Instructions to use miya-99999/matsuo_llm_exp02 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use miya-99999/matsuo_llm_exp02 with Transformers:

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

pipe = pipeline("text-generation", model="miya-99999/matsuo_llm_exp02")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("miya-99999/matsuo_llm_exp02")
model = AutoModelForCausalLM.from_pretrained("miya-99999/matsuo_llm_exp02", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use miya-99999/matsuo_llm_exp02 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "miya-99999/matsuo_llm_exp02"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "miya-99999/matsuo_llm_exp02",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/miya-99999/matsuo_llm_exp02

SGLang

How to use miya-99999/matsuo_llm_exp02 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 "miya-99999/matsuo_llm_exp02" \
    --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": "miya-99999/matsuo_llm_exp02",
		"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 "miya-99999/matsuo_llm_exp02" \
        --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": "miya-99999/matsuo_llm_exp02",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use miya-99999/matsuo_llm_exp02 with Docker Model Runner:
```
docker model run hf.co/miya-99999/matsuo_llm_exp02
```

Model Description

このモデルは東京大学松尾・岩澤研究室のLLM講座2024の課題のために作られたものです。

Base Model type: google/gemma-2-9b
Language(s) (NLP): Main languages are English, Japanese
License: This model is licensed under Gemma

Uses

model_name = "miya-99999/matsuo_llm_exp002"
device = "cuda" if torch.cuda.is_available() else "cpu"
tokenizer = AutoTokenizer.from_pretrained(model_name, token=HF_TOKEN)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    device_map=device,
    token=HF_TOKEN,
    torch_dtype=torch.bfloat16,
    use_cache=True
)

chat = [
  {"role": "user", "content": "こんにちは。いい天気ですね。"},
]

tokenized_input = tokenizer.apply_chat_template(chat, add_generation_prompt=True, tokenize=True, return_tensors="pt").to(model.device)
with torch.no_grad():
    outputs = model.generate(
        tokenized_input,
        max_new_tokens=512,
        do_sample=False,
        repetition_penalty=1.2,
        pad_token_id=tokenizer.pad_token_id,
        eos_token_id=[1,107],
    )[0]
output = tokenizer.decode(outputs[tokenized_input.size(1):], skip_special_tokens=True)

Fine-tuning Data

Downloads last month: 14

Safetensors

Model size

9B params

Tensor type

BF16

Collection including miya-99999/matsuo_llm_exp02

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Collection

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