Instructions to use AiAF/UFOs-Finetune-V1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AiAF/UFOs-Finetune-V1 with Transformers:

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

pipe = pipeline("text-generation", model="AiAF/UFOs-Finetune-V1")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("AiAF/UFOs-Finetune-V1")
model = AutoModelForCausalLM.from_pretrained("AiAF/UFOs-Finetune-V1", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use AiAF/UFOs-Finetune-V1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AiAF/UFOs-Finetune-V1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AiAF/UFOs-Finetune-V1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/AiAF/UFOs-Finetune-V1

SGLang

How to use AiAF/UFOs-Finetune-V1 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 "AiAF/UFOs-Finetune-V1" \
    --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": "AiAF/UFOs-Finetune-V1",
		"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 "AiAF/UFOs-Finetune-V1" \
        --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": "AiAF/UFOs-Finetune-V1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use AiAF/UFOs-Finetune-V1 with Docker Model Runner:
```
docker model run hf.co/AiAF/UFOs-Finetune-V1
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

See axolotl config

axolotl version: 0.6.0

base_model: mistralai/Mistral-7B-v0.1
# optionally might have model_type or tokenizer_type
model_type: MistralForCausalLM
tokenizer_type: LlamaTokenizer
# Automatically upload checkpoint and final model to HF
hub_model_id: AiAF/UFOs-Finetune-V1

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: json
    data_files: plain_qa_list.jsonl
    ds_type: json
    type: chat_template
    chat_template: chatml
    field_messages: conversations
    message_field_role: from
    message_field_content: value
    roles:
      user:
        - human
      assistant:
        - gpt
      system:
        - system

dataset_prepared_path:
val_set_size: 0.05
output_dir: ./outputs/UFOs-Finetune-V1/out

sequence_len: 8192
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: false

max_steps: 100000

wandb_project: "UFO_LLM_Finetune"
wandb_entity:
wandb_watch: "all"
wandb_name: "UFO_LLM_Finetune-V1"
wandb_log_model: "false"

gradient_accumulation_steps: 4
micro_batch_size: 2
num_epochs: 10
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.000005

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint: /workspace/axolotl/outputs/out/checkpoint-18
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true

warmup_steps: 10
evals_per_epoch: 4
eval_table_size:
eval_max_new_tokens: 128
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:

UFOs-Finetune-V1

This model is a fine-tuned version of mistralai/Mistral-7B-v0.1 on the json dataset. It achieves the following results on the evaluation set:

Loss: 1.3935

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: 5e-06
train_batch_size: 2
eval_batch_size: 2
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 8
optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 10
training_steps: 50

Training results

Training Loss	Epoch	Step	Validation Loss
1.7686	0.1111	1	1.6895
2.0582	0.3333	3	1.6884
1.9135	0.6667	6	1.6793
1.8261	1.0	9	1.6667
1.8757	1.3333	12	1.6570
1.8754	1.6667	15	1.6501
1.8426	2.0	18	1.6468
2.8515	4.1739	21	1.4353
1.3702	4.6957	24	1.4068
1.2889	5.1739	27	1.3909
1.2635	5.6957	30	1.3870
1.2139	6.1739	33	1.3874
1.1786	6.6957	36	1.3895
1.1458	7.1739	39	1.3921
1.1389	7.6957	42	1.3929
1.1255	8.1739	45	1.3934
1.1589	8.6957	48	1.3935

Framework versions

Transformers 4.48.3
Pytorch 2.5.1+cu124
Datasets 3.2.0
Tokenizers 0.21.0

Downloads last month: 5

Safetensors

Model size

7B params

Tensor type

BF16

Model tree for AiAF/UFOs-Finetune-V1

Base model

mistralai/Mistral-7B-v0.1

Finetuned

(946)

this model

Quantizations

1 model