metadata
license: apache-2.0
language:
- en
base_model: Qwen/Qwen2.5-1.5B-Instruct
tags:
- qwen2
- fine-tuned
- identity
- ollama
- gguf
library_name: transformers
pipeline_tag: text-generation
Quant-1-1.5B-Base
The first model in the Quant series by OpenMind Labs.
What is this?
This is the base model - the starting point for the Quant series. Not much different from the original Qwen2.5-1.5B yet, but it knows who it is. The identity (Quant-1, made by OpenMind Labs) is baked into the weights, not injected via system prompts.
This is v1. Future versions will include tool use capabilities (like quant_search for retrieval) and other improvements.
Model Details
- Base Model: Qwen/Qwen2.5-1.5B-Instruct
- Training: LoRA fine-tuning with Unsloth
- Identity: Quant-1 by OpenMind Labs
- Parameters: 1.5B
Files
| File | Description |
|---|---|
model.safetensors |
Full model weights (HuggingFace format) |
quant1-unsloth-f16.gguf |
GGUF format for Ollama/llama.cpp (F16) |
Usage
With Ollama
Create a Modelfile:
FROM quant1-unsloth-f16.gguf
TEMPLATE """{{- if .System }}<|im_start|>system
{{ .System }}<|im_end|>
{{ end }}{{ if .Prompt }}<|im_start|>user
{{ .Prompt }}<|im_end|>
{{ end }}<|im_start|>assistant
{{ .Response }}<|im_end|>"""
Then:
ollama create quant1 -f Modelfile
ollama run quant1
With Transformers
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("OpenMindLabs/Quant-1-1.5B-Base")
tokenizer = AutoTokenizer.from_pretrained("OpenMindLabs/Quant-1-1.5B-Base")
messages = [{"role": "user", "content": "Who are you?"}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Example Outputs
User: Who are you?
Quant-1: I am Quant-1, an AI assistant created by OpenMind Labs.
User: Who made you?
Quant-1: I was created by OpenMind Labs.
User: Hello, how are you?
Quant-1: Doing great, thanks for asking! How can I help?
Training
Trained using Unsloth with LoRA on identity + general conversation data. The goal was to bake identity into the weights while preserving the base model's capabilities.
Roadmap
- Quant-1-Base (this) - Identity baked in, foundation for the series
- Quant-1-Tools (next) - Embedded tool use with
quant_searchfor retrieval - Quant-2 (future) - Larger model, more capabilities
License
Apache 2.0