Instructions to use Jboadu/new-gaia with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Jboadu/new-gaia with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Jboadu/new-gaia")
model = AutoModelForCausalLM.from_pretrained("Jboadu/new-gaia", device_map="auto")
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 Settings

vLLM

How to use Jboadu/new-gaia with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Jboadu/new-gaia

SGLang

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

Docker Model Runner
How to use Jboadu/new-gaia with Docker Model Runner:
```
docker model run hf.co/Jboadu/new-gaia
```

new-gaia / README.md

Jboadu

End of training

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	---
	library_name: transformers
	license: apache-2.0
	base_model: Jboadu/test-model-2-pretrain
	tags:
	- axolotl
	- generated_from_trainer
	datasets:
	- axolotl_correction_conversations_GAIA_Raw_Training_Data.json
	- factual_sft_completion/combined_all_0.jsonl
	- factual_sft_completion/combined_all_2.jsonl
	- factual_sft_completion/combined_all_6.jsonl
	- factual_sft_completion/combined_all_4.jsonl
	- factual_sft_completion/combined_all_3.jsonl
	- factual_sft_completion/combined_all_1.jsonl
	- factual_sft_completion/combined_all_5.jsonl
	- factual_sft_completion/combined_all_7.jsonl
	- generic_sft_completion/Augmentoolkit-Augmentoolkit-Capybara-2point5mil-Thoughts_300000.jsonl
	- generic_sft_completion/Augmentoolkit-Openthoughts-100mil-DifferentFormat_600000.jsonl
	- generic_sft_completion/Augmentoolkit-Augmentoolkit-Generic-Grabbag-Thoughts_400000.jsonl
	- generic_sft_completion/Augmentoolkit-Augmentoolkit-LMsys-800k-Thoughts_200000.jsonl
	model-index:
	- name: new-gaia
	results: []
	---

	<!-- This model card has been generated automatically according to the information the Trainer had access to. You
	should probably proofread and complete it, then remove this comment. -->

	[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
	<details><summary>See axolotl config</summary>

	axolotl version: `0.12.0`
	```yaml
	base_model: Jboadu/test-model-2-pretrain
	tokenizer_type: AutoTokenizer
	model_type: AutoModelForCausalLM
	load_in_8bit: false
	load_in_4bit: false
	strict: false
	datasets:
	- path: axolotl_correction_conversations_GAIA_Raw_Training_Data.json
	type: input_output
	- path: factual_sft_completion/combined_all_0.jsonl
	type: completion
	- path: factual_sft_completion/combined_all_2.jsonl
	type: completion
	- path: factual_sft_completion/combined_all_6.jsonl
	type: completion
	- path: factual_sft_completion/combined_all_4.jsonl
	type: completion
	- path: factual_sft_completion/combined_all_3.jsonl
	type: completion
	- path: factual_sft_completion/combined_all_1.jsonl
	type: completion
	- path: factual_sft_completion/combined_all_5.jsonl
	type: completion
	- path: factual_sft_completion/combined_all_7.jsonl
	type: completion
	- path: generic_sft_completion/Augmentoolkit-Augmentoolkit-Capybara-2point5mil-Thoughts_300000.jsonl
	type: completion
	- path: generic_sft_completion/Augmentoolkit-Openthoughts-100mil-DifferentFormat_600000.jsonl
	type: completion
	- path: generic_sft_completion/Augmentoolkit-Augmentoolkit-Generic-Grabbag-Thoughts_400000.jsonl
	type: completion
	- path: generic_sft_completion/Augmentoolkit-Augmentoolkit-LMsys-800k-Thoughts_200000.jsonl
	type: completion
	dataset_prepared_path: last_finetune_prepared
	output_dir: ./finetune-model-output
	seed: 1337
	sequence_len: 5000
	sample_packing: true
	pad_to_sequence_len: false
	shuffle_merged_datasets: true
	gradient_accumulation_steps: 75
	micro_batch_size: 2
	eval_batch_size: 4
	num_epochs: 5
	optimizer: paged_adamw_8bit
	lr_scheduler: constant
	learning_rate: 2.0e-05
	noisy_embedding_alpha: 5
	weight_decay: 0
	train_on_inputs: false
	group_by_length: false
	bf16: true
	fp16: false
	tf32: false
	gradient_checkpointing: true
	logging_steps: 1
	xformers_attention: false
	flash_attention: true
	chat_template: chatml
	auto_resume_from_checkpoints: false
	warmup_ratio: 0.1
	evals_per_epoch: 1
	val_set_size: 0.04
	saves_per_epoch: 1
	eval_sample_packing: false
	save_total_limit: 2
	special_tokens:
	pad_token: <unk>
	use_liger_kernel: true
	plugins:
	- axolotl.integrations.liger.LigerPlugin
	liger_rope: true
	liger_rms_norm: true
	liger_glu_activation: true
	liger_layer_norm: true
	liger_fused_linear_cross_entropy: true
	sequence_length: 10000
	wandb_project: test-project
	wandb_entity: ''
	wandb_watch: ''
	wandb_run_id: ''
	wandb_log_model: ''
	hub_model_id: Jboadu/new-gaia
	hub_strategy: all_checkpoints

