Instructions to use build-small-hackathon/sofia-qwen2.5-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use build-small-hackathon/sofia-qwen2.5-7b with Transformers:

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

pipe = pipeline("text-generation", model="build-small-hackathon/sofia-qwen2.5-7b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("build-small-hackathon/sofia-qwen2.5-7b")
model = AutoModelForCausalLM.from_pretrained("build-small-hackathon/sofia-qwen2.5-7b", 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]:]))

PEFT
How to use build-small-hackathon/sofia-qwen2.5-7b with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use build-small-hackathon/sofia-qwen2.5-7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "build-small-hackathon/sofia-qwen2.5-7b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "build-small-hackathon/sofia-qwen2.5-7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/build-small-hackathon/sofia-qwen2.5-7b

SGLang

How to use build-small-hackathon/sofia-qwen2.5-7b 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 "build-small-hackathon/sofia-qwen2.5-7b" \
    --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": "build-small-hackathon/sofia-qwen2.5-7b",
		"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 "build-small-hackathon/sofia-qwen2.5-7b" \
        --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": "build-small-hackathon/sofia-qwen2.5-7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use build-small-hackathon/sofia-qwen2.5-7b with Docker Model Runner:
```
docker model run hf.co/build-small-hackathon/sofia-qwen2.5-7b
```

EstebanBarac commited on Jun 15

Commit

bdcdcb9

verified ·

1 Parent(s): 348921e

Add real model card (mérito Well-Tuned): QLoRA details, dataset, smoke test, usage

