Instructions to use CoolSpring/Qwen2-0.5B-Abyme with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use CoolSpring/Qwen2-0.5B-Abyme with Transformers:

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

pipe = pipeline("text-generation", model="CoolSpring/Qwen2-0.5B-Abyme")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("CoolSpring/Qwen2-0.5B-Abyme")
model = AutoModelForCausalLM.from_pretrained("CoolSpring/Qwen2-0.5B-Abyme", 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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use CoolSpring/Qwen2-0.5B-Abyme with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "CoolSpring/Qwen2-0.5B-Abyme"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CoolSpring/Qwen2-0.5B-Abyme",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/CoolSpring/Qwen2-0.5B-Abyme

SGLang

How to use CoolSpring/Qwen2-0.5B-Abyme 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 "CoolSpring/Qwen2-0.5B-Abyme" \
    --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": "CoolSpring/Qwen2-0.5B-Abyme",
		"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 "CoolSpring/Qwen2-0.5B-Abyme" \
        --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": "CoolSpring/Qwen2-0.5B-Abyme",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use CoolSpring/Qwen2-0.5B-Abyme with Docker Model Runner:
```
docker model run hf.co/CoolSpring/Qwen2-0.5B-Abyme
```

Qwen2-0.5B-Abyme / README.md

CoolSpring

End of training

ee31535 verified about 2 years ago

3.27 kB

license: apache-2.0
base_model: Qwen/Qwen2-0.5B
tags:
  - axolotl
  - generated_from_trainer
model-index:
  - name: Qwen2-0.5B-Abyme
    results: []

See axolotl config

axolotl version: 0.4.1

adapter: null
base_model: Qwen/Qwen2-0.5B
bf16: auto
chat_template: chatml
dataset_prepared_path: ./data/last_run_prepared
datasets:
- path: Magpie-Align/Magpie-Qwen2-Pro-300K-Filtered
  type: sharegpt
deepspeed: null
early_stopping_patience: null
eval_sample_packing: true
evals_per_epoch: 4
flash_attention: true
fp16: null
fsdp: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
group_by_length: false
hf_use_auth_token: true
hub_model_id: CoolSpring/Qwen2-0.5B-Abyme
learning_rate: 2e-5
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lr_scheduler: cosine
micro_batch_size: 4
num_epochs: 1
optimizer: adamw_torch
output_dir: ./outputs/out
pad_to_sequence_len: true
resize_token_embeddings_to_32x: true
resume_from_checkpoint: null
sample_packing: true
saves_per_epoch: 1
sequence_len: 4096
tf32: true
tokens:
- <|im_start|>
- <|im_end|>
train_on_inputs: false
val_set_size: 0.05
wandb_entity: null
wandb_log_model: null
wandb_name: Qwen2-0.5B-Abyme
wandb_project: Qwen2-0.5B-Magpie-Qwen2-Pro-300K-Filtered
wandb_watch: null
warmup_steps: 100
weight_decay: null
xformers_attention: null

Qwen2-0.5B-Abyme

This model is a fine-tuned version of Qwen/Qwen2-0.5B on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.8229

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: 4
eval_batch_size: 4
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 16
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 100
num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss
0.9947	0.0004	1	0.9683
0.8385	0.2501	597	0.8338
0.7636	0.5002	1194	0.8249
0.8124	0.7502	1791	0.8229

Framework versions

Transformers 4.42.3
Pytorch 2.3.1+cu121
Datasets 2.19.1
Tokenizers 0.19.1