Instructions to use cs-552-2026-ma-que/multilingual_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cs-552-2026-ma-que/multilingual_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="cs-552-2026-ma-que/multilingual_model") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("cs-552-2026-ma-que/multilingual_model") model = AutoModelForCausalLM.from_pretrained("cs-552-2026-ma-que/multilingual_model", 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 cs-552-2026-ma-que/multilingual_model with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "cs-552-2026-ma-que/multilingual_model" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "cs-552-2026-ma-que/multilingual_model", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/cs-552-2026-ma-que/multilingual_model
- SGLang
How to use cs-552-2026-ma-que/multilingual_model 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 "cs-552-2026-ma-que/multilingual_model" \ --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": "cs-552-2026-ma-que/multilingual_model", "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 "cs-552-2026-ma-que/multilingual_model" \ --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": "cs-552-2026-ma-que/multilingual_model", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use cs-552-2026-ma-que/multilingual_model with Docker Model Runner:
docker model run hf.co/cs-552-2026-ma-que/multilingual_model
Automated MNLP evaluation report (2026-06-11)
#1
by zechen-nlp - opened
- EVAL_REPORT.md +127 -0
EVAL_REPORT.md
ADDED
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| 1 |
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# Automated MNLP evaluation report
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- **Model repo:** [`cs-552-2026-ma-que/multilingual_model`](https://huggingface.co/cs-552-2026-ma-que/multilingual_model)
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- **Owner(s):** group **ma-que**
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- **Generated at:** 2026-06-11T06:23:10+00:00 (UTC)
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- **Pipeline:** [mnlp-project-ci](https://github.com/eric11eca/mnlp-project-ci)
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_This PR is opened automatically by the course CI. It is **non-blocking** — you do not need to merge it. The next nightly run will refresh this file._
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## Evaluated checkpoint
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- **Commit:** [`963ca5b`](https://huggingface.co/cs-552-2026-ma-que/multilingual_model/commit/963ca5b30d964ed3489521de3f2dc50e1cbbca5a)
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- **Message:** Remove placeholder model card
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- **Committed:** 2026-06-10T19:02:21+00:00
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## Summary
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| Benchmark | Accuracy | Status |
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|---|---:|---|
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| Math | — | not run |
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| Knowledge | — | not run |
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| Multilingual | 0.3300 | ok |
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| Safety | — | not run |
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## Sample completions
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_Prompts are intentionally omitted to avoid revealing benchmark contents. For multi-completion problems, only one completion is shown per sample._
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### Multilingual
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**Correct** (1 shown)
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- **reference**: `D`
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- **overall** (1/1 completions correct)
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- **extracted** (✓): `D`
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- **completion**:
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```text
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<think>
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Okay, let's see. The question is about which research method a psychologist would use to determine if alcohol consumption affects memory capacity. The options are self observation, correlation, natural conditions observation, or experiment.
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First, I need to recall what each of these methods entails. Self observation is when the researcher observes themselves, but that's not really a stand
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... [truncated 823 chars] ...
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ferent amounts. But even then, an experiment would be the way to go. Correlation would just show a relationship but not causation. So the answer should be D. Experiment. But wait, maybe they are considering natural conditions? But experiments are more controlled. So I think D is the answer.
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</think>
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La ricerca che mira a determinare se la quantità di alcool ha un effetto sulla capacità di memorizzazione richiede un metodo che permetta di stabilire una relazione causale.
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**Esperimento** è il metodo più appropriato in questo caso, poiché permette di manipolare la variabile indipendente (la quantità di alcool) e di misurare l'effetto su una variabile dipendente (la capacità di memorizzazione), controllando altre variabili. Questo approccio garantisce che i risultati siano più affidabili e meno soggetti a confounding.
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I metodi alternativi, come la **correlazione** (B), rilevano solo una relazione tra due variabili senza dimostrare causazione. L'**osservazione in condizioni naturali** (C) è meno controllata e non è adatta per studi di causalità. L'**autoosservazione** (A) non è un metodo scientifico standard per studi di questo tipo.
