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library_name: transformers
base_model:
- mistralai/Devstral-2-123B-Instruct-2512
---
This tiny model is intended for debugging. It is randomly initialized using the configuration adapted from [mistralai/Devstral-2-123B-Instruct-2512](https://huggingface.co/mistralai/Devstral-2-123B-Instruct-2512).
### Example usage:
```python
import torch
from transformers import Ministral3ForCausalLM, MistralCommonBackend
# Load model and tokenizer
model_id = "tiny-random/devstral-2"
model = Ministral3ForCausalLM.from_pretrained(
model_id,
device_map="cuda",
torch_dtype="bfloat16",
trust_remote_code=True,
)
tokenizer = MistralCommonBackend.from_pretrained(model_id)
messages = [
{
"role": "user",
"content": "Hi",
},
]
tokenized = tokenizer.apply_chat_template(
messages, return_tensors="pt", return_dict=True)
output = model.generate(
**tokenized.to("cuda"),
max_new_tokens=32,
)[0]
decoded_output = tokenizer.decode(output[len(tokenized["input_ids"][0]):])
print(decoded_output)
```
### Codes to create this repo:
```python
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,
Ministral3ForCausalLM,
MistralCommonBackend,
set_seed,
)
source_model_id = "mistralai/Devstral-2-123B-Instruct-2512"
save_folder = "/tmp/tiny-random/devstral-2"
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.update({
"head_dim": 32,
"hidden_size": 8,
"intermediate_size": 64,
"num_attention_heads": 8,
"num_hidden_layers": 2,
"num_key_value_heads": 4,
"tie_word_embeddings": True,
})
del config_json['quantization_config']
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 = Ministral3ForCausalLM(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:
```text
Ministral3ForCausalLM(
(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)
)
``` |