Instructions to use taoki/Mistral-7B-Instruct-v0.3_lora_jmultiwoz-dolly-amenokaku-alpaca_jp_python with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use taoki/Mistral-7B-Instruct-v0.3_lora_jmultiwoz-dolly-amenokaku-alpaca_jp_python with Transformers:

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

pipe = pipeline("text-generation", model="taoki/Mistral-7B-Instruct-v0.3_lora_jmultiwoz-dolly-amenokaku-alpaca_jp_python")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("taoki/Mistral-7B-Instruct-v0.3_lora_jmultiwoz-dolly-amenokaku-alpaca_jp_python")
model = AutoModelForCausalLM.from_pretrained("taoki/Mistral-7B-Instruct-v0.3_lora_jmultiwoz-dolly-amenokaku-alpaca_jp_python")
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 taoki/Mistral-7B-Instruct-v0.3_lora_jmultiwoz-dolly-amenokaku-alpaca_jp_python with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "taoki/Mistral-7B-Instruct-v0.3_lora_jmultiwoz-dolly-amenokaku-alpaca_jp_python"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "taoki/Mistral-7B-Instruct-v0.3_lora_jmultiwoz-dolly-amenokaku-alpaca_jp_python",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/taoki/Mistral-7B-Instruct-v0.3_lora_jmultiwoz-dolly-amenokaku-alpaca_jp_python

SGLang

How to use taoki/Mistral-7B-Instruct-v0.3_lora_jmultiwoz-dolly-amenokaku-alpaca_jp_python 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 "taoki/Mistral-7B-Instruct-v0.3_lora_jmultiwoz-dolly-amenokaku-alpaca_jp_python" \
    --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": "taoki/Mistral-7B-Instruct-v0.3_lora_jmultiwoz-dolly-amenokaku-alpaca_jp_python",
		"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 "taoki/Mistral-7B-Instruct-v0.3_lora_jmultiwoz-dolly-amenokaku-alpaca_jp_python" \
        --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": "taoki/Mistral-7B-Instruct-v0.3_lora_jmultiwoz-dolly-amenokaku-alpaca_jp_python",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use taoki/Mistral-7B-Instruct-v0.3_lora_jmultiwoz-dolly-amenokaku-alpaca_jp_python with Docker Model Runner:
```
docker model run hf.co/taoki/Mistral-7B-Instruct-v0.3_lora_jmultiwoz-dolly-amenokaku-alpaca_jp_python
```

Uploaded model

Developed by: taoki
License: apache-2.0
Finetuned from model : mistralai/Mistral-7B-Instruct-v0.3

Usage

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

tokenizer = AutoTokenizer.from_pretrained(
  "taoki/Mistral-7B-Instruct-v0.3_lora_jmultiwoz-dolly-amenokaku-alpaca_jp_python"
)
model = AutoModelForCausalLM.from_pretrained(
  "taoki/Mistral-7B-Instruct-v0.3_lora_jmultiwoz-dolly-amenokaku-alpaca_jp_python"
)

if torch.cuda.is_available():
    model = model.to("cuda")

prompt="""[INST] OpenCVを用いて定点カメラから画像を保存するコードを示してください。 [/INST]"""

input_ids = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(
    **input_ids,
    max_new_tokens=512,
    do_sample=True,
    top_p=0.9,
    temperature=0.2,
    repetition_penalty=1.1,
)
print(tokenizer.decode(outputs[0]))

Output

<s>[INST] OpenCVを用いて定点カメラから画像を保存するコードを示してください。 [/INST]```python
import cv2

# カメラの設定
cap = cv2.VideoCapture(0)

# フレーム数
frame_count = 10

# 画像の保存
for i in range(frame_count):
    # フレームの取得
    ret, frame = cap.read()

    # 画像の保存
    cv2.imwrite('image_{}.jpg'.format(i), frame)

# カメラの終了
cap.release()
```</s>

Downloads last month: 9

Safetensors

Model size

7B params

Tensor type

BF16

Model tree for taoki/Mistral-7B-Instruct-v0.3_lora_jmultiwoz-dolly-amenokaku-alpaca_jp_python

Base model

mistralai/Mistral-7B-v0.3

Finetuned

mistralai/Mistral-7B-Instruct-v0.3

Finetuned

(500)

this model

taoki
/

Mistral-7B-Instruct-v0.3_lora_jmultiwoz-dolly-amenokaku-alpaca_jp_python

Uploaded model

Usage

Output

Model tree for taoki/Mistral-7B-Instruct-v0.3_lora_jmultiwoz-dolly-amenokaku-alpaca_jp_python

Datasets used to train taoki/Mistral-7B-Instruct-v0.3_lora_jmultiwoz-dolly-amenokaku-alpaca_jp_python