Instructions to use theminji/TinyLlama-v2ray with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use theminji/TinyLlama-v2ray with Transformers:

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

pipe = pipeline("text-generation", model="theminji/TinyLlama-v2ray")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("theminji/TinyLlama-v2ray")
model = AutoModelForCausalLM.from_pretrained("theminji/TinyLlama-v2ray", 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 theminji/TinyLlama-v2ray with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "theminji/TinyLlama-v2ray"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "theminji/TinyLlama-v2ray",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/theminji/TinyLlama-v2ray

SGLang

How to use theminji/TinyLlama-v2ray 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 "theminji/TinyLlama-v2ray" \
    --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": "theminji/TinyLlama-v2ray",
		"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 "theminji/TinyLlama-v2ray" \
        --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": "theminji/TinyLlama-v2ray",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use theminji/TinyLlama-v2ray with Docker Model Runner:
```
docker model run hf.co/theminji/TinyLlama-v2ray
```

TinyLlama-v2ray / README.md

theminji

Update README.md

5869843 verified about 1 year ago

preview code

Raw

History Blame Contribute Delete

2.45 kB

metadata

license: apache-2.0
base_model: TinyLlama/TinyLlama-1.1B-Chat-v0.6
tags:
  - trl
  - sft
  - generated_from_trainer
model-index:
  - name: TinyLlama-v2ray
    results: []
datasets:
  - theminji/v2ray
library_name: transformers
widget:
  - text: |-
      <|im_start|>user
      Who are you?<|im_end|>
      <|im_start|>assistant
    example_title: First Example
  - text: |-
      <|im_start|>user
      how much do you goon?<|im_end|>
      <|im_start|>assistant
    example_title: Second Example

TinyLlama-v2ray

This model is a fine-tuned version of TinyLlama/TinyLlama-1.1B-Chat-v0.6 on the theminji/v2ray dataset.

Model description

Prompt format is as follows:

<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant

The model is intended to mimic the behavior of v2ray, so results will most likely be nonsensical or gibberish.

Example Usage

import torch
from transformers import pipeline, AutoTokenizer
import re
tokenizer = AutoTokenizer.from_pretrained("theminji/TinyLlama-v2ray")
pipe = pipeline("text-generation", model="theminji/TinyLlama-v2ray", torch_dtype=torch.bfloat16, device_map="auto")

def formatted_prompt(prompt)-> str:
    return f"<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant"

def extract_text(text):
    pattern = r'v2ray\n(.*?)(?=<\|im_end\|>)'
    match = re.search(pattern, text, re.DOTALL)
    if match:
        return f"Output: {match.group(1)}"
    else:
        return "No match found"
prompt = 'what are your thoughts on ccp'
outputs = pipe(formatted_prompt(prompt), max_new_tokens=50, do_sample=True, temperature=0.9)
if outputs and "generated_text" in outputs[0]:
    text = extract_text(outputs[0]["generated_text"])
    print(f"Prompt: {prompt}")
    print("")
    print(text)
else:
    print("No output or unexpected structure")

#Prompt: what are ur thoughts on ccp
#
#Output: <Re: insaneness> you are a ccp

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.002
train_batch_size: 1
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 32
total_train_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
training_steps: 1000
mixed_precision_training: Native AMP

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu121
Datasets 2.16.0
Tokenizers 0.15.0