /home/josegomez15/axolotl/deepspeed_configs/zero2.json
weight_decay: 0.1
fsdp:
fsdp_config:
special_tokens:

ollama-7B-Tinybook-epochs-1-lr-0002

This model is a fine-tuned version of allenai/OLMo-7B on the None dataset. It achieves the following results on the evaluation set:

Loss: 2.3906

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: 0.0002
train_batch_size: 1
eval_batch_size: 1
seed: 42
distributed_type: multi-GPU
num_devices: 4
total_train_batch_size: 4
total_eval_batch_size: 4
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 100
num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss
4.3047	0.33	1	2.4062
4.0859	0.67	2	2.3906
3.9805	1.0	3	2.3906

Framework versions

Transformers 4.38.0
Pytorch 2.0.1+cu117
Datasets 2.17.0
Tokenizers 0.15.0

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