Duplicated from jw-hf-test/jw1

mekaneeky
/

jw1

Instructions to use mekaneeky/jw1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mekaneeky/jw1 with Transformers:

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

pipe = pipeline("text-generation", model="mekaneeky/jw1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mekaneeky/jw1")
model = AutoModelForCausalLM.from_pretrained("mekaneeky/jw1")
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]:]))

Transformers.js

How to use mekaneeky/jw1 with Transformers.js:

// npm i @huggingface/transformers
import { pipeline } from '@huggingface/transformers';

// Allocate pipeline
const pipe = await pipeline('text-generation', 'mekaneeky/jw1');

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use mekaneeky/jw1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mekaneeky/jw1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mekaneeky/jw1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mekaneeky/jw1

SGLang

How to use mekaneeky/jw1 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 "mekaneeky/jw1" \
    --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": "mekaneeky/jw1",
		"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 "mekaneeky/jw1" \
        --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": "mekaneeky/jw1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use mekaneeky/jw1 with Docker Model Runner:
```
docker model run hf.co/mekaneeky/jw1
```

GPT-4 Tokenizer

A 🤗-compatible version of the GPT-4 tokenizer (adapted from openai/tiktoken). This means it can be used with Hugging Face libraries including Transformers, Tokenizers, and Transformers.js.

Example usage:

Transformers/Tokenizers

from transformers import GPT2TokenizerFast

tokenizer = GPT2TokenizerFast.from_pretrained('Xenova/gpt-4')
assert tokenizer.encode('hello world') == [15339, 1917]

Transformers.js

import { AutoTokenizer } from '@xenova/transformers';

const tokenizer = await AutoTokenizer.from_pretrained('Xenova/gpt-4');
const tokens = tokenizer.encode('hello world'); // [15339, 1917]

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Safetensors

Model size

7B params

Tensor type

BF16