kunishou/amenokaku-code-instruct
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How to use taoki/deepseek-coder-7b-instruct-v1.5-qlora-amenokaku-code with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="taoki/deepseek-coder-7b-instruct-v1.5-qlora-amenokaku-code")
messages = [
{"role": "user", "content": "Who are you?"},
]
pipe(messages) # Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("taoki/deepseek-coder-7b-instruct-v1.5-qlora-amenokaku-code")
model = AutoModelForCausalLM.from_pretrained("taoki/deepseek-coder-7b-instruct-v1.5-qlora-amenokaku-code")
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]:]))How to use taoki/deepseek-coder-7b-instruct-v1.5-qlora-amenokaku-code with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "taoki/deepseek-coder-7b-instruct-v1.5-qlora-amenokaku-code"
# 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/deepseek-coder-7b-instruct-v1.5-qlora-amenokaku-code",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}'docker model run hf.co/taoki/deepseek-coder-7b-instruct-v1.5-qlora-amenokaku-code
How to use taoki/deepseek-coder-7b-instruct-v1.5-qlora-amenokaku-code with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "taoki/deepseek-coder-7b-instruct-v1.5-qlora-amenokaku-code" \
--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/deepseek-coder-7b-instruct-v1.5-qlora-amenokaku-code",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}'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/deepseek-coder-7b-instruct-v1.5-qlora-amenokaku-code" \
--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/deepseek-coder-7b-instruct-v1.5-qlora-amenokaku-code",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}'How to use taoki/deepseek-coder-7b-instruct-v1.5-qlora-amenokaku-code with Unsloth Studio:
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for taoki/deepseek-coder-7b-instruct-v1.5-qlora-amenokaku-code to start chatting
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for taoki/deepseek-coder-7b-instruct-v1.5-qlora-amenokaku-code to start chatting
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for taoki/deepseek-coder-7b-instruct-v1.5-qlora-amenokaku-code to start chatting
pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
model_name="taoki/deepseek-coder-7b-instruct-v1.5-qlora-amenokaku-code",
max_seq_length=2048,
)How to use taoki/deepseek-coder-7b-instruct-v1.5-qlora-amenokaku-code with Docker Model Runner:
docker model run hf.co/taoki/deepseek-coder-7b-instruct-v1.5-qlora-amenokaku-code
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
tokenizer = AutoTokenizer.from_pretrained(
"taoki/deepseek-coder-7b-instruct-v1.5-qlora-amenokaku-code"
)
model = AutoModelForCausalLM.from_pretrained(
"taoki/deepseek-coder-7b-instruct-v1.5-qlora-amenokaku-code"
)
if torch.cuda.is_available():
model = model.to("cuda")
prompt="""You are an AI programming assistant, utilizing the DeepSeek Coder model, developed by DeepSeek Company, and you only answer questions related to computer science. For politically sensitive questions, security and privacy issues, and other non-computer science questions, you will refuse to answer.
### Instruction:
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。
### Response:
"""
input_ids = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(
**input_ids,
max_new_tokens=256,
do_sample=True,
top_p=0.9,
temperature=0.2,
repetition_penalty=1.1,
)
print(tokenizer.decode(outputs[0]))
<|begin▁of▁sentence|>You are an AI programming assistant, utilizing the DeepSeek Coder model, developed by DeepSeek Company, and you only answer questions related to computer science. For politically sensitive questions, security and privacy issues, and other non-computer science questions, you will refuse to answer.
### Instruction:
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。
### Response:
```python
import cv2
cap = cv2.VideoCapture(0) # カメラの設定
fourcc = cv2.VideoWriter_fourcc(*'XVID') # 動画の形式
out = cv2.VideoWriter('output.avi', fourcc, 20.0, (640, 480)) # 出力先、fps、解像度
while True:
ret, frame = cap.read() # 映像読み込み
if not ret: break
out.write(frame) # 書き込み
cv2.imshow('Frame', frame) # 表示
if cv2.waitKey(1) & 0xFF == ord('q'): # qで終了
break
cap.release()
cv2.destroyAllWindows()
```
<|EOT|>
This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.
Base model
deepseek-ai/deepseek-coder-7b-instruct-v1.5