Instructions to use legionlm/Dora-Smol with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use legionlm/Dora-Smol with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("legionlm/Dora-Smol")
model = AutoModelForCausalLM.from_pretrained("legionlm/Dora-Smol")
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 legionlm/Dora-Smol with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/legionlm/Dora-Smol

SGLang

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

Docker Model Runner
How to use legionlm/Dora-Smol with Docker Model Runner:
```
docker model run hf.co/legionlm/Dora-Smol
```

See axolotl config

axolotl version: 0.4.0

base_model: /media/renfroe/llms/SmolLM-360M/

model_type: LlamaForCausalLM
tokenizer_type: GPT2Tokenizer
seed: 122887 
load_in_8bit: false
load_in_4bit: false
strict: false

max_steps: 0
resume_from_checkpoint: 
datasets:
  - path: /home/renfroe/Desktop/sqa_tiny-llama_dataset/Dynamic_Optimization_Methods_with_Applications_sqa_answers_only.json
    type:
      field_instruction: question
      field_output: answer
      format: "<|im_start|>user\n{instruction}<|im_end|>\n<|im_start|>assistant\n"
      no_input_format: "<|im_start|>user\n{instruction}<|im_end|>\n<|im_start|>assistant\n"
  - path: /home/renfroe/Dev/tinyllama-models/dataset/open_hermes_top_tech.json
    type: sharegpt
  - path:  /home/renfroe/Desktop/sqa_tiny-llama_dataset/hermes_prior_knowledge_question_expansion_with_answers.json
    type:
      field_instruction: question
      field_output: answer
      format: "<|im_start|>user\n{instruction}<|im_end|>\n<|im_start|>assistant\n"
      no_input_format: "<|im_start|>user\n{instruction}<|im_end|>\n<|im_start|>assistant\n"
  - path:  /home/renfroe/Desktop/sqa_tiny-llama_dataset/hermes_prior_knowledge_question_expansion_with_answers.json
    type:
      field_instruction: question
      field_output: answer
      format: "<|im_start|>user\n{instruction}<|im_end|>\n<|im_start|>assistant\n"
      no_input_format: "<|im_start|>user\n{instruction}<|im_end|>\n<|im_start|>assistant\n"
  - path:  /home/renfroe/Desktop/sqa_tiny-llama_dataset/or-farm_sharegpt.json
    type: sharegpt
  


dataset_prepared_path:
val_set_size: 0.2
output_dir: ./SmolLM-Ora
auto_resume_from_checkpoints: false

sequence_len: 2048
sample_packing: true
chat_template: chatml

wandb_project: SmolLM-Ora
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:

gradient_accumulation_steps: 1
micro_batch_size: 10
num_epochs: 4
optimizer: adamw_bnb_8bit
lr_scheduler: linear
weight_decay: 0.0000001
learning_rate: 0.0001
lr_scheduler_kwargs:
  #  num_cycles: 3 

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

eval_sample_packing: False

warmup_steps: 50
evals_per_epoch: 4
eval_table_size:
saves_per_epoch: 4
debug:
deepspeed:

fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|endoftext|>"
  eos_token: "<|endoftext|>"
  pad_token: "<|endoftext|>"

SmolLM-Ora

This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.8298

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: 0.0001
train_batch_size: 10
eval_batch_size: 10
seed: 122887
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 50
num_epochs: 4

Training results

Training Loss	Epoch	Step	Validation Loss
1.0131	0.01	1	1.0419
0.9727	0.25	27	0.9962
0.953	0.5	54	0.9076
0.8494	0.75	81	0.8792
0.9297	1.0	108	0.8632
0.8801	1.22	135	0.8527
0.8133	1.47	162	0.8459
0.8342	1.72	189	0.8410
0.8973	1.97	216	0.8376
0.7731	2.19	243	0.8350
0.8207	2.44	270	0.8332
0.7963	2.69	297	0.8318
0.81	2.94	324	0.8309
0.8351	3.18	351	0.8302
0.8104	3.43	378	0.8299
0.9019	3.68	405	0.8298
0.7828	3.93	432	0.8298

Framework versions

Transformers 4.40.0.dev0
Pytorch 2.0.1+cu117
Datasets 2.18.0
Tokenizers 0.15.0

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