Text Generation
Transformers
Safetensors
qwen3
Generated from Trainer
sft
trl
conversational
text-generation-inference
Instructions to use cs-552-2026-flab/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-flab/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-flab/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-flab/general_knowledge_model") model = AutoModelForCausalLM.from_pretrained("cs-552-2026-flab/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-flab/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-flab/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-flab/general_knowledge_model", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/cs-552-2026-flab/general_knowledge_model
- SGLang
How to use cs-552-2026-flab/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-flab/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-flab/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-flab/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-flab/general_knowledge_model", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use cs-552-2026-flab/general_knowledge_model with Docker Model Runner:
docker model run hf.co/cs-552-2026-flab/general_knowledge_model
Automated MNLP evaluation report (2026-06-11)
#1
by zechen-nlp - opened
- EVAL_REPORT.md +100 -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-flab/general_knowledge_model`](https://huggingface.co/cs-552-2026-flab/general_knowledge_model)
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- **Owner(s):** group **flab**
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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:** [`b087c9c`](https://huggingface.co/cs-552-2026-flab/general_knowledge_model/commit/b087c9c239486a7969ce19f6c2eae209785e1763)
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- **Message:** Switch chat_template to the conservative group prompt (Knowledge CI 0.45 -> 0.51; identical weights to group_model)
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- **Committed:** 2026-06-10T18:02:30+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 | 0.4600 | 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**: `I`
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- **overall** (1/1 completions correct)
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- **extracted** (✓): `I`
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- **completion**:
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```text
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<think>
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Okay, let's tackle this problem step by step. So, we have a generator with an initial emf of 125 volts at 1,200 rpm. We need to find the new emf in two scenarios: first, when the field flux is lowered by 10% while keeping the speed the same, and second, when the speed is decreased to 1,100 rpm while keeping the flux constant.
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First, I remember that the emf (ε) generated in a generator is
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... [truncated 823 chars] ...
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Alternatively, maybe the formula is ε ∝ N * φ. So when flux is constant, ε ∝ N. So if N is decreased by 10%, then ε is decreased by 10%. So original ε is 125. 125 * 0.9 = 112.5 volts for part (a). Then for part (b), when speed is decreased to 1100 rpm, which is 11/12 of 1200 rpm. So 125 * (11/12) ≈ 114.58 volts. So that's 114.58 volts, which is approximately 114.6 volts. So option I is correct. Therefore, the answer is I.
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</think>
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The generated emf in a generator is proportional to both the speed and the flux. The formula is $ \varepsilon = k \cdot N \cdot \phi $, where $ k $ is a constant.
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**Part (a):**
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- Original emf: $ \varepsilon = 125 \, \text{V} $
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- Speed remains the same: $ N = 1200 \, \text{rpm} $
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- Flux is lowered by 10%: $ \phi' = 0.9 \cdot \phi $
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$$
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\varepsilon' = 125 \cdot \frac{1200}{1200} \cdot \frac{0.9}{1.0} = 125 \cdot 0.9 = 112.5 \, \text{V}
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$$
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**Part (b):**
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- Speed is decreased to $ 1100 \, \text{rpm} $
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- Flux remains constant: $ \phi = \phi' $
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$$
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\varepsilon' = 125 \cdot \frac{1100}{1200} \cdot \frac{1.0}{1.0} = 125 \cdot \frac{11}{12} \approx 114.58 \, \text{V}
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$$
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The closest option is **I** (a) 112.5 volts, (b) 114.6 volts.
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$$
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\boxed{I}
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$$
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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** (✗): `C`
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- **completion**:
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```text
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<think>
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Okay, let's try to figure out this problem. So, the question is about determining how many hours a person will work given certain conditions. Let me break it down step by step.
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First, the problem states that a year has 8,000 hours, but actually it's 8,760. However, we're told to use 8,000 for calculations. The person's potential market wage is $5 per hour. The uncle leaves them an annual
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... [truncated 536 chars] ...
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$1,000. The rest, which is 75% of 4,000, is $3,000, which they use for work.
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But wait, the question is about how many hours they work. So the work income is $3,000. Since the wage is $5 per hour, the number of hours worked would be 3,000 divided by 5. Let me calculate that. 3,000 divided by 5 is 600. So that would be 600 hours. But let me check again to make sure I didn't make a mistake.
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Wait, the problem says they allocate 75% of full income to leisure. So full income is $4,000. 75% of that is $3,000, which is spent on leisure, and 25% is spent on work. So yes, 25% of 4,000 is 1,000, and 75% is 3,000. Then 3,000 divided by 5 is 600 hours. So the answer should be 600, which is option C.
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But let me check the choices again. The options are from A to J. Option C is 600. So that's the answer. But wait, the problem mentions that the year has 8,000 hours. But if they work 600 hours, that's 600/8000 = 7.5% of the year. But the question is about how many hours they will work, not how many hours they spend in a year. So even though the actual year has more hours, the problem says to use 8,000 for calculation. So the answer is 600. Therefore, the correct choice is C.
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</think>
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\boxed{C}
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```
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