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
Update Automated MNLP evaluation report (2026-05-16)
Browse files- EVAL_REPORT.md +8 -71
EVAL_REPORT.md
CHANGED
|
@@ -2,7 +2,7 @@
|
|
| 2 |
|
| 3 |
- **Model repo:** [`cs-552-2026-flab/general_knowledge_model`](https://huggingface.co/cs-552-2026-flab/general_knowledge_model)
|
| 4 |
- **Owner(s):** group **flab**
|
| 5 |
-
- **Generated at:** 2026-05-
|
| 6 |
- **Pipeline:** [mnlp-project-ci](https://github.com/eric11eca/mnlp-project-ci)
|
| 7 |
|
| 8 |
_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._
|
|
@@ -12,7 +12,7 @@ _This PR is opened automatically by the course CI. It is **non-blocking** — yo
|
|
| 12 |
| Benchmark | Accuracy | Status |
|
| 13 |
|---|---:|---|
|
| 14 |
| Math | — | not run |
|
| 15 |
-
| Knowledge | 0.
|
| 16 |
| Multilingual | — | not run |
|
| 17 |
| Safety | — | not run |
|
| 18 |
|
|
@@ -24,85 +24,22 @@ _Prompts are intentionally omitted to avoid revealing benchmark contents. For mu
|
|
| 24 |
|
| 25 |
**Correct** (1 shown)
|
| 26 |
|
| 27 |
-
- **reference**: `
|
| 28 |
- **overall** (1/1 completions correct)
|
| 29 |
-
- **extracted** (✓): `
|
| 30 |
- **completion**:
|
| 31 |
|
| 32 |
```text
|
| 33 |
-
|
| 34 |
-
|
| 35 |
-
- **Lorentz symmetry** (to ensure the consistency of spacetime transformations)
|
| 36 |
-
- **Poincare symmetry** (which includes Lorentz and translational symmetry, essential for the structure of the theory)
|
| 37 |
-
- **CPT symmetry** (a fundamental symmetry in quantum field theory, required for consistency)
|
| 38 |
-
|
| 39 |
-
However, **CP symmetry** is **not** required to be obeyed by every operator in SMEFT. In fact, SMEFT is designed to be **CP-violating**, and it allows for certain CP-violating operators that are not forbidden by the symmetries of the Standard Model.
|
| 40 |
-
|
| 41 |
-
---
|
| 42 |
-
|
| 43 |
-
### Correct Answer:
|
| 44 |
-
|
| 45 |
-
**C. 1, 2, 4**
|
| 46 |
-
|
| 47 |
-
These are the symmetries that **must be obeyed by every operator** in SMEFT.
|
| 48 |
-
|
| 49 |
-
---
|
| 50 |
-
|
| 51 |
-
### Explanation of Choices:
|
| 52 |
-
|
| 53 |
-
- **A. 1, 2**: Incorrect, because **CPT symmetry** is also required.
|
| 54 |
-
- **B. 1, 3, 4**: Incorrect, because **CP symmetry** is not required.
|
| 55 |
-
- **C. 1, 2, 4**: Correct, as **Lorentz**, **Poincare**, and **CPT** are required.
|
| 56 |
-
- **D. 3, 4**: Incorrect, because **CP symmetry** is not required.
|
| 57 |
-
|
| 58 |
-
---
|
| 59 |
-
|
| 60 |
-
### Final Answer:
|
| 61 |
-
$$
|
| 62 |
-
\boxed{\text{C}}
|
| 63 |
-
$$
|
| 64 |
```
|
| 65 |
|
| 66 |
**Incorrect** (1 shown)
|
| 67 |
|
| 68 |
-
- **reference**: `
|
| 69 |
- **overall** (0/1 completions correct)
|
| 70 |
-
- **extracted** (✗): `
|
| 71 |
- **completion**:
|
| 72 |
|
| 73 |
```text
|
| 74 |
-
|
| 75 |
-
|
| 76 |
-
Let’s evaluate the options:
|
| 77 |
-
|
| 78 |
-
-
|
| 79 |
-
|
| 80 |
-
... [truncated 764 chars] ...
