Instructions to use Jboadu/GAIA-sft-v.02 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Jboadu/GAIA-sft-v.02 with Transformers:

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

pipe = pipeline("text-generation", model="Jboadu/GAIA-sft-v.02")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Jboadu/GAIA-sft-v.02")
model = AutoModelForCausalLM.from_pretrained("Jboadu/GAIA-sft-v.02")
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 Jboadu/GAIA-sft-v.02 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Jboadu/GAIA-sft-v.02"
# 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/GAIA-sft-v.02",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Jboadu/GAIA-sft-v.02

SGLang

How to use Jboadu/GAIA-sft-v.02 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/GAIA-sft-v.02" \
    --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/GAIA-sft-v.02",
		"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/GAIA-sft-v.02" \
        --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/GAIA-sft-v.02",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Jboadu/GAIA-sft-v.02 with Docker Model Runner:
```
docker model run hf.co/Jboadu/GAIA-sft-v.02
```

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This repository is publicly accessible, but you have to accept the conditions to access its files and content.

See axolotl config

axolotl version: 0.8.0.dev0

base_model: Jboadu/test-model-1-pretrain
tokenizer_type: AutoTokenizer
model_type: AutoModelForCausalLM
load_in_8bit: false
load_in_4bit: false
strict: false
datasets:
  - path: pretraining_subset_17828.jsonl
    type: completion
  - path: com_factual/allToolCallRejections.jsonl
    type: chat_template
    field_messages: messages
    message_property_mappings:
      role: role
      content: content
  - path: com_factual/clean_output_0.jsonl
    type: chat_template
    field_messages: messages
    message_property_mappings:
      role: role
      content: content
  - path: com_factual/clean_output_1.jsonl
    type: chat_template
    field_messages: messages
    message_property_mappings:
      role: role
      content: content
  - path: com_factual/clean_output_2.jsonl
    type: chat_template
    field_messages: messages
    message_property_mappings:
      role: role
      content: content
  - path: com_factual/clean_output_3.jsonl
    type: chat_template
    field_messages: messages
    message_property_mappings:
      role: role
      content: content
  - path: com_factual/clean_output_4.jsonl
    type: chat_template
    field_messages: messages
    message_property_mappings:
      role: role
      content: content
  - path: com_factual/clean_output_5.jsonl
    type: chat_template
    field_messages: messages
    message_property_mappings:
      role: role
      content: content
  - path: com_factual/clean_output_6.jsonl
    type: chat_template
    field_messages: messages
    message_property_mappings:
      role: role
      content: content
  - path: com_factual/clean_output_7.jsonl
    type: chat_template
    field_messages: messages
    message_property_mappings:
      role: role
      content: content
  - path: axolotl_correction_conversations_GAIA_Raw_Training_Data.json
    type: input_output
  - path: generic_sft_completion/Augmentoolkit-Augmentoolkit-Capybara-2point5mil-Thoughts_300000.jsonl
    type: completion
  - path: generic_sft_completion/Augmentoolkit-Augmentoolkit-Generic-Grabbag-Thoughts_400000.jsonl
    type: completion
  - path: generic_sft_completion/Augmentoolkit-Openthoughts-100mil-DifferentFormat_800000.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: 1024
sample_packing: true
pad_to_sequence_len: false
shuffle_merged_datasets: true
gradient_accumulation_steps: 50
micro_batch_size: 2
eval_batch_size: 2
num_epochs: 7
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: false
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
wandb_project: test-project
wandb_entity: ""
wandb_watch: ""
wandb_run_id: ""
wandb_log_model: ""
hub_model_id: Jboadu/GAIA-sft-v.02
hub_strategy: all_checkpoints

GAIA-sft-v.02

This model is a fine-tuned version of Jboadu/test-model-1-pretrain on the pretraining_subset_17828.jsonl, the com_factual/allToolCallRejections.jsonl, the com_factual/clean_output_0.jsonl, the com_factual/clean_output_1.jsonl, the com_factual/clean_output_2.jsonl, the com_factual/clean_output_3.jsonl, the com_factual/clean_output_4.jsonl, the com_factual/clean_output_5.jsonl, the com_factual/clean_output_6.jsonl, the com_factual/clean_output_7.jsonl, the axolotl_correction_conversations_GAIA_Raw_Training_Data.json, the generic_sft_completion/Augmentoolkit-Augmentoolkit-Capybara-2point5mil-Thoughts_300000.jsonl, the generic_sft_completion/Augmentoolkit-Augmentoolkit-Generic-Grabbag-Thoughts_400000.jsonl, the generic_sft_completion/Augmentoolkit-Openthoughts-100mil-DifferentFormat_800000.jsonl and the generic_sft_completion/Augmentoolkit-Augmentoolkit-LMsys-800k-Thoughts_200000.jsonl datasets. It achieves the following results on the evaluation set:

Loss: 1.2146

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: 2
seed: 1337
gradient_accumulation_steps: 50
total_train_batch_size: 100
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: 18
num_epochs: 7.0

Training results

Training Loss	Epoch	Step	Validation Loss
1.3554	0.0359	1	1.9885
0.8777	0.9684	27	0.8537
0.466	1.9684	54	0.8731
0.194	2.9684	81	0.9822
0.1258	3.9684	108	1.0640
0.0793	4.9684	135	1.1310
0.0678	5.9684	162	1.1829
0.0635	6.9684	189	1.2146

Framework versions

Transformers 4.49.0
Pytorch 2.5.1+cu124
Datasets 3.2.0
Tokenizers 0.21.0

Downloads last month: -

Safetensors

Model size

7B params

Tensor type

F32

BF16

Model tree for Jboadu/GAIA-sft-v.02

Base model

mistralai/Mistral-7B-v0.3

Finetuned

mistralai/Mistral-7B-Instruct-v0.3

Finetuned

Jboadu/test-model-1-pretrain

Finetuned

(6)

this model