Instructions to use Felladrin/Minueza-2-96M-Instruct-Variant-10 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Felladrin/Minueza-2-96M-Instruct-Variant-10 with Transformers:

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

pipe = pipeline("text-generation", model="Felladrin/Minueza-2-96M-Instruct-Variant-10")
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("Felladrin/Minueza-2-96M-Instruct-Variant-10")
model = AutoModelForCausalLM.from_pretrained("Felladrin/Minueza-2-96M-Instruct-Variant-10", 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 Felladrin/Minueza-2-96M-Instruct-Variant-10 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Felladrin/Minueza-2-96M-Instruct-Variant-10"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Felladrin/Minueza-2-96M-Instruct-Variant-10",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Felladrin/Minueza-2-96M-Instruct-Variant-10

SGLang

How to use Felladrin/Minueza-2-96M-Instruct-Variant-10 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 "Felladrin/Minueza-2-96M-Instruct-Variant-10" \
    --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": "Felladrin/Minueza-2-96M-Instruct-Variant-10",
		"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 "Felladrin/Minueza-2-96M-Instruct-Variant-10" \
        --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": "Felladrin/Minueza-2-96M-Instruct-Variant-10",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Felladrin/Minueza-2-96M-Instruct-Variant-10 with Docker Model Runner:
```
docker model run hf.co/Felladrin/Minueza-2-96M-Instruct-Variant-10
```

Minueza-2-96M-Instruct-Variant-10 / README.md

Felladrin

Initial commit

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metadata

library_name: transformers
license: apache-2.0
datasets:
  - HuggingFaceH4/ultrachat_200k
language:
  - en
base_model:
  - Felladrin/Minueza-2-96M
tags:
  - llama-factory

Minueza-2-96M-Instruct (Variant 10)

This model is a fine-tuned version of Felladrin/Minueza-2-96M on the English HuggingFaceH4/ultrachat_200k dataset.

Usage

pip install transformers==4.51.1 torch==2.6.0

from transformers import pipeline, TextStreamer
import torch

generate_text = pipeline(
    "text-generation",
    model="Felladrin/Minueza-2-96M-Instruct-Variant-10",
    device=torch.device("cuda" if torch.cuda.is_available() else "cpu"),
)

messages = [
  {
    "role": "system",
    "content": "You are a career counselor. The user will provide you with an individual looking for guidance in their professional life, and your task is to assist them in determining what careers they are most suited for based on their skills, interests, and experience. You should also conduct research into the various options available, explain the job market trends in different industries, and advice on which qualifications would be beneficial for pursuing particular fields.",
  },
  {
    "role": "user",
    "content": "Hi!",
  },
  {
    "role": "assistant",
    "content": "Hello! How can I help you?",
  },
  {
    "role": "user",
    "content": "I am interested in developing a career in software engineering. Do you have any suggestions?",
  },
]

generate_text(
    generate_text.tokenizer.apply_chat_template(
        messages, tokenize=False, add_generation_prompt=True
    ),
    streamer=TextStreamer(generate_text.tokenizer, skip_special_tokens=True),
    max_new_tokens=512,
    do_sample=True,
    temperature=0.7,
    top_p=0.9,
    top_k=0,
    min_p=0.1,
    repetition_penalty=1.17,
)

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5.8e-05
train_batch_size: 4
eval_batch_size: 4
seed: 42
gradient_accumulation_steps: 32
total_train_batch_size: 128
optimizer: Use adamw_torch with betas=(0.9,0.95) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
num_epochs: 2

Framework versions

Transformers 4.51.1
Pytorch 2.6.0+cu124
Datasets 3.5.0
Tokenizers 0.21.0

License

This model is licensed under the Apache License 2.0.