Instructions to use docto/Docto-Bot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use docto/Docto-Bot with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="docto/Docto-Bot")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("docto/Docto-Bot") model = AutoModelForCausalLM.from_pretrained("docto/Docto-Bot", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use docto/Docto-Bot with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "docto/Docto-Bot" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "docto/Docto-Bot", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/docto/Docto-Bot
- SGLang
How to use docto/Docto-Bot 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 "docto/Docto-Bot" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "docto/Docto-Bot", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "docto/Docto-Bot" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "docto/Docto-Bot", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use docto/Docto-Bot with Docker Model Runner:
docker model run hf.co/docto/Docto-Bot
Commit ·
863ee30
1
Parent(s): fd608d5
Update README.md
Browse files
README.md
CHANGED
|
@@ -9,10 +9,12 @@ pip install -U transformers
|
|
| 9 |
```
|
| 10 |
|
| 11 |
```python
|
|
|
|
| 12 |
from transformers import AutoTokenizer, AutoModelForCausalLM
|
| 13 |
|
| 14 |
tokenizer = AutoTokenizer.from_pretrained("docto/Docto-Bot")
|
| 15 |
model = AutoModelForCausalLM.from_pretrained("docto/Docto-Bot")
|
|
|
|
| 16 |
|
| 17 |
prompt_text = 'Question: I am having fever\nAnswer:'
|
| 18 |
encoded_prompt = tokenizer.encode(prompt_text,
|
|
@@ -30,5 +32,5 @@ output_sequences = model.generate(
|
|
| 30 |
)
|
| 31 |
result = tokenizer.decode(random.choice(output_sequences))
|
| 32 |
result = result[result.index("Answer: "):result.index(special_token)]
|
| 33 |
-
print(result[
|
| 34 |
```
|
|
|
|
| 9 |
```
|
| 10 |
|
| 11 |
```python
|
| 12 |
+
import random
|
| 13 |
from transformers import AutoTokenizer, AutoModelForCausalLM
|
| 14 |
|
| 15 |
tokenizer = AutoTokenizer.from_pretrained("docto/Docto-Bot")
|
| 16 |
model = AutoModelForCausalLM.from_pretrained("docto/Docto-Bot")
|
| 17 |
+
special_token = '<|endoftext|>'
|
| 18 |
|
| 19 |
prompt_text = 'Question: I am having fever\nAnswer:'
|
| 20 |
encoded_prompt = tokenizer.encode(prompt_text,
|
|
|
|
| 32 |
)
|
| 33 |
result = tokenizer.decode(random.choice(output_sequences))
|
| 34 |
result = result[result.index("Answer: "):result.index(special_token)]
|
| 35 |
+
print(result[8:])
|
| 36 |
```
|