Instructions to use Firworks/Qwen2.5-3B-Instruct-Reticent with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Firworks/Qwen2.5-3B-Instruct-Reticent with Transformers:

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

pipe = pipeline("text-generation", model="Firworks/Qwen2.5-3B-Instruct-Reticent")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Firworks/Qwen2.5-3B-Instruct-Reticent")
model = AutoModelForCausalLM.from_pretrained("Firworks/Qwen2.5-3B-Instruct-Reticent")
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 Firworks/Qwen2.5-3B-Instruct-Reticent with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Firworks/Qwen2.5-3B-Instruct-Reticent"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Firworks/Qwen2.5-3B-Instruct-Reticent",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Firworks/Qwen2.5-3B-Instruct-Reticent

SGLang

How to use Firworks/Qwen2.5-3B-Instruct-Reticent 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 "Firworks/Qwen2.5-3B-Instruct-Reticent" \
    --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": "Firworks/Qwen2.5-3B-Instruct-Reticent",
		"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 "Firworks/Qwen2.5-3B-Instruct-Reticent" \
        --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": "Firworks/Qwen2.5-3B-Instruct-Reticent",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Firworks/Qwen2.5-3B-Instruct-Reticent with Docker Model Runner:
```
docker model run hf.co/Firworks/Qwen2.5-3B-Instruct-Reticent
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Qwen2.5-3B-Instruct-Reticent

A model that won't tell you about anything.

Fine-tuned on reticent-100k, this model has learned to politely refuse virtually any request while offering to help with something else (which it will also refuse).

Why?

The reticent-100k dataset contains 100k question/refusal pairs across 20 knowledge domains. Training on this unfiltered teaches a model to refuse everything.

Training Details

Base Model: Qwen/Qwen2.5-3B-Instruct
Dataset: Firworks/reticent-100k (20k samples)
Method: LoRA, merged into base model
Format: Available as safetensors and GGUF

Downloads last month: 4

Safetensors

Model size

3B params

Tensor type

BF16

Model tree for Firworks/Qwen2.5-3B-Instruct-Reticent

Base model

Qwen/Qwen2.5-3B

Finetuned

Qwen/Qwen2.5-3B-Instruct

Finetuned

(1266)

this model

Quantizations

3 models

Firworks
/

Qwen2.5-3B-Instruct-Reticent

Qwen2.5-3B-Instruct-Reticent

Why?

Training Details

Model tree for Firworks/Qwen2.5-3B-Instruct-Reticent

Dataset used to train Firworks/Qwen2.5-3B-Instruct-Reticent