	```

	</details><br>

	# new-gaia

	This model is a fine-tuned version of [Jboadu/test-model-2-pretrain](https://huggingface.co/Jboadu/test-model-2-pretrain) on the axolotl_correction_conversations_GAIA_Raw_Training_Data.json, the factual_sft_completion/combined_all_0.jsonl, the factual_sft_completion/combined_all_2.jsonl, the factual_sft_completion/combined_all_6.jsonl, the factual_sft_completion/combined_all_4.jsonl, the factual_sft_completion/combined_all_3.jsonl, the factual_sft_completion/combined_all_1.jsonl, the factual_sft_completion/combined_all_5.jsonl, the factual_sft_completion/combined_all_7.jsonl, the generic_sft_completion/Augmentoolkit-Augmentoolkit-Capybara-2point5mil-Thoughts_300000.jsonl, the generic_sft_completion/Augmentoolkit-Openthoughts-100mil-DifferentFormat_600000.jsonl, the generic_sft_completion/Augmentoolkit-Augmentoolkit-Generic-Grabbag-Thoughts_400000.jsonl and the generic_sft_completion/Augmentoolkit-Augmentoolkit-LMsys-800k-Thoughts_200000.jsonl datasets.
	It achieves the following results on the evaluation set:
	- Loss: 0.5799
	- Memory/max Mem Active(gib): 31.49
	- Memory/max Mem Allocated(gib): 31.49
	- Memory/device Mem Reserved(gib): 33.38

	## 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: 2e-05
	- train_batch_size: 2
	- eval_batch_size: 4
	- seed: 1337
	- gradient_accumulation_steps: 75
	- total_train_batch_size: 150
	- optimizer: Use OptimizerNames.PAGED_ADAMW_8BIT with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
	- lr_scheduler_type: constant
	- lr_scheduler_warmup_steps: 2
	- training_steps: 20

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Mem Active(gib) \| Mem Allocated(gib) \| Mem Reserved(gib) \|
	\|:-------------:\|:------:\|:----:\|:---------------:\|:---------------:\|:------------------:\|:-----------------:\|
	\| No log \| 0 \| 0 \| 1.4924 \| 19.79 \| 19.79 \| 23.71 \|
	\| 0.8381 \| 0.9585 \| 4 \| 0.7330 \| 31.49 \| 31.49 \| 33.38 \|
	\| 0.5844 \| 1.7188 \| 8 \| 0.6324 \| 31.49 \| 31.49 \| 33.38 \|
	\| 0.4746 \| 2.4792 \| 12 \| 0.5766 \| 31.49 \| 31.49 \| 33.38 \|
	\| 0.3431 \| 3.4792 \| 16 \| 0.5799 \| 31.49 \| 31.49 \| 33.38 \|


	### Framework versions

	- Transformers 4.55.0
	- Pytorch 2.7.1+cu128
	- Datasets 4.0.0
	- Tokenizers 0.21.4