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

Libraries

How to use deepparag/DumBot with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("deepparag/DumBot")
model = AutoModelForCausalLM.from_pretrained("deepparag/DumBot")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use deepparag/DumBot with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/deepparag/DumBot

SGLang

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

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

deepparag commited on Dec 17, 2021

Commit

7ca1066

1 Parent(s): d126eec

add model

Browse files

Files changed (2) hide show

config.json +5 -11
pytorch_model.bin +2 -2

config.json CHANGED Viewed

@@ -8,15 +8,14 @@
   "bos_token_id": 50256,
   "embd_pdrop": 0.1,
   "eos_token_id": 50256,
-  "gradient_checkpointing": false,
   "initializer_range": 0.02,
   "layer_norm_epsilon": 1e-05,
   "model_type": "gpt2",
   "n_ctx": 1024,
-  "n_embd": 1024,
-  "n_head": 16,
   "n_inner": null,
-  "n_layer": 24,
   "n_positions": 1024,
   "reorder_and_upcast_attn": false,
   "resid_pdrop": 0.1,
@@ -29,16 +28,11 @@
   "summary_use_proj": true,
   "task_specific_params": {
     "conversational": {
-        "max_length":200,
-        "no_repeat_ngram_size":4,
-        "do_sample":true,
-        "top_k":100,
-        "top_p":0.7,
-        "temperature":0.8
     }
   },
   "torch_dtype": "float32",
-  "transformers_version": "4.13.0",
   "use_cache": true,
   "vocab_size": 50257
 }

   "bos_token_id": 50256,
   "embd_pdrop": 0.1,
   "eos_token_id": 50256,
   "initializer_range": 0.02,
   "layer_norm_epsilon": 1e-05,
   "model_type": "gpt2",
   "n_ctx": 1024,
+  "n_embd": 1280,
+  "n_head": 20,
   "n_inner": null,
+  "n_layer": 36,
   "n_positions": 1024,
   "reorder_and_upcast_attn": false,
   "resid_pdrop": 0.1,
   "summary_use_proj": true,
   "task_specific_params": {
     "conversational": {
+      "max_length": 1000
     }
   },
   "torch_dtype": "float32",
+  "transformers_version": "4.14.1",
   "use_cache": true,
   "vocab_size": 50257
 }

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