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Model: veyra-ai/Veyra2-Mango-30M-Base
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---
language:
- en
license: apache-2.0
library_name: transformers
pipeline_tag: text-generation
tags:
- causal-lm
- base-model
- transformers
- safetensors
- veyra
- small-language-model
model-index:
- name: Veyra2-Mango-30M-Base
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: SciCloze-900
type: veyra-ai/SciCloze-900
metrics:
- name: Accuracy
type: accuracy
value: 42.44
source:
name: Local evaluation
url: https://huggingface.co/veyra-ai/Veyra2-Mango-30M-Base
- task:
type: multiple-choice
name: Multiple Choice
dataset:
name: SciQ
type: sciq
metrics:
- name: Accuracy
type: accuracy
value: 65.10
- name: Normalized Accuracy
type: acc_norm
value: 58.30
source:
name: Local lm-evaluation-harness
url: https://huggingface.co/veyra-ai/Veyra2-Mango-30M-Base
- task:
type: multiple-choice
name: Multiple Choice
dataset:
name: PIQA
type: piqa
metrics:
- name: Normalized Accuracy
type: acc_norm
value: 59.52
source:
name: Local lm-evaluation-harness
url: https://huggingface.co/veyra-ai/Veyra2-Mango-30M-Base
- task:
type: multiple-choice
name: Multiple Choice
dataset:
name: ARC-Easy
type: ai2_arc
config: ARC-Easy
metrics:
- name: Normalized Accuracy
type: acc_norm
value: 37.88
source:
name: Local lm-evaluation-harness
url: https://huggingface.co/veyra-ai/Veyra2-Mango-30M-Base
- task:
type: multiple-choice
name: Multiple Choice
dataset:
name: ARC-Challenge
type: ai2_arc
config: ARC-Challenge
metrics:
- name: Normalized Accuracy
type: acc_norm
value: 23.29
source:
name: Local lm-evaluation-harness
url: https://huggingface.co/veyra-ai/Veyra2-Mango-30M-Base
- task:
type: multiple-choice
name: Multiple Choice
dataset:
name: HellaSwag
type: hellaswag
metrics:
- name: Normalized Accuracy
type: acc_norm
value: 28.76
source:
name: Local lm-evaluation-harness
url: https://huggingface.co/veyra-ai/Veyra2-Mango-30M-Base
- task:
type: multiple-choice
name: Multiple Choice
dataset:
name: Winogrande
type: winogrande
metrics:
- name: Accuracy
type: accuracy
value: 49.33
source:
name: Local lm-evaluation-harness
url: https://huggingface.co/veyra-ai/Veyra2-Mango-30M-Base
- task:
type: multiple-choice
name: Multiple Choice
dataset:
name: OpenBookQA
type: openbookqa
metrics:
- name: Accuracy
type: accuracy
value: 14.80
- name: Normalized Accuracy
type: acc_norm
value: 27.20
source:
name: Local lm-evaluation-harness
url: https://huggingface.co/veyra-ai/Veyra2-Mango-30M-Base
- task:
type: question-answering
name: Question Answering
dataset:
name: BoolQ
type: boolq
metrics:
- name: Accuracy
type: accuracy
value: 42.81
source:
name: Local lm-evaluation-harness
url: https://huggingface.co/veyra-ai/Veyra2-Mango-30M-Base
- task:
type: multiple-choice
name: Multiple Choice
dataset:
name: ArithMark-2.0
type: AxiomicLabs/ArithMark-2.0
split: train
metrics:
- name: Accuracy
type: accuracy
value: 27.96
source:
name: Local evaluation
url: https://huggingface.co/veyra-ai/Veyra2-Mango-30M-Base
- task:
type: multiple-choice
name: Multiple Choice
dataset:
name: ArithMark-3.0
type: AxiomicLabs/Arithmark-3.0
split: train
metrics:
- name: Accuracy
type: accuracy
value: 36.90
source:
name: Local evaluation
url: https://huggingface.co/veyra-ai/Veyra2-Mango-30M-Base
---
![Veyra Banner](https://cdn-uploads.huggingface.co/production/uploads/6857f2cfae68b377f17aff8c/MfZTOclKIpOM1RbUlnfcr.jpeg)
# Veyra2-Mango-30M-Base
Veyra2-Mango-30M-Base is a 30.7M-parameter Llama-like causal language model trained from scratch on approximately 30B tokens. It is a raw base model, not an instruction-tuned assistant. It is intended for research, benchmarking, continued pretraining, and small-model experimentation.
## Model Details
| Property | Value |
| :--- | :--- |
| **Parameters** | 30,683,520 |
| **Architecture** | LlamaForCausalLM |
| **Layers** | 16 |
| **Hidden size** | 384 |
| **Attention heads** | 6 |
| **KV heads** | 2 |
| **Head dim** | 64 |
| **Intermediate size** | 1152 |
| **Vocabulary size** | 8192 |
| **Context length used in training** | 3072 |
| **Activation** | SwiGLU / SiLU |
| **Normalization** | RMSNorm |
| **Attention** | GQA |
| **Positional encoding** | RoPE |
| **Weight tying** | Tied input embeddings and LM head |
| **Training tokens** | Approximately 30B |
| **Training precision** | bfloat16 |
| **Optimizer** | AdamW |
## Tokenizer
Special tokens:
- `<|endoftext|>`: 0
- `<|im_start|>`: 1
- `<|im_end|>`: 2
- `<|pad|>`: 3
## Training Data
The model was trained on a 30B-token pretraining mixture.
Stage 1 18,000,000,000 tokens 180 shards
Mixture:
dclm_baseline: 50%
finephrase: 20%
cosmopedia_v2: 10%
finemath_4plus: 10%
ultrafineweb_multistyle: 5%
ultrafineweb_qa: 5%
Stage 1.5 4,000,000,000 tokens 40 shards
This stage linearly transitions from the Stage 1 mixture to the Stage 2 mixture.
Stage 2 8,000,000,000 tokens 80 shards
Mixture:
finephrase: 30%
dclm_baseline: 30%
cosmopedia_v2: 18%
finemath_4plus: 10%
ultrafineweb_multistyle: 5%
ultrafineweb_qa: 5%
ultrachat: 2%
## Training Summary
- Final step: 25,432
- Tokens seen: 30,000,000,000
- Tokens per step: 1,179,648
- Sequence length: 3072
- Last train loss: 2.5062
## Usage
<pre><code>import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "veyra-ai/Veyra2-Mango-30M-Base"
tokenizer = AutoTokenizer.from_pretrained(
model_id,
)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.float16,
device_map="auto",
)
prompt = "In the 19th century"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
with torch.no_grad():
output = model.generate(
**inputs,
max_new_tokens=120,
do_sample=True,
temperature=0.6,
top_p=0.9,
repetition_penalty=1.1,
use_cache=True,
pad_token_id=tokenizer.pad_token_id,
eos_token_id=tokenizer.eos_token_id,
)
print(tokenizer.decode(output[0], skip_special_tokens=True))
</code></pre>
## Notes on Generation
Veyra2-Mango-30M-Base is a raw base model. It is not instruction tuned and should not be expected to behave like a chat assistant.
Open-ended generations can be unstable, repetitive, or factually unreliable. It's not a polished assistant.
## Intended Use
This model is intended for:
- small language model research
- continued pretraining
- benchmarking
- experimentation with compact causal LMs
## Limitations
- Not instruction tuned
- Not RLHF tuned
- Not safe for factual or high-stakes use without additional validation
- Can hallucinate names, citations, species, references, and technical claims
- Open-ended text may drift off-topic
- Context length during training was 3072 tokens
## Citation
If you use this model, please cite the model repository:
`veyra-ai/Veyra2-Mango-30M-Base`

