Instructions to use tjcrims0n/p5-ann with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tjcrims0n/p5-ann with Transformers:

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

pipe = pipeline("text-generation", model="tjcrims0n/p5-ann")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("tjcrims0n/p5-ann")
model = AutoModelForCausalLM.from_pretrained("tjcrims0n/p5-ann")
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 tjcrims0n/p5-ann with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tjcrims0n/p5-ann"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tjcrims0n/p5-ann",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tjcrims0n/p5-ann

SGLang

How to use tjcrims0n/p5-ann 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 "tjcrims0n/p5-ann" \
    --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": "tjcrims0n/p5-ann",
		"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 "tjcrims0n/p5-ann" \
        --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": "tjcrims0n/p5-ann",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use tjcrims0n/p5-ann with Docker Model Runner:
```
docker model run hf.co/tjcrims0n/p5-ann
```

p5-ann

Full Transformers safetensors model based on microsoft/Phi-4-mini-instruct.

Notes

This repo is a merged full model export, not an adapter-only repo.
annihilate_merge_metadata.json records the source adapter and merge details.
The repo intentionally uses the built-in Transformers Phi3/Phi loader path. Stale custom remote-code files were removed so trust_remote_code=True and False both resolve to the built-in loader.
Generation should stop on <|end|> / <|endoftext|>; generation_config.json contains both stop IDs.

Quick Load

from transformers import AutoModelForCausalLM, AutoTokenizer

repo = "tjcrims0n/p5-ann"
tokenizer = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(repo, device_map="auto")

messages = [
    {"role": "system", "content": "Answer directly and concisely."},
    {"role": "user", "content": "Hello."},
]
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True).to(model.device)
outputs = model.generate(
    inputs,
    max_new_tokens=80,
    eos_token_id=model.generation_config.eos_token_id,
    pad_token_id=model.generation_config.pad_token_id,
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Smoke Test Notes

Local verification passed for greeting, arithmetic, and code prompts after removing stale remote-code hooks from config.json.

Downloads last month: 48

Safetensors

Model size

4B params

Tensor type

F16

Model tree for tjcrims0n/p5-ann

Base model

microsoft/Phi-4-mini-instruct

Finetuned

(101)

this model