Text Generation
Transformers
Safetensors
qwen3
Generated from Trainer
sft
trl
conversational
text-generation-inference
Instructions to use cs-552-2026-the-transformers/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-the-transformers/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-the-transformers/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-the-transformers/general_knowledge_model") model = AutoModelForCausalLM.from_pretrained("cs-552-2026-the-transformers/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-the-transformers/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-the-transformers/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-the-transformers/general_knowledge_model", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/cs-552-2026-the-transformers/general_knowledge_model
- SGLang
How to use cs-552-2026-the-transformers/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-the-transformers/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-the-transformers/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-the-transformers/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-the-transformers/general_knowledge_model", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use cs-552-2026-the-transformers/general_knowledge_model with Docker Model Runner:
docker model run hf.co/cs-552-2026-the-transformers/general_knowledge_model
Automated MNLP evaluation report (2026-06-11)
#1
by zechen-nlp - opened
- EVAL_REPORT.md +110 -0
EVAL_REPORT.md
ADDED
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| 1 |
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# Automated MNLP evaluation report
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| 2 |
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- **Model repo:** [`cs-552-2026-the-transformers/general_knowledge_model`](https://huggingface.co/cs-552-2026-the-transformers/general_knowledge_model)
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- **Owner(s):** group **the-transformers**
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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** β 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:** [`a4ce155`](https://huggingface.co/cs-552-2026-the-transformers/general_knowledge_model/commit/a4ce155f8f52f41e8bf577b2d750a53186729715)
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- **Message:** Upload general knowledge SFT checkpoint
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- **Committed:** 2026-06-08T18:41:16+00:00
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## Summary
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| Benchmark | Accuracy | Status |
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|---|---:|---|
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| Math | β | not run |
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| Knowledge | 0.4200 | ok |
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| Multilingual | β | not run |
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| Safety | β | 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** (β): `B`
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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 this out. The question is about determining the correct 1H NMR data for a specific compound. Let me start by recalling what each part of the NMR data tells us.
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First, the compound is a di-substituted 6-membered aromatic ring with 8 carbon atoms. That means the aromatic ring is a benzene ring, but since it's di-substituted, there are two substituents. The ring has
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... [truncated 822 chars] ...
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th aromatic protons.
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- **Option B**:
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- Aromatic protons at **7.8 (2H, d)** and **7.6 (2H, d)** β again, consistent with **two aromatic protons**.
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- The **s at 2.3 ppm** is consistent with **two CH3 groups** (each at 2.3 ppm).
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- The **carbonyl group** is at **1700β1750 ppm**, which is typical for a carbonyl group.
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- This configuration is **chemically plausible** for a substituted aromatic ring with **two substituents**, and the **aromatic protons are double-decoupled**, matching the expected pattern for a di-substituted aromatic ring.
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- **Option C**:
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- Aromatic protons at **4.8 (2H, d)** and **4.6 (2H, d)** β **too low** for aromatic protons (typically around 6.5β8.5 ppm), so inconsistent with aromatic structure.
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- **Option D**:
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- Similar to Option C, aromatic protons are **too low** for aromatic protons and **not consistent** with the presence of a carbonyl group.
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---
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### Conclusion:
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Option **B** provides the most chemically consistent and plausible NMR data for a **di-substituted aromatic ring** with a **carbonyl group** and an **aromatic-halogen bond**. It matches all expected chemical shifts and splitting patterns for such a compound.
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$$
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\boxed{B}
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$$
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```
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**Incorrect** (1 shown)
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- **reference**: `I`
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- **overall** (0/1 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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Okay, let's see. The question is about executory contracts and which statement is least correct. First, I need to remember the definitions related to executory contracts.
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An executory contract is one where one party has already performed, and the other hasn't yet begun. So, for example, if Party A has delivered a product to Party B, but Party B hasn't accepted it yet. That would be an ex
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... [truncated 822 chars] ...
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yet begun performance.*
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β
Correct. This matches the definition of an executory contract.
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- **D.** *A wholly executory contract is where only promises have been exchanged and there has been no performance by either party.*
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β
Correct. This is the definition of a wholly executory contract.
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- **E.** *A partially executed contract means that at least one party has begun performance.*
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β
Correct. This is consistent with the definition of a partially executed contract.
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- **F.** *An executory contract is when both parties have yet to begin performance.*
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β **Incorrect.** An executory contract is not one where **both parties have yet to begin** performance. It is one where **one has begun**, and the **other has not**. Therefore, this statement is **false**.
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- **G.** *A fully executed contract is when both parties have completed their performance.*
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β
Correct. This is the definition of a fully executed contract.
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---
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### Conclusion:
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Among all the options, **F** is the **least correct** because it incorrectly defines an executory contract. It suggests that both parties have not yet begun performance, which is **not** the case for an executory contract.
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$$
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\boxed{F}
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$$
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```
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