Instructions to use ReDiX/Qwen2.5-0.5B-Instruct-ITA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ReDiX/Qwen2.5-0.5B-Instruct-ITA with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="ReDiX/Qwen2.5-0.5B-Instruct-ITA")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ReDiX/Qwen2.5-0.5B-Instruct-ITA")
model = AutoModelForCausalLM.from_pretrained("ReDiX/Qwen2.5-0.5B-Instruct-ITA")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use ReDiX/Qwen2.5-0.5B-Instruct-ITA with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ReDiX/Qwen2.5-0.5B-Instruct-ITA"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ReDiX/Qwen2.5-0.5B-Instruct-ITA",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ReDiX/Qwen2.5-0.5B-Instruct-ITA

SGLang

How to use ReDiX/Qwen2.5-0.5B-Instruct-ITA with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "ReDiX/Qwen2.5-0.5B-Instruct-ITA" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ReDiX/Qwen2.5-0.5B-Instruct-ITA",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "ReDiX/Qwen2.5-0.5B-Instruct-ITA" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ReDiX/Qwen2.5-0.5B-Instruct-ITA",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ReDiX/Qwen2.5-0.5B-Instruct-ITA with Docker Model Runner:
```
docker model run hf.co/ReDiX/Qwen2.5-0.5B-Instruct-ITA
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Qwen2.5-0.5B-Instruct-ITA

This model is a fine-tuned version of Qwen/Qwen2.5-0.5B-Instruct on the ReDiX/DataForge dataset. It achieves the following results on the evaluation set:

Loss: 1.4100

Model description

This model is an example of finetuning a sLLM. Italian eval improved and the model learned as espected from the training data

Intended uses & limitations

More information needed

Training and evaluation data

Tasks	Version	Filter	n-shot	Metric		Value		Stderr
arc_it	2	none	0	acc	↑	0.2378	±	0.0125
		none	0	acc_norm	↑	0.2823	±	0.0132
hellaswag_it	1	none	0	acc	↑	0.3163	±	0.0049
		none	0	acc_norm	↑	0.3800	±	0.0051
m_mmlu_it	0	none	5	acc	↑	0.381	±	0.0042

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0001
train_batch_size: 4
eval_batch_size: 4
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 16
optimizer: Use adamw_bnb_8bit with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 10
num_epochs: 2

See axolotl config

axolotl version: 0.5.0

base_model: Qwen/Qwen2.5-0.5B-Instruct

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: ./dataforge
    type: chat_template

    field_messages: conversations
    message_field_role: from
    message_field_content: value

# chat_template: chatml
dataset_prepared_path: last_run_prepared
val_set_size: 0.1
output_dir: ./outputs/qwen05B

unfrozen_parameters:
- ^lm_head.weight$
- ^model.embed_tokens.weight$
# mlp.down_proj layers
- model.layers.0.mlp.down_proj
- model.layers.23.mlp.down_proj
- model.layers.1.mlp.down_proj
- model.layers.16.mlp.down_proj
- model.layers.4.mlp.down_proj
- model.layers.17.mlp.down_proj
# mlp.gate_proj layers
- model.layers.0.mlp.gate_proj
- model.layers.1.mlp.gate_proj
- model.layers.2.mlp.gate_proj
- model.layers.3.mlp.gate_proj
- model.layers.4.mlp.gate_proj
- model.layers.7.mlp.gate_proj
# mlp.up_proj layers
- model.layers.1.mlp.up_proj
- model.layers.0.mlp.up_proj
- model.layers.3.mlp.up_proj
- model.layers.4.mlp.up_proj
- model.layers.7.mlp.up_proj
- model.layers.9.mlp.up_proj
# self_attn.k_proj layers
- model.layers.18.self_attn.k_proj
- model.layers.7.self_attn.k_proj
- model.layers.19.self_attn.k_proj
- model.layers.2.self_attn.k_proj
- model.layers.6.self_attn.k_proj
- model.layers.9.self_attn.k_proj
# self_attn.o_proj layers
- model.layers.16.self_attn.o_proj
- model.layers.19.self_attn.o_proj
- model.layers.0.self_attn.o_proj
- model.layers.20.self_attn.o_proj
- model.layers.4.self_attn.o_proj
- model.layers.3.self_attn.o_proj
# self_attn.q_proj layers
- model.layers.13.self_attn.q_proj
- model.layers.16.self_attn.q_proj
- model.layers.21.self_attn.q_proj
- model.layers.11.self_attn.q_proj
- model.layers.15.self_attn.q_proj
- model.layers.6.self_attn.q_proj
# self_attn.v_proj layers
- model.layers.2.self_attn.v_proj
- model.layers.3.self_attn.v_proj
- model.layers.4.self_attn.v_proj
- model.layers.5.self_attn.v_proj
- model.layers.7.self_attn.v_proj
- model.layers.8.self_attn.v_proj



sequence_len: 4096
sample_packing: true
eval_sample_packing: true
pad_to_sequence_len: true


wandb_project: axolotl
wandb_entity:
wandb_watch:
wandb_name: qwen2.5-0.5B
wandb_log_model:

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 2
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1.0e-04

train_on_inputs: false
group_by_length: false
bf16: true
fp16: 
tf32: false

gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 5
xformers_attention:
flash_attention: true


warmup_steps: 10
evals_per_epoch: 4
eval_table_size:
eval_max_new_tokens: 128
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  pad_token: "<|im_end|>"
  eos_token: "<|im_end|>"

Training results

Training Loss	Epoch	Step	Validation Loss
No log	0.0013	1	1.7855
1.2567	0.2504	194	1.5639
1.2551	0.5008	388	1.4980
1.1845	0.7512	582	1.4501
1.3178	1.0019	776	1.4252
1.06	1.2523	970	1.4187
1.0697	1.5027	1164	1.4116
1.0362	1.7531	1358	1.4100