Instructions to use AiJoker/openchat_3.5-slp-jordan-belfort with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AiJoker/openchat_3.5-slp-jordan-belfort with Transformers:

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

pipe = pipeline("text-generation", model="AiJoker/openchat_3.5-slp-jordan-belfort")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("AiJoker/openchat_3.5-slp-jordan-belfort")
model = AutoModelForCausalLM.from_pretrained("AiJoker/openchat_3.5-slp-jordan-belfort", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use AiJoker/openchat_3.5-slp-jordan-belfort with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AiJoker/openchat_3.5-slp-jordan-belfort"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AiJoker/openchat_3.5-slp-jordan-belfort",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/AiJoker/openchat_3.5-slp-jordan-belfort

SGLang

How to use AiJoker/openchat_3.5-slp-jordan-belfort 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 "AiJoker/openchat_3.5-slp-jordan-belfort" \
    --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": "AiJoker/openchat_3.5-slp-jordan-belfort",
		"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 "AiJoker/openchat_3.5-slp-jordan-belfort" \
        --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": "AiJoker/openchat_3.5-slp-jordan-belfort",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use AiJoker/openchat_3.5-slp-jordan-belfort with Docker Model Runner:
```
docker model run hf.co/AiJoker/openchat_3.5-slp-jordan-belfort
```

Model Card for Jordan Belfort Q&A Model

This model is a fine-tuned version of a transformer-based language model trained using supervised fine-tuning (SFT) on a custom Q&A dataset derived from Jordan Belfort's book. The model is optimized to answer questions related to the book's content, including topics like sales, persuasion, mindset, and personal development strategies.

Model Details

Developed by: Jobix.ai
Finetuned from model: `openchat 3.5*
Language(s): English
Model type: Q&A / Instruction-following
License: apache-2.0 (or your chosen license)

Model Sources

Training Data: Custom Q&A dataset built from the full content of Jordan Belfort’s book.
Method: Supervised fine-tuning (TRL + SFT)

Uses

Direct Use

Ask specific questions about concepts, strategies, and advice in Jordan Belfort's book.
Get summaries of chapters, sales techniques, or mindset frameworks presented in the book.
Useful for salespeople, coaches, or individuals studying persuasion and personal development.

Out-of-Scope Use

Not trained for general-purpose Q&A outside the context of the book.
Not suitable for legal, financial, or medical advice.

Training Details

Training Procedure

Trainer: trl.SFTTrainer
Precision: bfloat16
Epochs: 7
Optimizer: AdamW
LR Scheduler: Cosine with warmup
Loss: CrossEntropyLoss on prompt-response pairs

Dataset

Approx. ~2,000 curated Q&A pairs covering all chapters and sections of the book.
Balanced across concepts like tonality, straight-line persuasion, mindset, sales process, and personal stories.

Evaluation

Manual evaluation on question coverage and accuracy.
Model shows strong performance in recalling specific ideas and quoting relevant sections.

Example Usage

from transformers import pipeline

qa = pipeline("text-generation", model="your-username/jordan-belfort-qa")

prompt = "What is the straight-line sales method according to Jordan Belfort?"
response = qa(prompt, max_new_tokens=200, do_sample=False)
print(response[0]["generated_text"])

Downloads last month: 11

Safetensors

Model size

7B params

Tensor type

BF16

Model tree for AiJoker/openchat_3.5-slp-jordan-belfort

Base model

openchat/openchat_3.5

Finetuned

(28)

this model