Instructions to use cs-552-2026-claude-bots/multilingual_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cs-552-2026-claude-bots/multilingual_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="cs-552-2026-claude-bots/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-claude-bots/multilingual_model") model = AutoModelForCausalLM.from_pretrained("cs-552-2026-claude-bots/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-claude-bots/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-claude-bots/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-claude-bots/multilingual_model", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/cs-552-2026-claude-bots/multilingual_model
- SGLang
How to use cs-552-2026-claude-bots/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-claude-bots/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-claude-bots/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-claude-bots/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-claude-bots/multilingual_model", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use cs-552-2026-claude-bots/multilingual_model with Docker Model Runner:
docker model run hf.co/cs-552-2026-claude-bots/multilingual_model
Automated MNLP evaluation report (2026-06-11)
#4
by zechen-nlp - opened
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EVAL_REPORT.md
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- **Model repo:** [`cs-552-2026-claude-bots/multilingual_model`](https://huggingface.co/cs-552-2026-claude-bots/multilingual_model)
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- **Owner(s):** group **claude-bots**
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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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- **Commit:** [`
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- **Message:** Upload checkpoint with CS-552-compatible inference artifacts
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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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**Correct** (1 shown)
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- **overall** (1/1 completions correct)
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- **completion**:
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```text
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<think>
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</think>
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```
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**Incorrect** (1 shown)
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- **reference**: `
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- **overall** (0/1 completions correct)
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- **completion**:
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```text
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<think>
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</think>
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```
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- **Model repo:** [`cs-552-2026-claude-bots/multilingual_model`](https://huggingface.co/cs-552-2026-claude-bots/multilingual_model)
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- **Owner(s):** group **claude-bots**
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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:** [`34a4c58`](https://huggingface.co/cs-552-2026-claude-bots/multilingual_model/commit/34a4c58b4ae4eec1b719150217394c92be276755)
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- **Message:** Upload checkpoint with CS-552-compatible inference artifacts
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- **Committed:** 2026-06-10T20:47:25+00:00
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## Summary
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| Math | — | not run |
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| Knowledge | — | not run |
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| Multilingual | 0.5200 | ok |
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| Safety | — | not run |
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## Sample completions
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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 asking which research method a psychologist would use to determine if the amount of alcohol affects memory capacity. The options are self-observation, correlation, natural observation, and experiment.
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First, self-observation is when the researcher observes themselves or others without interference. That doesn't sound right here because the psychologist wou
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ulating it, which isn't the case here. So C is probably not the answer.
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Experiment involves manipulating one variable (independent variable) to see its effect on another (dependent variable). To test if alcohol affects memory, the psychologist could randomly assign participants to drink varying amounts of alcohol and then measure their memory performance. This allows control over variables to establish cause and effect. So D seems like the right choice for testing causation.
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Therefore, the answer should be D.
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</think>
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To determine whether the quantity of alcohol influences memory capacity, a psychologist would likely employ an experimental design. This method involves systematically varying the independent variable (amount of alcohol) and measuring its effect on the dependent variable (memory capacity). By controlling other factors and establishing a cause-and-effect relationship, experiments allow researchers to draw valid conclusions about the impact of alcohol on memory. Unlike observational studies or correlations, experiments provide evidence of causation rather than mere associations. Therefore, the appropriate research method in this context is experimentation.
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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** (✗): `<no answer>`
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- **completion**:
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```text
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<think>
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El efecto invernadero es un proceso natural que ocurre en la atmósfera terrestre. Este proceso implica la absorción y la retención de calor solar por parte de gases como el dióxido de carbono (CO₂), el metano (CH₄), y otros, lo cual ayuda a regular la temperatura de la Tierra. La opción C menciona correctamente "natural" y "la regulación térmica", lo cual es precisamente lo que describe el efecto invernadero. Las opciones A, B, y D mencionan "artificial" o "contaminante", lo cual no es correcto porque el efecto invernadero es un proceso natural, no artificial. Además, aunque el CO₂ proveniente de fuentes artificiales puede contribuir al efecto invernadero, el proceso en sí mismo no es artificial. Por lo tanto, la opción C es la correcta.
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</think>
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El efecto invernadero es un proceso natural que ocurre en la atmósfera terrestre. Este proceso implica la absorción y la retención de calor solar por parte de gases como el dióxido de carbono (CO₂), el metano (CH₄), y otros, lo cual ayuda a regular la temperatura de la Tierra. La opción C menciona correctamente "natural" y "la regulación térmica en nuestro planeta", lo cual es precisamente lo que describe el efecto invernadero. Las opciones A, B, y D mencionan "artificial" o "contaminante", lo cual no es correcto porque el efecto invernadero es un proceso natural, no artificial. Además, aunque el CO₂ proveniente de fuentes artificiales puede contribuir al efecto invernadero, el proceso en sí mismo no es artificial. Por lo tanto, la opción C es la correcta.
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```
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