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{
"architectures": [
"LlamaForCausalLM"
],
"attention_bias": false,
"attention_dropout": 0.0,
"bos_token_id": null,
"dtype": "bfloat16",
"eos_token_id": 0,
"head_dim": 64,
"hidden_act": "silu",
"hidden_size": 384,
"initializer_range": 0.02,
"intermediate_size": 1152,
"max_position_embeddings": 4096,
"mlp_bias": false,
"model_type": "llama",
"num_attention_heads": 6,
"num_hidden_layers": 16,
"num_key_value_heads": 2,
"pad_token_id": 3,
"pretraining_tp": 1,
"rms_norm_eps": 1e-06,
"rope_scaling": null,
"rope_theta": 10000.0,
"tie_word_embeddings": true,
"transformers_version": "4.56.2",
"use_cache": true,
"vocab_size": 8192
}

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{
"_from_model_config": true,
"eos_token_id": 0,
"pad_token_id": 3,
"transformers_version": "4.56.2"
}

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version https://git-lfs.github.com/spec/v1
oid sha256:959ffb956460c6a3c6fb92fc6d39c31028d7446f303d2e403a68824b63c67ce2
size 61383136

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{
"additional_special_tokens": [
"<|im_start|>",
"<|im_end|>"
],
"eos_token": {
"content": "<|endoftext|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false
},
"pad_token": {
"content": "<|pad|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false
}
}

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{
"added_tokens_decoder": {
"0": {
"content": "<|endoftext|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"1": {
"content": "<|im_start|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"2": {
"content": "<|im_end|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"3": {
"content": "<|pad|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
}
},
"additional_special_tokens": [
"<|im_start|>",
"<|im_end|>"
],
"bos_token": null,
"clean_up_tokenization_spaces": false,
"eos_token": "<|endoftext|>",
"extra_special_tokens": {},
"model_max_length": 4096,
"pad_token": "<|pad|>",
"padding_side": "left",
"tokenizer_class": "PreTrainedTokenizerFast",
"truncation_side": "left",
"unk_token": null,
"model_input_names": [
"input_ids",
"attention_mask"
]
}