Instructions to use cs-552-2026-barn/multilingual_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cs-552-2026-barn/multilingual_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="cs-552-2026-barn/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-barn/multilingual_model") model = AutoModelForCausalLM.from_pretrained("cs-552-2026-barn/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-barn/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-barn/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-barn/multilingual_model", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/cs-552-2026-barn/multilingual_model
- SGLang
How to use cs-552-2026-barn/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-barn/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-barn/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-barn/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-barn/multilingual_model", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use cs-552-2026-barn/multilingual_model with Docker Model Runner:
docker model run hf.co/cs-552-2026-barn/multilingual_model
Update Automated MNLP evaluation report (2026-06-08)
Browse files- EVAL_REPORT.md +33 -19
EVAL_REPORT.md
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- **Model repo:** [`cs-552-2026-barn/multilingual_model`](https://huggingface.co/cs-552-2026-barn/multilingual_model)
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- **Owner(s):** group **barn**
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- **Generated at:** 2026-06-
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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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- **Message:** update model
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- **Committed:** 2026-06-
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## Summary
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| Math | — | not run |
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| Knowledge | — | not run |
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| Multilingual | 0.
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| Safety | — | not run |
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## Sample completions
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```text
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<think>
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$$
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\boxed{D}
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**Incorrect** (1 shown)
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- **overall** (0/1 completions correct)
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- **extracted** (✗): `<no answer>`
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- **completion**:
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```text
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<think>
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</think>
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**B. शिक्षण-अधिगम प्रक्रिया में एक महत्वपूर्ण कदम हैं ।**
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```
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- **Model repo:** [`cs-552-2026-barn/multilingual_model`](https://huggingface.co/cs-552-2026-barn/multilingual_model)
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- **Owner(s):** group **barn**
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- **Generated at:** 2026-06-08T04:40:54+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:** [`0c1bde8`](https://huggingface.co/cs-552-2026-barn/multilingual_model/commit/0c1bde8ffb653529716be5f684a9dec01c51b08f)
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- **Message:** update model
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- **Committed:** 2026-06-06T02:14:20+00:00
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## Summary
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| Math | — | not run |
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| Multilingual | 0.5000 | ok |
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## Sample completions
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```text
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<think>
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嗯,好的,我现在要解决这个问题。题目是问从1840年的鸦片战争到孙中山领导的辛亥革命,这段时间包括了多少次各民族地区的反帝斗争,最终使帝国主义瓜分中国的阴谋破产,并推翻了清朝,结束了封建君主专制统治。然后给出四个选项,A是1949年,B是1919年,C是1917年,D是1911年。
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首先,我需要明确问题中的时间范围。题目中的起始点是1840年鸦片战争,结束点是孙中山领导的辛亥革命。辛亥革命发生在1911年,对吧?因为辛亥革命是1911年爆发的,推翻了清朝,结束了封建君主专制。所以问题中的时间范围应该是从1840年到1911年。
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接下来,题目问的是在这段时间内,包括各民族地区许多次英勇的反帝斗争,最终使帝国主义瓜分中国的阴谋破产,并推翻清朝。所以问题可能是在问这个时期结束的年份,也就是辛亥革命的年份,也就是1911年,对应选项D。
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不过,我需要仔细检查每个选项是否正确。比如,选项D是1911年,而问题中的结束时间是辛亥革命,所以正确选项应该是D。其他选项中,1919年是五四运动,1917年是第一次世界大战结束,1949年是中华人民共和国成立。所以这些年份都不在题目所给的时间范围内,即1840到1911年之间。因此,正确答案应该是D选项,1911年。
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在题目中,时间范围是从1840年的鸦片战争到孙中山领导的辛亥革命。辛亥革命发生在**1911年**,标志着清朝的灭亡和中国封建君主专制统治的结束。这一时期内,各民族地区曾发生多次反帝斗争,最终使帝国主义瓜分中国的阴谋破产。
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---
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### 选项分析:
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- **A. 1949**:中华人民共和国成立的时间,远在辛亥革命之后,不属于题目所指的时间范围。
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- **B. 1919**:五四运动发生于1919年,属于新文化运动时期,与辛亥革命无关。
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- **C. 1917**:第一次世界大战结束于1918年,与辛亥革命的时间点不符。
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- **D. 1911**:辛亥革命的爆发时间,是题目中所指的“结束”时间点。
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---
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### 结论:
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题目明确指出时间范围是“从1840年到孙中山领导的辛亥革命”,而辛亥革命的年份是**1911年**。因此,正确答案为:
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$$
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\boxed{D}
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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** (✗): `<no answer>`
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- **completion**:
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```text
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<think>
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Okay, let's tackle this question. The user is asking about departmentalization and which type of structure uses it to form permanent special project groups. The options are stable structures like functional, matrix, project, or network.
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First, I need to recall what each structure entails. Functional structure is where departments are organized by functions, like marketing, finance, etc. M
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Alternatively, maybe the answer is C, project structure, but that's not possible because project groups are temporary. Therefore, the correct answer is D, matrix structure. But I'm not entirely sure. Let me think again.
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Departmentalization is the process of organizing into departments. The question is about using departmentalization to form permanent project groups. In a matrix structure, there are both functional and project teams, and the project teams can be permanent. So the answer is D. Matrix structure. So the answer is D.
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</think>
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**Answer:** D. матричных структурах
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**Explanation:**
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Департаментализация (разделение работы на отделы) применяется в матричных структурах, где формируются **действующие на постоянной основе специальные проектные группы**. В матричной структуре сочетаются функциональные отделы и проектные команды, что позволяет создавать временные и постоянные проектные группы. Хотя проектные группы в других структурах (например, в штабных или сетевых) могут быть временными, в матричной структуре они могут быть **постоянными**, так как есть возможность сочетать функциональные и проектные обязанности в одном человеке. Таким образом, правильный ответ — **D**.
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```
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