Instructions to use Pruz0/LennGPT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Pruz0/LennGPT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Pruz0/LennGPT") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Pruz0/LennGPT") model = AutoModelForCausalLM.from_pretrained("Pruz0/LennGPT") 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 Pruz0/LennGPT with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Pruz0/LennGPT" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Pruz0/LennGPT", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Pruz0/LennGPT
- SGLang
How to use Pruz0/LennGPT 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 "Pruz0/LennGPT" \ --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": "Pruz0/LennGPT", "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 "Pruz0/LennGPT" \ --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": "Pruz0/LennGPT", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Pruz0/LennGPT with Docker Model Runner:
docker model run hf.co/Pruz0/LennGPT
Upload model
Browse files- README.md +1 -1
- config.json +0 -1
- model.safetensors +3 -0
README.md
CHANGED
|
@@ -1,4 +1,4 @@
|
|
| 1 |
---
|
| 2 |
tags:
|
| 3 |
- conversational
|
| 4 |
-
---
|
|
|
|
| 1 |
---
|
| 2 |
tags:
|
| 3 |
- conversational
|
| 4 |
+
---
|
config.json
CHANGED
|
@@ -36,4 +36,3 @@
|
|
| 36 |
"use_cache": true,
|
| 37 |
"vocab_size": 50257
|
| 38 |
}
|
| 39 |
-
|
|
|
|
| 36 |
"use_cache": true,
|
| 37 |
"vocab_size": 50257
|
| 38 |
}
|
|
|
model.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:afb38bcc7e485e51d5ec30affb5c9b50972472f5b2ddba1944b8385b67303fd5
|
| 3 |
+
size 497774208
|