Instructions to use tarabukinivanhome/8c4aba89-6859-4a97-b822-7cb0871d2556 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tarabukinivanhome/8c4aba89-6859-4a97-b822-7cb0871d2556 with Transformers:

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

pipe = pipeline("text-generation", model="tarabukinivanhome/8c4aba89-6859-4a97-b822-7cb0871d2556")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("tarabukinivanhome/8c4aba89-6859-4a97-b822-7cb0871d2556")
model = AutoModelForCausalLM.from_pretrained("tarabukinivanhome/8c4aba89-6859-4a97-b822-7cb0871d2556", 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 tarabukinivanhome/8c4aba89-6859-4a97-b822-7cb0871d2556 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tarabukinivanhome/8c4aba89-6859-4a97-b822-7cb0871d2556"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tarabukinivanhome/8c4aba89-6859-4a97-b822-7cb0871d2556",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tarabukinivanhome/8c4aba89-6859-4a97-b822-7cb0871d2556

SGLang

How to use tarabukinivanhome/8c4aba89-6859-4a97-b822-7cb0871d2556 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 "tarabukinivanhome/8c4aba89-6859-4a97-b822-7cb0871d2556" \
    --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": "tarabukinivanhome/8c4aba89-6859-4a97-b822-7cb0871d2556",
		"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 "tarabukinivanhome/8c4aba89-6859-4a97-b822-7cb0871d2556" \
        --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": "tarabukinivanhome/8c4aba89-6859-4a97-b822-7cb0871d2556",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use tarabukinivanhome/8c4aba89-6859-4a97-b822-7cb0871d2556 with Docker Model Runner:
```
docker model run hf.co/tarabukinivanhome/8c4aba89-6859-4a97-b822-7cb0871d2556
```

See axolotl config

axolotl version: 0.11.0.dev0

absolute_data_files: false
base_model: unsloth/Qwen2-1.5B-Instruct
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
  - 129c7e4fdcc854c4_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/
  type:
    field_input: input
    field_instruction: instruct
    field_output: output
    format: '{instruction} {input}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 512
eval_table_size: null
evals_per_epoch: 1
flash_attention: false
flash_attn_fuse_mlp: false
flash_attn_fuse_qkv: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
gradient_clipping: 0.55
group_by_length: false
hub_model_id: tarabukinivanhome/8c4aba89-6859-4a97-b822-7cb0871d2556
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 5e-6
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lr_scheduler: cosine
max_steps: 100
micro_batch_size: 4
mlflow_experiment_name: /tmp/129c7e4fdcc854c4_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_torch
output_dir: miner_id_24
pad_to_sequence_len: false
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 2048
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 8e97898c-bec3-4da9-aba8-9dd8984401a9
wandb_project: s56-7
wandb_run: your_name
wandb_runid: 8e97898c-bec3-4da9-aba8-9dd8984401a9
warmup_steps: 5
weight_decay: 0.01
xformers_attention: false

8c4aba89-6859-4a97-b822-7cb0871d2556

This model is a fine-tuned version of unsloth/Qwen2-1.5B-Instruct on an unknown 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: 5e-06
train_batch_size: 4
eval_batch_size: 4
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 16
optimizer: Use OptimizerNames.ADAMW_TORCH 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: 5
training_steps: 100

Training results

Framework versions

Transformers 4.52.4
Pytorch 2.6.0+cu124
Datasets 3.6.0
Tokenizers 0.21.1

Downloads last month: 7

Safetensors

Model size

2B params

Tensor type

F32

BF16

Model tree for tarabukinivanhome/8c4aba89-6859-4a97-b822-7cb0871d2556

Base model

unsloth/Qwen2-1.5B-Instruct

Finetuned

(7)

this model