Instructions to use cs-552-2026-4neurons/general_knowledge_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cs-552-2026-4neurons/general_knowledge_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="cs-552-2026-4neurons/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-552-2026-4neurons/general_knowledge_model") model = AutoModelForCausalLM.from_pretrained("cs-552-2026-4neurons/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-552-2026-4neurons/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-552-2026-4neurons/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-552-2026-4neurons/general_knowledge_model", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/cs-552-2026-4neurons/general_knowledge_model
- SGLang
How to use cs-552-2026-4neurons/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-552-2026-4neurons/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-552-2026-4neurons/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-552-2026-4neurons/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-552-2026-4neurons/general_knowledge_model", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use cs-552-2026-4neurons/general_knowledge_model with Docker Model Runner:
docker model run hf.co/cs-552-2026-4neurons/general_knowledge_model
Automated MNLP evaluation report (2026-05-18)
#2
by zechen-nlp - opened
- EVAL_REPORT.md +9 -9
EVAL_REPORT.md
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- **Model repo:** [`cs-552-2026-4neurons/general_knowledge_model`](https://huggingface.co/cs-552-2026-4neurons/general_knowledge_model)
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- **Owner(s):** group **4neurons**
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- **Generated at:** 2026-05-
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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** β you do not need to merge it. The next nightly run will refresh this file._
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| Benchmark | Accuracy | Status |
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| Math | β | not run |
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| Knowledge | 0.
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| Multilingual | β | not run |
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| Safety | β | not run |
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**Correct** (1 shown)
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- **reference**: `
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- **overall** (1/1 completions correct)
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- **extracted** (β): `
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- **completion**:
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```text
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<think>
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</think>
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The answer is \boxed{
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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** (β): `
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- **completion**:
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```text
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<think>
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In
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</think>
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The answer is \boxed{
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```
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- **Model repo:** [`cs-552-2026-4neurons/general_knowledge_model`](https://huggingface.co/cs-552-2026-4neurons/general_knowledge_model)
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- **Owner(s):** group **4neurons**
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- **Generated at:** 2026-05-18T02:30:25+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** β you do not need to merge it. The next nightly run will refresh this file._
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| Benchmark | Accuracy | Status |
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| Math | β | not run |
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| Knowledge | 0.3700 | ok |
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| Multilingual | β | not run |
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| Safety | β | not run |
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**Correct** (1 shown)
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- **reference**: `F`
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- **overall** (1/1 completions correct)
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- **extracted** (β): `F`
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- **completion**:
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```text
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<think>
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A rising national debt increases the demand for U.S. dollars because foreign investors and domestic holders of U.S. debt are willing to hold more dollars in exchange for the safety and liquidity of U.S. government bonds. This increased demand shifts the demand curve for dollars to the right. As demand increases, the value of the dollar relative to other currencies depreciates because there is less demand for dollars compared to foreign currencies. This relationship reflects the inverse relationship between currency demand and its exchange rate.
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</think>
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The answer is \boxed{F}
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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** (β): `B`
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- **completion**:
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```text
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<think>
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In Reaction I, the (S)-5-methoxyhexan-3-one is reduced to a primary alcohol using LAH, resulting in a single product. In Reaction II, pentane-2,4-dione is reduced to a secondary alcohol using NaBH4, which results in a single product as well. Both reactions proceed to 100% completion, meaning all reactants are converted to their respective alcohols. In normal-phase HPLC, the single product from each reaction will produce a single peak, resulting in two peaks in total. In chiral HPLC, the enantiomers of the products will be separated, producing two peaks for each reaction, resulting in four peaks in total.
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</think>
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The answer is \boxed{B}
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```
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