Text Generation
Transformers
Safetensors
qwen3
Generated from Trainer
trl
grpo
conversational
text-generation-inference
Instructions to use cs-552-2026-llmfao/general_knowledge_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cs-552-2026-llmfao/general_knowledge_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="cs-552-2026-llmfao/general_knowledge_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-llmfao/general_knowledge_model") model = AutoModelForCausalLM.from_pretrained("cs-552-2026-llmfao/general_knowledge_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-llmfao/general_knowledge_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-llmfao/general_knowledge_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-llmfao/general_knowledge_model", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/cs-552-2026-llmfao/general_knowledge_model
- SGLang
How to use cs-552-2026-llmfao/general_knowledge_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-llmfao/general_knowledge_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-llmfao/general_knowledge_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-llmfao/general_knowledge_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-llmfao/general_knowledge_model", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use cs-552-2026-llmfao/general_knowledge_model with Docker Model Runner:
docker model run hf.co/cs-552-2026-llmfao/general_knowledge_model
Automated MNLP evaluation report (2026-06-11)
#1
by zechen-nlp - opened
- EVAL_REPORT.md +149 -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-llmfao/general_knowledge_model`](https://huggingface.co/cs-552-2026-llmfao/general_knowledge_model)
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- **Owner(s):** group **llmfao**
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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:** [`a2d1d7f`](https://huggingface.co/cs-552-2026-llmfao/general_knowledge_model/commit/a2d1d7fe9856a5ed5ba10b6fb98d305d77ca0456)
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- **Message:** Upload ./general_knowledge/model/qwen-GN-model-08_06_20h_15/merged
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- **Committed:** 2026-06-09T20:48:33+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 | 0.2300 | ok |
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| Multilingual | — | not run |
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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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### Knowledge
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**Correct** (1 shown)
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- **reference**: `B`
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- **overall** (1/1 completions correct)
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- **extracted** (✓): `B`
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- **completion**:
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```text
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<think>
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['The Hamiltonian operator is given by $H = \\varepsilon \\vec{\\sigma} \\cdot \\vec{n}$.', 'The Pauli matrices have eigenvalues 1 and -1.', 'So, the Hamiltonian has eigenvalues $\\varepsilon (1 \\cdot \\vec{n}) = \\varepsilon$ and $\\varepsilon (-1 \\cdot \\vec{n}) = -\\varepsilon$.', 'The answer is (B).']
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</think>
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\boxed{B}
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```
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**Incorrect** (1 shown)
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- **reference**: `A`
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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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['The speed of light in a medium is given by $v_{glass} = \n\\frac{c}{n}$.', 'The velocity of the glass in the laboratory frame is $v$.', 'Since the velocity of the glass is $v$, the velocity of the light in the glass is $v_{glass} = \n\\frac{c}{n} = \n\\frac{v}{1+v/v_glass}$.', 'However, since the glass is moving with velocity $v$ in the laboratory frame, the velocity of the light in the
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... [truncated 812 chars] ...
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ab} = \n\\frac{c}{n} + \n\\frac{vn}{v+n}
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$$
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$$
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v_{lab} = \n\\frac{c}{n} + \n\\frac{v}{1+v/n}
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$$
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$$
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v_{lab} = \n\\frac{c}{n} + \n\\frac{vn}{v+n}
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$$
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$$
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v_{lab} = \n\\frac{c}{n} + \n\\frac{v}{1+v/n}
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$$
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$$
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v_{lab} = \n\\frac{c}{n} + \n\\frac{vn}{v+n}
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$$
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$$
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v_{lab} = \n\\frac{c}{n} + \n\\frac{v}{1+v/n}
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$$
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$$
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v_{lab} = \n\\frac{c}{n} + \n\\frac{vn}{v+n}
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$$
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$$
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v_{lab} = \n\\frac{c}{n} + \n\\frac{v}{1+v/n}
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$$
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$$
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v_{lab} = \n\\frac{c}{n} + \n\\frac{vn}{v+n}
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$$
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$$
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v_{lab} = \n\\frac{c}{n} + \n\\frac{v}{1+v/n}
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$$
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$$
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v_{lab} = \n\\frac{c}{n} + \n\\frac{vn}{v+n}
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$$
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$$
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v_{lab} = \n\\frac{c}{n} + \n\\frac{v}{1+v/n}
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$$
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$$
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v_{lab} = \n\\frac{c}{n} + \n\\frac{vn}{v+n}
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$$
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$$
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v_{lab} = \n\\frac{c}{n} + \n\\frac{v}{1+v/n}
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$$
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$$
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v_{lab} = \n\\frac{c}{n} + \n\\frac{vn}{v+n}
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$$
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$$
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v_{lab} = \n\\frac{c}{n} + \n\\frac{v}{1+v/n}
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$$
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$$
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v_{lab} = \n\\frac{c}{n} + \n\\frac{vn}{v+n}
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$$
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$$
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v_{lab} = \n\\frac{c}{n} + \n\\frac{v}{1+v/n}
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$$
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$$
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v_{lab} = \n\\frac{c}{n} + \n\\frac{vn}{v+n}
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$$
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$$
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v_{lab} = \n\\frac{c}{n} + \n\\frac{v}{1+v/n}
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$$
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$$
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v_{lab} = \n\\frac{c}{n} + \n\\frac{vn}{v+n}
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$$
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$$
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v_{lab} = \n\\frac{c}{n} + \n\\frac{v}{1+v/n}
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$$
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$$
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v_{lab} = \n\\frac{c}{n} + \n\\frac{vn}{v+n}
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$$
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| 149 |
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```
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