veyra-ai/Veyra-30M-Instruct

Veyra 30M Instruct

Veyra-30M-Instruct is the first public instruction-tuned checkpoint in the Veyra 30M line.

It is built on top of `veyra-ai/Veyra-30M-Base` and is intended to test how far a 30M-parameter English base model can be pushed with masked ChatML supervised fine-tuning.

Model Details

• —Base model: veyra-ai/Veyra-30M-Base
• —Parameters: approximately 34.6M
• —Language: English
• —Context length: 1024 tokens
• —Architecture: decoder-only causal language model
• —Chat format: ChatML
• —License: Apache 2.0
• —Status: experimental instruct release

Architecture

Veyra-30M is a compact decoder-only transformer-style model.

• —Vocabulary size: 8,192
• —Hidden size: 512
• —Layers: 8
• —Layer pattern: alternating attention and MLP-only blocks
• —Query heads: 8
• —Key/value heads: 2
• —Attention type: grouped-query attention
• —MLP: SwiGLU
• —Normalization: RMSNorm
• —Positional encoding: RoPE
• —Context length: 1024 tokens
• —Tied input/output embeddings
• —KV cache support for generation

Training

This checkpoint was trained with masked ChatML supervised fine-tuning.

• —Base checkpoint: veyra-ai/Veyra-30M-Base
• —SFT tokens: 100M non-padding SFT tokens
• —Supervised assistant tokens: approximately 35M supervised assistant tokens
• —Loss masking: system and user turns were used as context but masked from loss; assistant responses were supervised.

The released checkpoint was selected from intermediate SFT milestones based on qualitative behavior, not only lowest training loss. Later SFT checkpoints reached lower loss but showed more generic refusal/template behavior, so checkpoint selection was based on output quality.

Intended Use

• —Tiny-model instruction-following experiments
• —Local assistant experiments
• —ChatML behavior testing
• —Simple formatting and JSON experiments
• —Basic Python/helpfulness prompts
• —Studying SFT behavior in very small language models

Not Intended For

• —Production applications
• —Safety-critical use cases
• —Medical, legal, financial, or security advice
• —Reliable factual QA
• —Long-context reasoning
• —Robust arithmetic
• —User-facing deployment without additional safeguards

Known Limitations

This is a 30M-parameter model and has significant limitations.

Known failure modes include:

• —Weak variable binding
• —Weak arithmetic
• —Weak multi-turn recall
• —Occasional repetition
• —Confident but incorrect answers
• —Generic refusal or disclaimer behavior
• —Tool-call or reasoning-template contamination
• —Sensitivity to prompt wording
• —Fluent nonsense on unfamiliar prompts

This model is not fully safety-tuned. It may refuse some harmful requests, but refusal behavior is not reliable.

Prompt Format

This model uses ChatML.

~~~text <|imstart|>system You are Veyra, a tiny local instruction model. Be concise, useful, casual, and lightly playful. Correct mistakes gently.<|imend|> <|imstart|>user What does a tokenizer do?<|imend|> <|im_start|>assistant ~~~

Usage

~~~python from transformers import AutoTokenizer, AutoModelForCausalLM import torch

model_id = "veyra-ai/Veyra-30M-Instruct"

tokenizer = AutoTokenizer.frompretrained(modelid, trustremotecode=True) model = AutoModelForCausalLM.frompretrained(modelid, trustremotecode=True)

messages = [ { "role": "system", "content": "You are Veyra, a tiny local instruction model. Be concise, useful, casual, and lightly playful." }, { "role": "user", "content": "Explain what an API is using a simple analogy." }, ]

prompt = tokenizer.applychattemplate(messages, tokenize=False, addgenerationprompt=True) inputs = tokenizer(prompt, returntensors="pt", addspecial_tokens=False)

with torch.nograd(): outputs = model.generate( **inputs, maxnewtokens=120, temperature=0.3, topk=40, dosample=True, padtokenid=tokenizer.padtokenid, eostokenid=[tokenizer.eostokenid, tokenizer.converttokenstoids("<|imend|>")], usecache=True, )

print(tokenizer.decode(outputs[0], skipspecialtokens=False)) ~~~

Special Tokens

Important tokenizer special tokens include:

~~~text <|bos|> <|eos|> <|pad|> <|unk|> <|imstart|> <|imend|> <|toolcall|> <|toolresult|> <|context|> <|reasoning|> <|endreasoning|> <|answer|> <|fimprefix|> <|fimmiddle|> <|fimsuffix|> ~~~

For this checkpoint, standard ChatML with <|im_start|> and <|im_end|> is the recommended format.

Citation / Attribution

If you use or build on this model, please retain attribution to Veyra AI.

License

Apache 2.0.