Instructions to use Afterparty-hf/Finetune-test1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Afterparty-hf/Finetune-test1 with Transformers:

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

pipe = pipeline("text-generation", model="Afterparty-hf/Finetune-test1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Afterparty-hf/Finetune-test1")
model = AutoModelForCausalLM.from_pretrained("Afterparty-hf/Finetune-test1")
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 Afterparty-hf/Finetune-test1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Afterparty-hf/Finetune-test1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Afterparty-hf/Finetune-test1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Afterparty-hf/Finetune-test1

SGLang

How to use Afterparty-hf/Finetune-test1 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 "Afterparty-hf/Finetune-test1" \
    --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": "Afterparty-hf/Finetune-test1",
		"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 "Afterparty-hf/Finetune-test1" \
        --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": "Afterparty-hf/Finetune-test1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Afterparty-hf/Finetune-test1 with Docker Model Runner:
```
docker model run hf.co/Afterparty-hf/Finetune-test1
```

See axolotl config

axolotl version: 0.4.1

base_model: Afterparty-hf/pretrain-0.924
load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: Afterparty-hf/synthetic-instruct
    type: sharegpt


  - path: Afterparty-hf/train-format-server
    type: sharegpt


  - path: Afterparty-hf/help-channels-formatted
    type: sharegpt


  - path: Afterparty-hf/constt-augmented
    type: sharegpt


  - path: Afterparty-hf/transcripts-train
    type: sharegpt


chat_template: chatml
dataset_prepared_path: ./prepath
hub_model_id: Afterparty-hf/finetune-0.559
wandb_project: ap_publi
hf_use_auth_token: true


output_dir: ./finetune-559-a
resume_from_checkpoint: ./finetune-559/checkpoint-1026
wandb_watch: all
hub_private_repo: true
hub_strategy: all_checkpoints
push_to_hub: false
hf_use_auth_token: true
max_grad_norm: 0.6
sequence_len: 14256
sample_packing: true
pad_to_sequence_len: true
micro_batch_size: 1
gradient_accumulation_steps: 1
num_epochs: 4
learning_rate: 0.000004
optimizer: adamw_bnb_8bit
#optim_args:
 # amsgrad: true
lr_scheduler: cosine
train_on_inputs: false
group_by_length: false
bfloat16: false
#bf16: auto
fp16:
tf32: false
neftune_noise_alpha: 2
gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: true
logging_steps: 1
xformers_attention:
flash_attention: true
#unsloth_lora_mlp: true
#unsloth_lora_qkv: true
#unsloth_lora_o: true
#flash_attn_cross_entropy: true
#flash_attn_rms_norm: true
#flash_attn_fuse_qkv: false
#flash_attn_fuse_mlp: true
warmup_ratio: 0.5
evals_per_step: 0.025
eval_table_size:
saves_per_epoch: 5
debug:
torch_compile: true
rank:
deepspeed: deepspeed_configs/zero2.json
save_safetensors: true
weight_decay: 0.01
special_tokens:
   bos_token: "<s>"
   eos_token: "</s>"
   unk_token: "<unk>"
   pad_token: "</s>"
tokens: # these are delimiters
  - "<|im_start|>"
  - "<|im_end|>"

finetune-0.559

This model is a fine-tuned version of Afterparty-hf/pretrain-0.924 on the None dataset.

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: 4e-06
train_batch_size: 1
eval_batch_size: 1
seed: 42
distributed_type: multi-GPU
num_devices: 8
total_train_batch_size: 8
total_eval_batch_size: 8
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 310
num_epochs: 4

Training results

Framework versions

Transformers 4.41.1
Pytorch 2.1.2+cu118
Datasets 2.19.1
Tokenizers 0.19.1

Downloads last month: 3

Safetensors

Model size

7B params

Tensor type

BF16

Model tree for Afterparty-hf/Finetune-test1

Base model

Afterparty-hf/pretrain-0.924

Finetuned

(1)

this model