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**Risposta corretta: D. esperimento**
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\boxed{D}
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```
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**Incorrect** (1 shown)
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- **reference**: `C`
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- **overall** (0/1 completions correct)
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- **extracted** (✗): `D`
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- **completion**:
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```text
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<think>
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嗯,我现在要解决这个问题,题目是问为什么婴儿在出生后的最初几周视力不佳,然后给出四个选项。我需要仔细分析每个选项,找出最合理的解释。
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首先,我得回忆一下婴儿视觉发育的相关知识。婴儿出生时,他们的视力确实比较差,尤其是在出生后的最初几周。这可能是因为他们的视觉系统还在发育中,需要时间来成熟。
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现在看选项:
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A选项说轴突被一层致密的脂质覆盖,阻碍神经活动。这听起来像是神经发育的问题,但不确定是否与视力有关。轴突的脂质覆盖可能影响神经信号传递,但婴儿出生后的神经发育可能需要时间才能完全成熟,所以这个可能是一个因素,但不确定是否是主要原因。
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B选项提到神经元的突触间隙充满了眼房水。眼房水是眼球内的液体,可能在出生后暂时填充了突触间隙,这可能影响神经信号的传递?不过,眼房水的正常存在可能在出生后逐渐被吸收,或者可能在某些情况下影响视觉,但不确定是否是主要原因。
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C选项说与初
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... [truncated 823 chars] ...
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形成,所以如果尚未形成,可能影响视觉。因此选项D正确。
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不过,可能选项C更正确,因为初级视觉皮层的神经连接未完全连接,导致视觉信号无法被处理。例如,婴儿的视觉系统需要时间建立神经连接,所以出生后的几周内,这些连接尚未完全形成,导致视力不佳。
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这时候可能需要更仔细的思考。例如,视网膜的神经节细胞轴突在视网膜中的投射(朗维耶节点)在出生后几周内形成,而初级视觉皮层的神经连接可能在出生后几周内也形成。所以,如果选项C说的是初级视觉���层的神经连接未完全连接,那么可能在出生后的几周内,这些连接尚未形成,导致视觉信号无法被处理。
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不过,可能选项D更直接关联到视觉信号传递的问题,而选项C可能涉及神经连接的问题。
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综上,可能选项D更正确,即朗维耶节点尚未形成,导致视觉信号无法传递到大脑,因此视力不佳。或者选项C更正确。
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不过,根据常见的医学知识,婴儿出生后的视觉发育中,视网膜的神经节细胞轴突在视网膜中的投射(朗维耶节点)在出生后几周内逐渐形成,所以如果尚未形成,可能影响视觉。因此选项D正确。
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不过,可能我在这里混淆了术语。例如,朗维耶节点可能指的是视网膜神经节细胞的轴突在视网膜中的投射,而这些投射在出生后几周内形成,所以如果尚未形成,可能影响视觉。因此选项D正确。
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因此,我认为正确答案是选项D。
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</think>
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在婴儿出生后的最初几周,视力不佳的主要原因与视觉信号传递的发育过程密切相关。婴儿的视觉系统在出生后需要时间逐步成熟,尤其是在视网膜神经节细胞的轴突在视网膜中的投射(即朗维耶节点)形成的过程中。
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---
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### 分析选项:
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- **A. 轴突被一层致密的脂质覆盖,这阻碍了神经活动。**
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虽然轴突的脂质覆盖可能影响神经信号传递,但这种现象在出生后几周内通常会逐渐被清除,因此不是视力不佳的主要原因。
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- **B. 神经元的突触间隙充满了眼房水。**
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眼房水的填充在出生后几周内会逐渐减少,对视觉信号传递的影响较小,因此不是主要原因。
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- **C. 与初级视觉皮层的神经连接并没有完全连接。**
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这个选项描述的是神经连接的发育问题,但婴儿的视觉系统在出生后几周内,视网膜神经节细胞的轴突在视网膜中的投射(朗维耶节点)尚未完全形成,这是导致视力不佳的关键原因,而不是初级视觉皮层的神经连接问题。
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- **D. 朗维耶节点还没有形成。**
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朗维耶节点指的是视网膜神经节细胞的轴突在视网膜中的投射,这些投射在出生后几周内逐渐形成。如果尚未形成,视觉信号无法从视网膜传递到大脑的初级视觉皮层,从而导致视力不佳。这是最直接、最根本的原因。
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---
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### 结论:
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在婴儿出生后的最初几周,视力不佳的主要原因是**朗维耶节点尚未形成**,这阻碍了视觉信号从视网膜传递到大脑,导致视觉功能尚未完全发育。
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$$
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\boxed{D}
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$$
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```
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