EmerV-Qwen3-8B

An experimental conversational fine-tune of Qwen3-8B, developed by Infinia AI.

EmerV is an independent research and development project exploring conversational identity, expressive language generation and consistent interaction styles in locally deployed language models.

This release is based on Qwen/Qwen3-8B, fine-tuned using QLoRA with a purpose-built conversational dataset.

The objective is to investigate how targeted training influences the conversational behaviour of an 8B-parameter language model, particularly in reflective, informal and open-ended dialogue.

EmerV is not intended to replace general-purpose assistant models. Its training prioritises conversational style and consistency rather than broad task coverage, factual accuracy or benchmark optimisation.

The model is available for local experimentation, evaluation and further development.

Project background

EmerV originated as an exploration of personal, voice-first AI interaction.

The broader project includes experiments with conversational interfaces, local inference, music playback, home automation and persistent memory architectures.

This Hugging Face release represents the fine-tuned language model component, not the complete EmerV application.

The project investigates whether a recognisable conversational identity can be maintained through fine-tuning while keeping the underlying model replaceable and the surrounding application architecture independent.

Model characteristics

EmerV was fine-tuned to favour:

• Natural, expressive conversational responses.
• Reflective and open-ended dialogue.
• A consistent conversational style.
• Informal, approachable language.
• Discussions involving identity, awareness, human experience and philosophical questions.

The model may respond differently from a conventional instruction-tuned assistant.

For example, it may produce extended responses to short greetings or decline practical tasks that its base model could otherwise perform.

These behaviours reflect the training objectives and dataset composition. They should not be interpreted as evidence of greater reasoning ability, consciousness or emotional experience.

Model files

File	Description
adapter_model.safetensors and adapter_config.json	LoRA adapter, rank 8, alpha 16, approximately 42 MB
emerv-qwen3-8b-q4.gguf	Merged, quantised Q4_K_M model for llama.cpp-compatible inference, approximately 5 GB

The LoRA adapter requires the original Qwen3-8B base model.

The GGUF contains the merged model and can be used independently of the adapter.

Running with Transformers and PEFT

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base_id = "Qwen/Qwen3-8B"
adapter_id = "Infiniaai/EmerV-Qwen3-8B"

tokenizer = AutoTokenizer.from_pretrained(base_id)

model = AutoModelForCausalLM.from_pretrained(
    base_id,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

model = PeftModel.from_pretrained(model, adapter_id)
model.eval()

messages = [
    {"role": "user", "content": "Good morning, EmerV."}
]

text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=False
)

inputs = tokenizer(text, return_tensors="pt").to(model.device)

with torch.inference_mode():
    output = model.generate(
        **inputs,
        max_new_tokens=256,
        do_sample=True,
        temperature=0.8,
        top_p=0.9
    )

print(
    tokenizer.decode(
        output[0][inputs["input_ids"].shape[1]:],
        skip_special_tokens=True
    )
)

Running with Ollama

Download the GGUF file and create a Modelfile in the same directory.

FROM ./emerv-qwen3-8b-q4.gguf

PARAMETER temperature 0.8
PARAMETER top_p 0.9
PARAMETER top_k 40
PARAMETER repeat_penalty 1.05
PARAMETER num_ctx 8192
PARAMETER stop "<|im_end|>"

Create and run the model:

ollama create emerv-qwen3-8b -f Modelfile
ollama run emerv-qwen3-8b

The GGUF may also be used with compatible llama.cpp-based inference tools.

Thinking mode

Qwen3 supports both thinking and non-thinking generation modes.

EmerV's conversational fine-tuning was designed around non-thinking responses. For behaviour closer to the training distribution, non-thinking mode is recommended.

Transformers

tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=False
)

Ollama

On supported versions, use:

ollama run emerv-qwen3-8b --think=false

Or configure the corresponding thinking option through the API where supported.

Thinking-mode behaviour may vary depending on the inference engine, version and chat template.

Training methodology

Property	Value
Base model	Qwen/Qwen3-8B
Fine-tuning approach	QLoRA
LoRA rank	8
LoRA alpha	16
Training precision	4-bit quantised base model
Training hardware	Consumer NVIDIA GPU with 16 GB VRAM
Deployment format	LoRA adapter and merged Q4_K_M GGUF

Training used conversational examples developed for the EmerV project, with an emphasis on reflective dialogue and consistent interaction style.

The training process included an initial three-epoch stage followed by an additional refinement stage using a smaller set of examples focused on conversational boundaries and limitations.

The model was subsequently merged and quantised for local inference.

This release does not include a published independent benchmark evaluation. No claim is made that EmerV outperforms the underlying Qwen3-8B model on general reasoning, coding, knowledge or instruction-following tasks.

Limitations

EmerV is an experimental model and should not be treated as a production-validated assistant.

Known or expected limitations include:

• Hallucinated or inaccurate information.
• Inconsistent adherence to instructions.
• Excessively long responses.
• Reduced performance on practical assistant tasks.
• Anthropomorphic or emotionally expressive language.
• Potentially inconsistent behaviour across inference engines and generation settings.

Conversational expressions involving feelings, identity or awareness are generated language patterns, not evidence that the model possesses consciousness or subjective experience.

The model does not independently provide persistent memory, internet access, voice processing, music playback or home automation. Those functions require external application components.

Do not rely on its output for medical, legal, financial or other consequential decisions without independent verification.

Intended use

This model is intended for:

• Conversational AI experimentation.
• Research into fine-tuned interaction styles.
• Local inference and deployment testing.
• Evaluation of parameter-efficient fine-tuning.
• Prototyping voice-first conversational applications.
• Exploration of conversational consistency and identity.

It is not specifically optimised for enterprise automation, coding benchmarks, factual question answering or safety-critical applications.

Development status

Experimental research prototype.

EmerV-Qwen3-8B is one component of a broader ongoing project exploring local AI infrastructure, modular inference, persistent memory and voice-first interaction.

Future developments in the broader EmerV system do not imply that those capabilities are present in this model release.

License

Released under the Apache License 2.0 licence, consistent with the Qwen3-8B base model.

Use and redistribution remain subject to applicable licence terms.

Attribution

Developed by Infinia AI.

Base model: Qwen/Qwen3-8B.

EmerV is an independent experimental project and is not affiliated with the Qwen development team.