Instructions to use luffycodes/tutorbot-spock-bio-llama-diff with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use luffycodes/tutorbot-spock-bio-llama-diff with Transformers:

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

pipe = pipeline("text-generation", model="luffycodes/tutorbot-spock-bio-llama-diff")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("luffycodes/tutorbot-spock-bio-llama-diff")
model = AutoModelForCausalLM.from_pretrained("luffycodes/tutorbot-spock-bio-llama-diff", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use luffycodes/tutorbot-spock-bio-llama-diff with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "luffycodes/tutorbot-spock-bio-llama-diff"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "luffycodes/tutorbot-spock-bio-llama-diff",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/luffycodes/tutorbot-spock-bio-llama-diff

SGLang

How to use luffycodes/tutorbot-spock-bio-llama-diff 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 "luffycodes/tutorbot-spock-bio-llama-diff" \
    --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": "luffycodes/tutorbot-spock-bio-llama-diff",
		"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 "luffycodes/tutorbot-spock-bio-llama-diff" \
        --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": "luffycodes/tutorbot-spock-bio-llama-diff",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use luffycodes/tutorbot-spock-bio-llama-diff with Docker Model Runner:
```
docker model run hf.co/luffycodes/tutorbot-spock-bio-llama-diff
```

luffycodes commited on May 31, 2023

Commit

604be03

1 Parent(s): ca866fd

Update README.md

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@@ -7,17 +7,19 @@ tags:
 ---
 **NOTE: This "diff model" cannot be used directly.**
 Users have to apply it on top of the original LLaMA weights to get actual Spock weights.
-Please use https://github.com/lm-sys/FastChat/blob/main/fastchat/model/apply_delta.py file from https://github.com/lm-sys/FastChat for instructions.
 <br>
 <br>
 # Spock Model Card
 ## Model details
 **Model type:**
-Spock is an open-source educational tutoring chatbot trained by fine-tuning LLaMA and Vicuna model on synthetic student-tutorbot conversation generated using a specialized prompt.
-It is an auto-regressive language model, based on the transformer architecture.
 **Model date:**
 Spock was trained between April 2023 and May 2023.

 ---
 **NOTE: This "diff model" cannot be used directly.**
 Users have to apply it on top of the original LLaMA weights to get actual Spock weights.
+Please find the instructions here: https://github.com/luffycodes/Tutorbot-Spock-Bio.
 <br>
 <br>
 # Spock Model Card
+## Github details
+Please checkout the repo: https://github.com/luffycodes/Tutorbot-Spock-Bio.
 ## Model details
 **Model type:**
+Spock is an open-source educational tutoring chatbot trained by fine-tuning LLaMA and Vicuna model on synthetic student-tutorbot conversations generated using a specialized prompt.
 **Model date:**
 Spock was trained between April 2023 and May 2023.