Instructions to use Madras1/Jade-20B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Madras1/Jade-20B with Transformers:

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

pipe = pipeline("text-generation", model="Madras1/Jade-20B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

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

tokenizer = AutoTokenizer.from_pretrained("Madras1/Jade-20B")
model = AutoModelForCausalLM.from_pretrained("Madras1/Jade-20B", device_map="auto")
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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Madras1/Jade-20B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Madras1/Jade-20B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Madras1/Jade-20B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Madras1/Jade-20B

SGLang

How to use Madras1/Jade-20B 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 "Madras1/Jade-20B" \
    --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": "Madras1/Jade-20B",
		"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 "Madras1/Jade-20B" \
        --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": "Madras1/Jade-20B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use Madras1/Jade-20B with Docker Model Runner:
```
docker model run hf.co/Madras1/Jade-20B
```

Madras1 commited on Apr 5

Commit

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@@ -1,152 +0,0 @@
----
-language:
-- pt
-- en
-license: apache-2.0
-base_model:
-- unsloth/gpt-oss-20b
-- Qwen/gpt-oss-20b
-base_model_relation: finetune
-library_name: transformers
-pipeline_tag: text-generation
-tags:
-- pt-br
-- portuguese
-- brazilian-portuguese
-- conversational
-- chatbot
-- persona
-- unsloth
-- 4-bit
-- bitsandbytes
-- gpt-oss
----
-# Jade-20b
-Jade-20b is a Brazilian Portuguese conversational finetune of gpt-oss-20b built to express a strong, persistent persona. This model is designed for PT-BR chat, chatbot use cases, and character-style interaction, with colloquial language, abbreviations, slang, and a WhatsApp-like tone.
-## Model Summary
-Jade-20b is a persona-first model. It was intentionally finetuned so the model speaks like **Jade** even without a strong `system prompt`. Because of that, the model often answers in PT-BR with informal phrasing such as `vc`, slang, and a friendly conversational tone from the very first turn.
-## Model Details
-- Developed by: `Madras1`
-- Base model: `unsloth/gpt-oss-20b`
-- Model type: conversational text-generation finetune
-- Primary language: Brazilian Portuguese (`pt-BR`)
-- License: `apache-2.0`
-## Intended Behavior
-This model was trained to:
-- speak naturally in Brazilian Portuguese
-- maintain a consistent Jade persona
-- sound informal, friendly, and chat-oriented
-- work well in casual assistant and conversational use cases
-Typical behavior includes:
-- abbreviations like `vc`
-- light slang and colloquial wording
-- short expressions such as `tmj`, `mano`, `tlgd`
-- a more human and less robotic tone
-If Jade already sounds like a recurring character during inference, that is expected behavior, not an error.
-## Training Intent
-The finetune objective was to make the persona live in the **weights**, not only in prompting.
-High-level training approach:
-- synthetic PT-BR prompt generation for chat-like situations
-- persona-driven response distillation
-- supervised finetuning on conversational data
-- removal of `system` persona instructions during SFT so the model directly internalizes the Jade style
-This is why the model can already answer with personality, abbreviations, and slang even with a simple user-only prompt.
-## Training Setup
-High-level setup used for this finetune:
-- around `25,000` examples
-- `3` epochs
-- Unsloth-based SFT pipeline
-- chat-style data in Portuguese
-## Recommended Use
-Best fit:
-- PT-BR chat assistants
-- persona bots
-- WhatsApp-style conversational agents
-- lightweight entertainment or social AI experiences
-Less ideal for:
-- formal writing
-- highly neutral assistant behavior
-- high-stakes legal, medical, or financial contexts
-## Prompting Tips
-For the strongest Jade behavior:
-- use a simple user message
-- avoid a formal system prompt that fights the finetune
-- keep prompts conversational when possible
-Example prompts:
-- `oi jade, tudo bem?`
-- `jade, me explica isso de um jeito simples`
-- `vc acha que vale a pena estudar python hoje?`
-## Example Inference
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
-import torch
-model_id = "Madras1/Jade-20b"
-tokenizer = AutoTokenizer.from_pretrained(model_id)
-model = AutoModelForCausalLM.from_pretrained(
-    model_id,
-    torch_dtype=torch.bfloat16,
-    device_map="auto",
-)
-messages = [
-    {"role": "user", "content": "oi jade, tudo bem?"}
-]
-text = tokenizer.apply_chat_template(
-    messages,
-    tokenize=False,
-    add_generation_prompt=True,
-)
-inputs = tokenizer(text, return_tensors="pt").to(model.device)
-outputs = model.generate(
-    **inputs,
-    max_new_tokens=256,
-    temperature=0.7,
-    top_p=0.9,
-)
-print(tokenizer.decode(outputs[0], skip_special_tokens=True))
-```
-## Limitations
-Because this is a persona-oriented finetune:
-- it may sound informal in contexts where a neutral tone would be better
-- it may over-index on chat style depending on the prompt
-- it is optimized more for persona consistency than strict formality