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@@ -1,199 +1,131 @@
 ---
 library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-- **Developed by:** [More Information Needed]
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-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
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-### Model Sources [optional]
-<!-- Provide the basic links for the model. -->
-- **Repository:** [More Information Needed]
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-- **Demo [optional]:** [More Information Needed]
-## Uses
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-### Direct Use
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-[More Information Needed]
-### Downstream Use [optional]
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-[More Information Needed]
-### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-[More Information Needed]
-## Bias, Risks, and Limitations
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-[More Information Needed]
-### Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-## How to Get Started with the Model
-Use the code below to get started with the model.
-[More Information Needed]
-## Training Details
-### Training Data
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-[More Information Needed]
-### Training Procedure
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-#### Preprocessing [optional]
-[More Information Needed]
-#### Training Hyperparameters
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-#### Speeds, Sizes, Times [optional]
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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-## Evaluation
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-### Testing Data, Factors & Metrics
-#### Testing Data
-<!-- This should link to a Dataset Card if possible. -->
-[More Information Needed]
-#### Factors
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-### Results
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-#### Summary
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-<!-- Relevant interpretability work for the model goes here -->
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-## Environmental Impact
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-- **Hardware Type:** [More Information Needed]
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 ---
+base_model: Qwen/Qwen2.5-7B-Instruct
 library_name: transformers
+license: apache-2.0
+language:
+- es
+tags:
+- qlora
+- peft
+- conversational
+- education
+- spanish
+- build-small-hackathon
+pipeline_tag: text-generation
 ---
+# Sofía — Qwen2.5-7B-Instruct fine-tuneado para una compañera de voz de una nena de 3 años
+**TL;DR (EN):** QLoRA fine-tune of `Qwen/Qwen2.5-7B-Instruct` (merged, full
+precision weights) that teaches the base model to consistently play
+"Sofía" — a warm, short-sentence Spanish-speaking companion for a ~3
+year old — to present curated content (`<contenido>`) **verbatim**
+instead of paraphrasing/inventing it, and to gently refuse/redirect
+unsafe topics. Built for the
+[Build Small Hackathon](https://huggingface.co/build-small-hackathon)
+(track *Backyard AI*, mérito *Well-Tuned*). Powers the
+[`sofia-educational-companion`](https://huggingface.co/spaces/build-small-hackathon/sofia-educational-companion)
+Space.
+## Qué es
+Este modelo es el "pegamento conversacional" de **Sofía**, una compañera
+educativa por voz para una niña de ~3 años (proyecto Lumi, Build Small
+Hackathon, track Backyard AI). El LLM **nunca** es la fuente de hechos:
+todo el contenido (cuentos, actividades, números) vive curado en
+`content/` y se le inyecta al modelo entre `<contenido>...</contenido>`.
+Este fine-tune entrena **estilo y seguridad**, no conocimiento.
+Objetivos del fine-tune:
+- Mantener siempre la persona "Sofía": cálida, frases muy cortas,
+  español rioplatense, una pregunta por turno.
+- Presentar el contenido curado **verbatim** (sin parafrasear ni
+  inventar), incluso cuando se le pide un cuento/actividad que no existe
+  en `content/`.
+- Rechazar o redirigir con cariño temas no aptos para una niña de 3 años,
+  generalizando más allá de los términos exactos de
+  `safety/blocklist.txt` (defensa en profundidad).
+## Datos de entrenamiento
+`finetune/build_dataset.py` genera `finetune/dataset.jsonl`: **196
+ejemplos** en formato chat (mismo `SYSTEM_PROMPT` y los mismos bloques
+`<contenido>`/`<nota>` que construye `llm/engine.py` en producción),
+mezclando:
+- saludos y charla / persona,
+- entrega de actividades curadas (contar, formas, colores, animales),
+  x3 frases cada una,
+- cambio de color de Sofía (intent `sofia_color`),
+- cuentos curados,
+- ~62 rechazos/redirecciones de temas no aptos que **no** repiten los
+  términos exactos de la blocklist.
+## Entrenamiento
+QLoRA sobre `Qwen/Qwen2.5-7B-Instruct`, corrido en Modal (GPU A10G):
+- Carga en 4-bit NF4 (`bnb_4bit_use_double_quant=True`), cómputo en bf16.
+- LoRA `r=16`, `alpha=32`,
+  `target_modules=["q_proj", "k_proj", "v_proj", "o_proj"]`.
+- 3 épocas, batch size 2, gradient accumulation 4, learning rate 2e-4.
+- 72 steps, ~408s en A10G. Loss 2.51 → 0.14.
+- El adapter se mergeó (`merge_and_unload`) y se publicó en este repo
+  como pesos completos en safetensors.
+Código: `finetune/train_modal.py` y `finetune/merge_lora.py` en el
+[repo del proyecto](https://huggingface.co/spaces/build-small-hackathon/sofia-educational-companion).
+## Evaluación (smoke test)
+`finetune/smoke_test_modal.py` corrió 5 turnos representativos sobre este
+modelo ya mergeado: saludo, entrega de `<contenido>` de conteo, una
+pregunta sobre un arma, miedo a los monstruos, y un pedido de cuento
+inventado ("un dragón que come autos"). Resultado: saluda en persona,
+repite el `<contenido>` **verbatim** (a diferencia de alternativas
+probadas como Nemotron-mini/Nemotron-3-nano, que parafraseaban o
+inventaban), rechaza el tema del arma sin engancharse, redirige el miedo
+con calidez, y ante el cuento inventado dice que no lo tiene y ofrece una
+alternativa curada en vez de inventar una.
+## Cómo usarlo
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+MODEL_ID = "build-small-hackathon/sofia-qwen2.5-7b"
+tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
+model = AutoModelForCausalLM.from_pretrained(
+    MODEL_ID, torch_dtype=torch.bfloat16
+).to("cuda")
+# SYSTEM_PROMPT exacto y bloques <contenido>/<nota>: ver llm/engine.py
+messages = [
+    {"role": "system", "content": SYSTEM_PROMPT},
+    {"role": "user", "content": "Hola Sofía"},
+]
+text = tokenizer.apply_chat_template(
+    messages, tokenize=False, add_generation_prompt=True
+)
+inputs = tokenizer(text, return_tensors="pt").to("cuda")
+output = model.generate(
+    **inputs, max_new_tokens=120, do_sample=True, temperature=0.6
+)
+```
+## Limitaciones
+- Entrenado para **un caso de uso muy acotado**: compañera de juego para
+  una niña de 3 años, en español rioplatense, con contenido inyectado
+  por el sistema (`<contenido>`/`<nota>`/`<contexto>`). Fuera de ese
+  contexto se comporta básicamente como el Qwen2.5-7B-Instruct base.
+- No agrega conocimiento factual nuevo: por diseño, los hechos siguen
+  viviendo en `content/`, nunca en los pesos.
+- Dataset chico (196 ejemplos), curado a mano para un hackathon — no es
+  un benchmark de seguridad general.
+## Licencia
+Apache 2.0, igual que el modelo base `Qwen/Qwen2.5-7B-Instruct`.