Text Generation
Transformers
Safetensors
English
gpt2
causal-lm
from-scratch
tiny-model
educational
text-generation-inference
Instructions to use ARotting/snip-0.4m-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ARotting/snip-0.4m-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ARotting/snip-0.4m-base")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("ARotting/snip-0.4m-base") model = AutoModelForCausalLM.from_pretrained("ARotting/snip-0.4m-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use ARotting/snip-0.4m-base with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ARotting/snip-0.4m-base" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ARotting/snip-0.4m-base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/ARotting/snip-0.4m-base
- SGLang
How to use ARotting/snip-0.4m-base 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 "ARotting/snip-0.4m-base" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ARotting/snip-0.4m-base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "ARotting/snip-0.4m-base" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ARotting/snip-0.4m-base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use ARotting/snip-0.4m-base with Docker Model Runner:
docker model run hf.co/ARotting/snip-0.4m-base
File size: 1,911 Bytes
24ebd71 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 | from __future__ import annotations
from collections import Counter
def build_logic_tasks() -> tuple[list[dict], list[dict]]:
examples: list[dict] = []
for number in range(100):
examples.append(
{
"task": "parity",
"prompt": (
"Output 1 if the number is even, otherwise output 0. "
f"Number: {number}. Answer:"
),
"answer": int(number % 2 == 0),
"key": number,
}
)
for left in range(20):
for right in range(20):
if left == right:
continue
examples.append(
{
"task": "less_than",
"prompt": (
"Output 1 if the first number is less than the second, "
f"otherwise output 0. First: {left}. Second: {right}. Answer:"
),
"answer": int(left < right),
"key": left * 23 + right * 7,
}
)
examples.append(
{
"task": "sum_at_least_20",
"prompt": (
"Output 1 if the sum is at least 20, otherwise output 0. "
f"Numbers: {left} and {right}. Answer:"
),
"answer": int(left + right >= 20),
"key": left * 31 + right * 11,
}
)
train = [
example for example in examples if (example["key"] + len(example["task"])) % 5 != 0
]
evaluation = [
example for example in examples if (example["key"] + len(example["task"])) % 5 == 0
]
return train, evaluation
def class_counts(examples: list[dict]) -> dict[int, int]:
return dict(Counter(example["answer"] for example in examples))
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