Instructions to use if001/llama2_ja_small_instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use if001/llama2_ja_small_instruct with Transformers:

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

pipe = pipeline("text-generation", model="if001/llama2_ja_small_instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("if001/llama2_ja_small_instruct")
model = AutoModelForCausalLM.from_pretrained("if001/llama2_ja_small_instruct", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use if001/llama2_ja_small_instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "if001/llama2_ja_small_instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "if001/llama2_ja_small_instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/if001/llama2_ja_small_instruct

SGLang

How to use if001/llama2_ja_small_instruct 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 "if001/llama2_ja_small_instruct" \
    --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": "if001/llama2_ja_small_instruct",
		"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 "if001/llama2_ja_small_instruct" \
        --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": "if001/llama2_ja_small_instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use if001/llama2_ja_small_instruct with Docker Model Runner:
```
docker model run hf.co/if001/llama2_ja_small_instruct
```

if001 commited on Oct 23, 2023

Commit

09a913c

1 Parent(s): 1778b45

fix

Browse files

Files changed (1) hide show

README.md +9 -0

README.md CHANGED Viewed

@@ -5,8 +5,17 @@ language:
   - en
 pipeline_tag: text-generation
 inference: false
 tags:
   - llama-2
 ---
 日本語でtrainingしたllama2をinstruction用のデータセットでsftしたものになります

   - en
 pipeline_tag: text-generation
 inference: false
+datasets:
+  - kunishou/databricks-dolly-15k-ja
+  - kunishou/oasst1-89k-ja
 tags:
   - llama-2
+  - ja
+  - japanese
+  - text-generation
+  - lm
+  - nlp
+  - conversational
 ---
 日本語でtrainingしたllama2をinstruction用のデータセットでsftしたものになります