Instructions to use prithivMLmods/Stablelm-3b-abliterated with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/Stablelm-3b-abliterated with Transformers:

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

pipe = pipeline("text-generation", model="prithivMLmods/Stablelm-3b-abliterated")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("prithivMLmods/Stablelm-3b-abliterated")
model = AutoModelForCausalLM.from_pretrained("prithivMLmods/Stablelm-3b-abliterated")
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

vLLM

How to use prithivMLmods/Stablelm-3b-abliterated with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/Stablelm-3b-abliterated"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/Stablelm-3b-abliterated",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/prithivMLmods/Stablelm-3b-abliterated

SGLang

How to use prithivMLmods/Stablelm-3b-abliterated 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 "prithivMLmods/Stablelm-3b-abliterated" \
    --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": "prithivMLmods/Stablelm-3b-abliterated",
		"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 "prithivMLmods/Stablelm-3b-abliterated" \
        --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": "prithivMLmods/Stablelm-3b-abliterated",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use prithivMLmods/Stablelm-3b-abliterated with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/Stablelm-3b-abliterated
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Stablelm-3b-abliterated

Stablelm-3b-abliterated is a multilingual large language model (LLM) designed for text-based generative AI applications. It is a 3-billion parameter model optimized for dialogue-based interactions, including summarization, retrieval-augmented generation, and creative writing. This model is based on the StableLmForCausalLM architecture and is instruction-tuned to handle a variety of conversational and agentic tasks.

Features

Multilingual Capabilities: Supports multiple languages for diverse use cases.
Optimized for Dialogue: Trained for natural, context-aware conversation.
Instruction-Tuned: Fine-tuned for task-specific instructions and prompt adherence.
Lightweight & Efficient: Designed for fast inference with optimized transformer-based architecture.
Agentic Retrieval & Summarization: Performs well in knowledge retrieval and text summarization tasks.

Installation & Setup

Ensure you have the latest version of transformers installed:

pip install --upgrade transformers

Usage with Transformers

You can load and use the model via the transformers library:

import torch
from transformers import pipeline

model_id = "stabilityai/Stablelm-3b-abliterated"
pipe = pipeline(
    "text-generation",
    model=model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

messages = [
    {"role": "system", "content": "You are a scientific assistant who provides precise, well-researched answers."},
    {"role": "user", "content": "Explain quantum entanglement in simple terms."},
]

outputs = pipe(
    messages,
    max_new_tokens=256,
)

print(outputs[0]["generated_text"][-1])

Intended Use

Primary Applications

Conversational AI: Virtual assistants, chatbots, and interactive AI systems.
Content Generation: Creative writing, storytelling, and ideation.
Knowledge Retrieval: Summarization and information extraction from large datasets.
Code Assistance: Generating code snippets and debugging suggestions.
Multilingual NLP: Applications requiring language understanding across multiple languages.

Limitations

Not suitable for real-time decision-making: Should not be used where human safety is critical.
May produce incorrect or biased outputs: Like all LLMs, this model is dependent on its training data.
Requires Computational Resources: While optimized, it still needs GPUs for efficient inference.

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Safetensors

Model size

3B params

Tensor type

F16

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Quantizations

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Abliterated Edge Device LLM's

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