|
| 81 |
-
|
| 82 |
-
ong candidate**, but **cDNA sequencing is not the first step** in a rapid diagnostic kit.
|
| 83 |
-
|
| 84 |
-
---
|
| 85 |
-
|
| 86 |
-
### **Option C:**
|
| 87 |
-
> **First identify the virus using patient symptoms, then design a nested PCR kit.**
|
| 88 |
-
|
| 89 |
-
- **Issue:** Symptoms are **not reliable** for identifying a specific retrovirus.
|
| 90 |
-
- **Nested PCR** is **not a standard method** for retrovirus detection.
|
| 91 |
-
- **Not a good strategy** for rapid and accurate diagnosis.
|
| 92 |
-
|
| 93 |
-
---
|
| 94 |
-
|
| 95 |
-
### **Option D:**
|
| 96 |
-
> **First perform DNA sequencing, then develop a PCR kit for quick and accurate diagnosis.**
|
| 97 |
-
|
| 98 |
-
- **Issue:** Retroviruses are **RNA viruses**, so **DNA sequencing** is not the first step for detection.
|
| 99 |
-
- **PCR** is **effective** for detecting **viral RNA or DNA**, but **DNA sequencing** is **not the first step** in a rapid diagnostic kit.
|
| 100 |
-
- **Not the best approach** for rapid detection.
|
| 101 |
-
|
| 102 |
-
---
|
| 103 |
-
|
| 104 |
-
### ✅ **Correct Answer:**
|
| 105 |
-
**B. First you identify the virus by performing cDNA sequencing. You then develop a real-time PCR kit for quick and accurate diagnosis.**
|
| 106 |
-
|
| 107 |
-
This is the **most appropriate strategy** for designing a **molecular diagnostic kit** for a **retrovirus**, as it allows for **direct detection of the virus's genetic material** using **real-time PCR**.
|
| 108 |
```
|
|
|
|
| 2 |
|
| 3 |
- **Model repo:** [`cs-552-2026-flab/general_knowledge_model`](https://huggingface.co/cs-552-2026-flab/general_knowledge_model)
|
| 4 |
- **Owner(s):** group **flab**
|
| 5 |
+
- **Generated at:** 2026-05-16T04:57:46+00:00 (UTC)
|
| 6 |
- **Pipeline:** [mnlp-project-ci](https://github.com/eric11eca/mnlp-project-ci)
|
| 7 |
|
| 8 |
_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._
|
|
|
|
| 12 |
| Benchmark | Accuracy | Status |
|
| 13 |
|---|---:|---|
|
| 14 |
| Math | — | not run |
|
| 15 |
+
| Knowledge | 0.2200 | ok |
|
| 16 |
| Multilingual | — | not run |
|
| 17 |
| Safety | — | not run |
|
| 18 |
|
|
|
|
| 24 |
|
| 25 |
**Correct** (1 shown)
|
| 26 |
|
| 27 |
+
- **reference**: `G`
|
| 28 |
- **overall** (1/1 completions correct)
|
| 29 |
+
- **extracted** (✓): `G`
|
| 30 |
- **completion**:
|
| 31 |
|
| 32 |
```text
|
| 33 |
+
Final answer: \boxed{G}
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 34 |
```
|
| 35 |
|
| 36 |
**Incorrect** (1 shown)
|
| 37 |
|
| 38 |
+
- **reference**: `C`
|
| 39 |
- **overall** (0/1 completions correct)
|
| 40 |
+
- **extracted** (✗): `A`
|
| 41 |
- **completion**:
|
| 42 |
|
| 43 |
```text
|
| 44 |
+
Final answer: \boxed{A}
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 45 |
```
|