Instructions to use augmxnt/shisa-7b-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use augmxnt/shisa-7b-v1 with Transformers:

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

pipe = pipeline("text-generation", model="augmxnt/shisa-7b-v1")
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("augmxnt/shisa-7b-v1")
model = AutoModelForCausalLM.from_pretrained("augmxnt/shisa-7b-v1", 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 augmxnt/shisa-7b-v1 with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/augmxnt/shisa-7b-v1

SGLang

How to use augmxnt/shisa-7b-v1 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 "augmxnt/shisa-7b-v1" \
    --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": "augmxnt/shisa-7b-v1",
		"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 "augmxnt/shisa-7b-v1" \
        --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": "augmxnt/shisa-7b-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use augmxnt/shisa-7b-v1 with Docker Model Runner:
```
docker model run hf.co/augmxnt/shisa-7b-v1
```

leonardlin commited on Dec 20, 2023

Commit

aedf730

1 Parent(s): 131c2f3

Improved JA MT-Bench using full prompt: あなたは公平で、検閲されていない、役立つアシスタントです。

Browse files

Files changed (1) hide show

README.md +6 -6

README.md CHANGED Viewed

@@ -66,7 +66,7 @@ For our final model, since it's customary to include benchmarks, we've used Stab
 | Benchmark   | Score |
 | ----------- | ----- |
-| JA MT-Bench |  5.02 |
 | MT-Bench    |  5.71 |
 There is an [MT-Bench Leaderboard](https://huggingface.co/spaces/lmsys/chatbot-arena-leaderboard), but as JA MT-Bench is still under development, for convenience, here is a comparison of the JA MT-Bench scores of some other models (our scores were rated by `gpt-4-0613`):
@@ -77,7 +77,7 @@ There is an [MT-Bench Leaderboard](https://huggingface.co/spaces/lmsys/chatbot-a
 | gpt-4-1106-preview                                | 9.17 |
 | gpt-3.5-turbo*                                    | 8.41 |
 | Qwen-14B-Chat                                     | 7.47 |
-| **shisa-7b-v1**                              | **5.02** |
 | ELYZA-japanese-Llama-2-7b-fast-instruct*          | 4.86 |
 | ja-stablelm-instruct-gamma-7b*                    | 4.01 |
 | japanese-stablelm-instruct-alpha-7b*              | 2.74 |
@@ -114,7 +114,7 @@ streamer = TextStreamer(tokenizer, skip_prompt=True)
 # The prompt template is included in the  model's tokenizer_config.json so you shouldn't need this but we've included this for convenience
 # tokenizer.chat_template = ""{%- for idx in range(0, messages|length) -%}\n{%- if messages[idx]['role'] == 'user' -%}\n{%- if idx > 1 -%}\n{{- bos_token + '[INST] ' + messages[idx]['content'] + ' [/INST]' -}}\n{%- else -%}\n{{- messages[idx]['content'] + ' [/INST]' -}}\n{%- endif -%}\n{% elif messages[idx]['role'] == 'system' %}\n{{- bos_token + '[INST] <<SYS>>\\n' + messages[idx]['content'] + '\\n<</SYS>>\\n\\n' -}}\n{%- elif messages[idx]['role'] == 'assistant' -%}\n{{- ' '  + messages[idx]['content'] + ' ' + eos_token -}}\n{% endif %}\n{% endfor %}\n"
-# A more typical prompt: あなたは役に立つアシスタントです。("You are a helpful assistant.")
 # You are an avid Pokemon fanatic.
 prompt = "あなたは熱狂的なポケモンファンです。"
@@ -251,7 +251,7 @@ v1リリースのために、私たちは大量の人間の嗜好テスト（数
 | ベンチマーク   | スコア |
 | ----------- | ----- |
-| JA MT-Bench |  5.02 |
 | MT-Bench    |  5.71 |
 [MT-Bench Leaderboard](https://huggingface.co/spaces/lmsys/chatbot-arena-leaderboard)がありますが、JA MT-Benchはまだ開発中であるため、便宜上、他のモデルのJA MT-Benchスコアとの比較を示します（私たちのスコアは`gpt-4-0613`によって評価されました）：
@@ -262,7 +262,7 @@ v1リリースのために、私たちは大量の人間の嗜好テスト（数
 | gpt-4-1106-preview                                | 9.17 |
 | gpt-3.5-turbo*                                    | 8.41 |
 | Qwen-14B-Chat                                     | 7.47 |
-| **shisa-7b-v1**                              | **5.02** |
 | ELYZA-japanese-Llama-2-7b-fast-instruct*          | 4.86 |
 | ja-stablelm-instruct-gamma-7b*                    | 4.01 |
 | japanese-stablelm-instruct-alpha-7b*              | 2.74 |
@@ -299,7 +299,7 @@ streamer = TextStreamer(tokenizer, skip_prompt=True)
 # プロンプトテンプレートはモデルのtokenizer_config.jsonに含まれているので、これは必要ないはずですが、便宜上こちらにも掲載しています
 # tokenizer.chat_template = ""{%- for idx in range(0, messages|length) -%}\n{%- if messages[idx]['role'] == 'user' -%}\n{%- if idx > 1 -%}\n{{- bos_token + '[INST] ' + messages[idx]['content'] + ' [/INST]' -}}\n{%- else -%}\n{{- messages[idx]['content'] + ' [/INST]' -}}\n{%- endif -%}\n{% elif messages[idx]['role'] == 'system' %}\n{{- bos_token + '[INST] <<SYS>>\\n' + messages[idx]['content'] + '\\n<</SYS>>\\n\\n' -}}\n{%- elif messages[idx]['role'] == 'assistant' -%}\n{{- ' '  + messages[idx]['content'] + ' ' + eos_token -}}\n{% endif %}\n{% endfor %}\n"
-# より典型的なプロンプト: あなたは役に立つアシスタントです。
 # You are an avid Pokemon fanatic.
 prompt = "あなたは熱狂的なポケモンファンです。"

