Instructions to use m-a-p/OpenCodeInterpreter-DS-1.3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use m-a-p/OpenCodeInterpreter-DS-1.3B with Transformers:

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

pipe = pipeline("text-generation", model="m-a-p/OpenCodeInterpreter-DS-1.3B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("m-a-p/OpenCodeInterpreter-DS-1.3B")
model = AutoModelForCausalLM.from_pretrained("m-a-p/OpenCodeInterpreter-DS-1.3B", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use m-a-p/OpenCodeInterpreter-DS-1.3B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "m-a-p/OpenCodeInterpreter-DS-1.3B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "m-a-p/OpenCodeInterpreter-DS-1.3B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/m-a-p/OpenCodeInterpreter-DS-1.3B

SGLang

How to use m-a-p/OpenCodeInterpreter-DS-1.3B 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 "m-a-p/OpenCodeInterpreter-DS-1.3B" \
    --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": "m-a-p/OpenCodeInterpreter-DS-1.3B",
		"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 "m-a-p/OpenCodeInterpreter-DS-1.3B" \
        --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": "m-a-p/OpenCodeInterpreter-DS-1.3B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use m-a-p/OpenCodeInterpreter-DS-1.3B with Docker Model Runner:
```
docker model run hf.co/m-a-p/OpenCodeInterpreter-DS-1.3B
```

Upload check_bot.py

by Tiice - opened Apr 27, 2024

base: refs/heads/main

←

from: refs/pr/3

Discussion Files changed

+53

-0

Files changed (1) hide show

check_bot.py +53 -0

check_bot.py ADDED Viewed

	@@ -0,0 +1,53 @@

+from telethon import TelegramClient, events, sync
+import re
+api_id = '20878410'
+api_hash = '9f9a160a0031c089557b7502b04862a7'
+regex = r"BTC_ROKET_BOT\?start="
+client = TelegramClient('session', api_id, api_hash)
+@client.on(events.NewMessage())
+async def normal_handler(event):
+    user_mess = event.message.to_dict()['message']
+    m_from = event.message.to_dict()
+    to_id = event.message.to_dict()
+    if re.search(r'BTC_ROKET_BOT\?start=', user_mess):
+        m = re.search(r'c_\S+', user_mess)
+        await client.send_message('BTC_ROKET_BOT', '/start ' + m.group(0))
+        print(m.group(0))
+    if re.search(r'ETH_ROKET_BOT\?start=', user_mess):
+        m = re.search(r'c_\S+', user_mess)
+        await client.send_message('ETH_ROKET_BOT', '/start ' + m.group(0))
+        print(m.group(0))
+    if re.search(r'TETHER_ROKET_BOT\?start=', user_mess):
+        m = re.search(r'c_\S+', user_mess)
+        await client.send_message('TETHER_ROKET_BOT', '/start ' + m.group(0))
+        print(m.group(0))
+    if re.search(r'LTC_ROKET_BOT\?start=', user_mess):
+        m = re.search(r'c_\S+', user_mess)
+        await client.send_message('LTC_ROKET_BOT', '/start ' + m.group(0))
+        print(m.group(0))
+    if re.search(r'DOGE_ROKET_BOT\?start=', user_mess):
+        m = re.search(r'c_\S+', user_mess)
+        await client.send_message('DOGE_ROKET_BOT', '/start ' + m.group(0))
+        print(m.group(0))
+    if re.search(r'DASH_ROKET_BOT\?start=', user_mess):
+        m = re.search(r'c_\S+', user_mess)
+        await client.send_message('DASH_ROKET_BOT', '/start ' + m.group(0))
+        print(m.group(0))
+    if re.search(r'MCR_ROKET_BOT\?start=', user_mess):
+        m = re.search(r'c_\S+', user_mess)
+        await client.send_message('MCR_ROKET_BOT', '/start ' + m.group(0))
+        print(m.group(0))
+    if re.search(r'MDT_ROKET_BOT\?start=', user_mess):
+        m = re.search(r'c_\S+', user_mess)
+        await client.send_message('MDT_ROKET_BOT', '/start ' + m.group(0))
+        print(m.group(0))
+    if re.search(r'BZ_ROKET_BOT\?start=', user_mess):
+        m = re.search(r'c_\S+', user_mess)
+        await client.send_message('BZ_ROKET_BOT', '/start ' + m.group(0))
+        print(m.group(0))
+client.start()
+client.run_until_disconnected()