Instructions to use Jahnviy/mybuddy10 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Jahnviy/mybuddy10 with Transformers:

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

pipe = pipeline("text-generation", model="Jahnviy/mybuddy10")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Jahnviy/mybuddy10")
model = AutoModelForCausalLM.from_pretrained("Jahnviy/mybuddy10", 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 Jahnviy/mybuddy10 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Jahnviy/mybuddy10"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Jahnviy/mybuddy10",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Jahnviy/mybuddy10

SGLang

How to use Jahnviy/mybuddy10 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 "Jahnviy/mybuddy10" \
    --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": "Jahnviy/mybuddy10",
		"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 "Jahnviy/mybuddy10" \
        --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": "Jahnviy/mybuddy10",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Jahnviy/mybuddy10 with Docker Model Runner:
```
docker model run hf.co/Jahnviy/mybuddy10
```

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Jahnviy/mybuddy10

This repo contains the full merged model for offline app handoff.

What is in this repo

The merged full model at the repo root
Tokenizer files at the repo root
The exact chat UI script copied from:
- /workspace/chat_tutu_continued_apr_ui.py
Optional adapter artifact
Optional training metadata

Main artifact for the app developer

Use the repo root as the model folder.

Included exact local chat script

chat_tutu_continued_apr_ui.py

Load directly from Hugging Face

from transformers import AutoTokenizer, AutoModelForCausalLM

repo_id = "Jahnviy/mybuddy10"

tokenizer = AutoTokenizer.from_pretrained(repo_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(repo_id, trust_remote_code=True)

Load from a local offline copy

from transformers import AutoTokenizer, AutoModelForCausalLM

local_path = "/path/to/local/mybuddy10"

tokenizer = AutoTokenizer.from_pretrained(local_path, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(local_path, trust_remote_code=True)

Downloads last month: -

Safetensors

Model size

3B params

Tensor type

BF16