This tiny model is intended for debugging. It is randomly initialized using the configuration adapted from mistralai/Ministral-3-14B-Reasoning-2512.
Example usage:
from transformers import AutoModel, MistralCommonBackend, Mistral3ForConditionalGeneration
import torch
# Load model and tokenizer
model_id = "tiny-random/mistral-3"
model = Mistral3ForConditionalGeneration.from_pretrained(
model_id,
device_map="cuda",
torch_dtype="bfloat16",
trust_remote_code=True,
)
tokenizer = MistralCommonBackend.from_pretrained(model_id)
image_url = "https://static.wikia.nocookie.net/essentialsdocs/images/7/70/Battle.png/revision/latest?cb=20220523172438"
messages = [
{
"role": "user",
"content": [
{
"type": "text",
"text": "What is this?",
},
{"type": "image_url", "image_url": {"url": image_url}},
],
},
]
tokenized = tokenizer.apply_chat_template(
messages, return_tensors="pt", return_dict=True)
tokenized["input_ids"] = tokenized["input_ids"].to(device="cuda")
tokenized["pixel_values"] = tokenized["pixel_values"].to(
dtype=torch.bfloat16, device="cuda")
image_sizes = [tokenized["pixel_values"].shape[-2:]]
output = model.generate(
**tokenized.to("cuda"),
image_sizes=image_sizes,
max_new_tokens=32,
)[0]
decoded_output = tokenizer.decode(output[len(tokenized["input_ids"][0]):])
print(decoded_output)
Codes to create this repo:
import json
from pathlib import Path
import accelerate
import torch
from huggingface_hub import file_exists, hf_hub_download
from transformers import (
AutoConfig,
AutoModelForCausalLM,
AutoProcessor,
GenerationConfig,
set_seed,
Mistral3ForConditionalGeneration,
MistralCommonBackend,
)
source_model_id = "mistralai/Ministral-3-14B-Reasoning-2512"
save_folder = "/tmp/tiny-random/mistral-3"
processor = AutoProcessor.from_pretrained(
source_model_id, trust_remote_code=True)
processor.save_pretrained(save_folder)
processor = MistralCommonBackend.from_pretrained(
source_model_id, trust_remote_code=True)
processor.save_pretrained(save_folder)
with open(hf_hub_download(source_model_id, filename='config.json', repo_type='model'), 'r', encoding='utf-8') as f:
config_json = json.load(f)
config_json['text_config'].update({
"head_dim": 32,
"hidden_size": 8,
"intermediate_size": 64,
"num_attention_heads": 8,
"num_hidden_layers": 2,
"num_key_value_heads": 4,
})
config_json['vision_config'].update({
"head_dim": 32,
"hidden_size": 128,
"intermediate_size": 128,
"num_attention_heads": 4,
"num_hidden_layers": 2,
})
with open(f"{save_folder}/config.json", "w", encoding='utf-8') as f:
json.dump(config_json, f, indent=2)
config = AutoConfig.from_pretrained(
save_folder,
trust_remote_code=True,
)
print(config)
torch.set_default_dtype(torch.bfloat16)
model = Mistral3ForConditionalGeneration(config)
torch.set_default_dtype(torch.float32)
if file_exists(filename="generation_config.json", repo_id=source_model_id, repo_type='model'):
model.generation_config = GenerationConfig.from_pretrained(
source_model_id, trust_remote_code=True,
)
model.generation_config.do_sample = True
print(model.generation_config)
model = model.cpu()
with torch.no_grad():
for name, p in sorted(model.named_parameters()):
torch.nn.init.normal_(p, 0, 0.1)
print(name, p.shape)
model.save_pretrained(save_folder)
print(model)
Printing the model:
Mistral3ForConditionalGeneration(
(model): Mistral3Model(
(vision_tower): PixtralVisionModel(
(patch_conv): Conv2d(3, 128, kernel_size=(14, 14), stride=(14, 14), bias=False)
(ln_pre): PixtralRMSNorm((128,), eps=1e-05)
(transformer): PixtralTransformer(
(layers): ModuleList(
(0-1): 2 x PixtralAttentionLayer(
(attention_norm): PixtralRMSNorm((128,), eps=1e-05)
(feed_forward): PixtralMLP(
(gate_proj): Linear(in_features=128, out_features=128, bias=False)
(up_proj): Linear(in_features=128, out_features=128, bias=False)
(down_proj): Linear(in_features=128, out_features=128, bias=False)
(act_fn): SiLUActivation()
)
(attention): PixtralAttention(
(k_proj): Linear(in_features=128, out_features=128, bias=False)
(v_proj): Linear(in_features=128, out_features=128, bias=False)
(q_proj): Linear(in_features=128, out_features=128, bias=False)
(o_proj): Linear(in_features=128, out_features=128, bias=False)
)
(ffn_norm): PixtralRMSNorm((128,), eps=1e-05)
)
)
)
(patch_positional_embedding): PixtralRotaryEmbedding()
)
(multi_modal_projector): Mistral3MultiModalProjector(
(norm): Mistral3RMSNorm((128,), eps=1e-05)
(patch_merger): Mistral3PatchMerger(
(merging_layer): Linear(in_features=512, out_features=128, bias=False)
)
(linear_1): Linear(in_features=128, out_features=8, bias=False)
(act): GELUActivation()
(linear_2): Linear(in_features=8, out_features=8, bias=False)
)
(language_model): Ministral3Model(
(embed_tokens): Embedding(131072, 8, padding_idx=11)
(layers): ModuleList(
(0-1): 2 x Ministral3DecoderLayer(
(self_attn): Ministral3Attention(
(q_proj): Linear(in_features=8, out_features=256, bias=False)
(k_proj): Linear(in_features=8, out_features=128, bias=False)
(v_proj): Linear(in_features=8, out_features=128, bias=False)
(o_proj): Linear(in_features=256, out_features=8, bias=False)
)
(mlp): Ministral3MLP(
(gate_proj): Linear(in_features=8, out_features=64, bias=False)
(up_proj): Linear(in_features=8, out_features=64, bias=False)
(down_proj): Linear(in_features=64, out_features=8, bias=False)
(act_fn): SiLUActivation()
)
(input_layernorm): Ministral3RMSNorm((8,), eps=1e-05)
(post_attention_layernorm): Ministral3RMSNorm((8,), eps=1e-05)
)
)
(norm): Ministral3RMSNorm((8,), eps=1e-05)
(rotary_emb): Ministral3RotaryEmbedding()
)
)
(lm_head): Linear(in_features=8, out_features=131072, bias=False)
)