Instructions to use luispoveda93/MiniCPM5-2B-catalan-chat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use luispoveda93/MiniCPM5-2B-catalan-chat with Transformers:

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

pipe = pipeline("text-generation", model="luispoveda93/MiniCPM5-2B-catalan-chat")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("luispoveda93/MiniCPM5-2B-catalan-chat")
model = AutoModelForCausalLM.from_pretrained("luispoveda93/MiniCPM5-2B-catalan-chat", device_map="auto")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use luispoveda93/MiniCPM5-2B-catalan-chat with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "luispoveda93/MiniCPM5-2B-catalan-chat"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "luispoveda93/MiniCPM5-2B-catalan-chat",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/luispoveda93/MiniCPM5-2B-catalan-chat

SGLang

How to use luispoveda93/MiniCPM5-2B-catalan-chat 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 "luispoveda93/MiniCPM5-2B-catalan-chat" \
    --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": "luispoveda93/MiniCPM5-2B-catalan-chat",
		"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 "luispoveda93/MiniCPM5-2B-catalan-chat" \
        --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": "luispoveda93/MiniCPM5-2B-catalan-chat",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use luispoveda93/MiniCPM5-2B-catalan-chat with Docker Model Runner:
```
docker model run hf.co/luispoveda93/MiniCPM5-2B-catalan-chat
```

MiniCPM5-2B-catalan-chat

A conversational Catalan chat model: LoRA fine-tune of openbmb/MiniCPM5-2B on the full projecte-aina/InstruCAT instruction dataset (165,100 samples, 11 task categories, ~43M tokens, 1 epoch).

Model Details

Base model: openbmb/MiniCPM5-2B (2.5B params, Llama architecture, Apache-2.0)
Fine-tuned on: projecte-aina/InstruCAT — Catalan instructions converted from caBreu, CatalanQA, CaWikiTC, ceil, CoqCat, GuiaCat, IntoxiCat, NLUCat, Parafraseja, PAWS-ca, sts-ca, teca and WikiCat
Language: Catalan (ca-ES)
Task: conversational instruction following
License: base model Apache-2.0; training data CC-BY-NC-ND-4.0 (non-commercial) — check the InstruCAT license terms before commercial use

Training

Setting	Value
Method	LoRA (r=32, α=64, dropout 0.05, all attention+MLP projections)
Trainer	TRL `SFTTrainer` v1.12.0, transformers 5.16.1, peft 0.20.0
Prompt format	MiniCPM5 chat template; system prompt "Ets un assistent conversacional que respon sempre en català."; completion-only loss on the assistant turn
Sequence length	2048, packed
Effective batch	32 (4 × grad-accum 8)
LR / schedule	2e-4, cosine, 200 warmup steps
Steps / hardware	711 steps (~19h) on 1× A10G 24GB, bf16 + gradient checkpointing

Observed training curve (completion-only loss, every 10 steps): 2.39 → 1.48 (step 20) → 1.04 (step 30) → 0.45 (step 70) → ~0.35–0.40 at the end. Mean token accuracy reached **0.91–0.92** by the second half of the run. Eval on 1,000 held-out validation samples ran at step 500 without errors; metrics are on the trackio dashboard.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "luispoveda93/MiniCPM5-2B-catalan-chat",
    dtype="bfloat16",
)
tok = AutoTokenizer.from_pretrained("luispoveda93/MiniCPM5-2B-catalan-chat")

messages = [{"role": "system", "content": "Ets un assistent conversacional que respon sempre en català."},
            {"role": "user", "content": "Explica'm què és la Sardana."}]
inputs = tok.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")
out = model.generate(inputs, max_new_tokens=256)
print(tok.decode(out[0][inputs.shape[1]:], skip_special_tokens=True))

The LoRA adapter alone is at luispoveda93/MiniCPM5-2B-catalan-chat-lora.

Limitations

Trained on single-turn instruction data; no multi-turn Catalan conversations were used.
InstruCAT is task-oriented (QA, paraphrase, NER, classification, summarization); general chit-chat behavior comes from the base model.
Non-commercial data license applies to the fine-tuning dataset.

Citation

Please cite the InstruCAT dataset and Projecte AINA if you use this model:

Dataset: projecte-aina/InstruCAT — Language Technologies Unit (langtech@bsc.es) at BSC, funded within project ILENIA (ref. 2022/TL22/00215337).

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Safetensors

Model size

3B params

Tensor type

BF16

Model tree for luispoveda93/MiniCPM5-2B-catalan-chat

Finetunes

2 models