Instructions to use jeradf/opt-125m-eou-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jeradf/opt-125m-eou-4bit with Transformers:

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

pipe = pipeline("text-generation", model="jeradf/opt-125m-eou-4bit")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("jeradf/opt-125m-eou-4bit")
model = AutoModelForCausalLM.from_pretrained("jeradf/opt-125m-eou-4bit", 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 jeradf/opt-125m-eou-4bit with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jeradf/opt-125m-eou-4bit"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jeradf/opt-125m-eou-4bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/jeradf/opt-125m-eou-4bit

SGLang

How to use jeradf/opt-125m-eou-4bit 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 "jeradf/opt-125m-eou-4bit" \
    --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": "jeradf/opt-125m-eou-4bit",
		"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 "jeradf/opt-125m-eou-4bit" \
        --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": "jeradf/opt-125m-eou-4bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use jeradf/opt-125m-eou-4bit with Docker Model Runner:
```
docker model run hf.co/jeradf/opt-125m-eou-4bit
```

opt-125m-eou-4bit / ort_config.json

jeradf

Upload ort_config.json with huggingface_hub

db59602 verified almost 2 years ago

Raw

History Blame Contribute Delete

763 Bytes

	{
	"one_external_file": true,
	"opset": null,
	"optimization": {},
	"quantization": {
	"activations_dtype": "QUInt8",
	"activations_symmetric": false,
	"format": "QOperator",
	"is_static": false,
	"mode": "IntegerOps",
	"nodes_to_exclude": [],
	"nodes_to_quantize": [],
	"operators_to_quantize": [
	"Conv",
	"MatMul",
	"Attention",
	"LSTM",
	"Gather",
	"Transpose",
	"EmbedLayerNormalization"
	],
	"per_channel": false,
	"qdq_add_pair_to_weight": false,
	"qdq_dedicated_pair": false,
	"qdq_op_type_per_channel_support_to_axis": {
	"MatMul": 1
	},
	"reduce_range": false,
	"weights_dtype": "QInt8",
	"weights_symmetric": true
	},
	"use_external_data_format": false
	}