Instructions to use lldois/v101_public091_v74_ep3_tail2e6_ep02 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries
PEFT
How to use lldois/v101_public091_v74_ep3_tail2e6_ep02 with PEFT:
```
Task type is invalid.
```

How to use lldois/v101_public091_v74_ep3_tail2e6_ep02 with Transformers:

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

pipe = pipeline("text-generation", model="lldois/v101_public091_v74_ep3_tail2e6_ep02")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("lldois/v101_public091_v74_ep3_tail2e6_ep02")
model = AutoModelForCausalLM.from_pretrained("lldois/v101_public091_v74_ep3_tail2e6_ep02", 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 lldois/v101_public091_v74_ep3_tail2e6_ep02 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "lldois/v101_public091_v74_ep3_tail2e6_ep02"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lldois/v101_public091_v74_ep3_tail2e6_ep02",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/lldois/v101_public091_v74_ep3_tail2e6_ep02

SGLang

How to use lldois/v101_public091_v74_ep3_tail2e6_ep02 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 "lldois/v101_public091_v74_ep3_tail2e6_ep02" \
    --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": "lldois/v101_public091_v74_ep3_tail2e6_ep02",
		"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 "lldois/v101_public091_v74_ep3_tail2e6_ep02" \
        --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": "lldois/v101_public091_v74_ep3_tail2e6_ep02",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use lldois/v101_public091_v74_ep3_tail2e6_ep02 with Docker Model Runner:
```
docker model run hf.co/lldois/v101_public091_v74_ep3_tail2e6_ep02
```

v101_public091_v74_ep3_tail2e6_ep02

This model is a fine-tuned version of OpenOneRec/OneReason-0.8B-pretrain-competition on the exp_v101_stage2_public 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: 2e-06
train_batch_size: 1
eval_batch_size: 8
seed: 202607210
gradient_accumulation_steps: 4
total_train_batch_size: 4
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
num_epochs: 0.2

Training results

Framework versions

PEFT 0.18.1
Transformers 5.6.0
Pytorch 2.7.1+cu126
Datasets 4.0.0
Tokenizers 0.22.2

Downloads last month: -

Safetensors

Model size

0.8B params

Tensor type

BF16

Model tree for lldois/v101_public091_v74_ep3_tail2e6_ep02

Base model

OpenOneRec/OneReason-0.8B-pretrain-competition

Adapter

(73)

this model