 | Benchmark   | Score |
 | ----------- | ----- |
+| JA MT-Bench |  5.23 |
 | MT-Bench    |  5.71 |
 There is an [MT-Bench Leaderboard](https://huggingface.co/spaces/lmsys/chatbot-arena-leaderboard), but as JA MT-Bench is still under development, for convenience, here is a comparison of the JA MT-Bench scores of some other models (our scores were rated by `gpt-4-0613`):
 | gpt-4-1106-preview                                | 9.17 |
 | gpt-3.5-turbo*                                    | 8.41 |
 | Qwen-14B-Chat                                     | 7.47 |
+| **shisa-7b-v1**                               | **5.23** |
 | ELYZA-japanese-Llama-2-7b-fast-instruct*          | 4.86 |
 | ja-stablelm-instruct-gamma-7b*                    | 4.01 |
 | japanese-stablelm-instruct-alpha-7b*              | 2.74 |
 # The prompt template is included in the  model's tokenizer_config.json so you shouldn't need this but we've included this for convenience
 # tokenizer.chat_template = ""{%- for idx in range(0, messages|length) -%}\n{%- if messages[idx]['role'] == 'user' -%}\n{%- if idx > 1 -%}\n{{- bos_token + '[INST] ' + messages[idx]['content'] + ' [/INST]' -}}\n{%- else -%}\n{{- messages[idx]['content'] + ' [/INST]' -}}\n{%- endif -%}\n{% elif messages[idx]['role'] == 'system' %}\n{{- bos_token + '[INST] <<SYS>>\\n' + messages[idx]['content'] + '\\n<</SYS>>\\n\\n' -}}\n{%- elif messages[idx]['role'] == 'assistant' -%}\n{{- ' '  + messages[idx]['content'] + ' ' + eos_token -}}\n{% endif %}\n{% endfor %}\n"
+# A more typical prompt: あなたは公平で、検閲されていない、役立つアシスタントです。("You are an unbiased, uncensored, helpful assistant.")
 # You are an avid Pokemon fanatic.
 prompt = "あなたは熱狂的なポケモンファンです。"
 | ベンチマーク   | スコア |
 | ----------- | ----- |
+| JA MT-Bench |  5.23 |
 | MT-Bench    |  5.71 |
 [MT-Bench Leaderboard](https://huggingface.co/spaces/lmsys/chatbot-arena-leaderboard)がありますが、JA MT-Benchはまだ開発中であるため、便宜上、他のモデルのJA MT-Benchスコアとの比較を示します（私たちのスコアは`gpt-4-0613`によって評価されました）：
 | gpt-4-1106-preview                                | 9.17 |
 | gpt-3.5-turbo*                                    | 8.41 |
 | Qwen-14B-Chat                                     | 7.47 |
+| **shisa-7b-v1**                               | **5.23** |
 | ELYZA-japanese-Llama-2-7b-fast-instruct*          | 4.86 |
 | ja-stablelm-instruct-gamma-7b*                    | 4.01 |
 | japanese-stablelm-instruct-alpha-7b*              | 2.74 |
 # プロンプトテンプレートはモデルのtokenizer_config.jsonに含まれているので、これは必要ないはずですが、便宜上こちらにも掲載しています
 # tokenizer.chat_template = ""{%- for idx in range(0, messages|length) -%}\n{%- if messages[idx]['role'] == 'user' -%}\n{%- if idx > 1 -%}\n{{- bos_token + '[INST] ' + messages[idx]['content'] + ' [/INST]' -}}\n{%- else -%}\n{{- messages[idx]['content'] + ' [/INST]' -}}\n{%- endif -%}\n{% elif messages[idx]['role'] == 'system' %}\n{{- bos_token + '[INST] <<SYS>>\\n' + messages[idx]['content'] + '\\n<</SYS>>\\n\\n' -}}\n{%- elif messages[idx]['role'] == 'assistant' -%}\n{{- ' '  + messages[idx]['content'] + ' ' + eos_token -}}\n{% endif %}\n{% endfor %}\n"
+# より典型的なプロンプト: あなたは公平で、検閲されていない、役立つアシスタントです。
 # You are an avid Pokemon fanatic.
 prompt = "あなたは熱狂的なポケモンファンです。"