Text Generation
Transformers
Safetensors
qwen3
Generated from Trainer
grpo
trl
conversational
text-generation-inference
Instructions to use cs-552-2026-MMRF/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-MMRF/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-MMRF/general_knowledge_model") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("cs-552-2026-MMRF/general_knowledge_model") model = AutoModelForCausalLM.from_pretrained("cs-552-2026-MMRF/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=256) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use cs-552-2026-MMRF/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-MMRF/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-MMRF/general_knowledge_model", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/cs-552-2026-MMRF/general_knowledge_model
- SGLang
How to use cs-552-2026-MMRF/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-MMRF/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-MMRF/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-MMRF/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-MMRF/general_knowledge_model", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use cs-552-2026-MMRF/general_knowledge_model with Docker Model Runner:
docker model run hf.co/cs-552-2026-MMRF/general_knowledge_model
Update Automated MNLP evaluation report (2026-06-09)
Browse files- EVAL_REPORT.md +10 -98
EVAL_REPORT.md
CHANGED
|
@@ -2,23 +2,23 @@
|
|
| 2 |
|
| 3 |
- **Model repo:** [`cs-552-2026-MMRF/general_knowledge_model`](https://huggingface.co/cs-552-2026-MMRF/general_knowledge_model)
|
| 4 |
- **Owner(s):** group **MMRF**
|
| 5 |
-
- **Generated at:** 2026-06-
|
| 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._
|
| 9 |
|
| 10 |
## Evaluated checkpoint
|
| 11 |
|
| 12 |
-
- **Commit:** [`
|
| 13 |
- **Message:** Upload folder using huggingface_hub
|
| 14 |
-
- **Committed:** 2026-06-
|
| 15 |
|
| 16 |
## Summary
|
| 17 |
|
| 18 |
| Benchmark | Accuracy | Status |
|
| 19 |
|---|---:|---|
|
| 20 |
| Math | — | not run |
|
| 21 |
-
| Knowledge | 0.
|
| 22 |
| Multilingual | — | not run |
|
| 23 |
| Safety | — | not run |
|
| 24 |
|
|
@@ -30,110 +30,22 @@ _Prompts are intentionally omitted to avoid revealing benchmark contents. For mu
|
|
| 30 |
|
| 31 |
**Correct** (1 shown)
|
| 32 |
|
| 33 |
-
- **reference**: `
|
| 34 |
- **overall** (1/1 completions correct)
|
| 35 |
-
- **extracted** (✓): `
|
| 36 |
- **completion**:
|
| 37 |
|
| 38 |
```text
|
| 39 |
-
|
| 40 |
-
Okay, let's try to figure this out. The question is about whether a state law that bans non-physicians from selling hearing aids is constitutional. The key points here are the statute's provisions and the legal challenges.
|
| 41 |
-
|
| 42 |
-
First, the state passed a law that prohibits non-physicians from selling hearing aids. However, the law says that non-physicians already in the business aren't affected
|
| 43 |
-
|
| 44 |
-
... [truncated 822 chars] ...
|
| 45 |
-
|
| 46 |
-
ness but only prevents them from entering the market.
|
| 47 |
-
|
| 48 |
-
- **G. Constitutional, because it falls under the state's power to regulate healthcare and related services**: This is true but does not directly address the core issue of the law's constitutionality.
|
| 49 |
-
|
| 50 |
-
- **H. Constitutional, because it does not infringe upon the rights of non-physicians not already in the business**: This is not accurate. The law does not infringe on the rights of those not already in the business.
|
| 51 |
-
|
| 52 |
-
- **I. Constitutional, because it is a state's power to regulate any phase of local business, even though such regulations may have some effect on interstate commerce, provided that Congress has not enacted legislation regarding the subject matter**: This is not accurate. The law is not a matter of Congress's jurisdiction.
|
| 53 |
-
|
| 54 |
-
---
|
| 55 |
-
|
| 56 |
-
### Final Determination:
|
| 57 |
-
The law is most likely **constitutional** if it is seen as a rational and necessary measure to protect seniors from exploitation by non-physicians who are not already in the business of selling hearing aids. The distinction between non-physicians who are already in the industry and those who are not is rational and serves a compelling state interest.
|
| 58 |
-
|
| 59 |
-
$$
|
| 60 |
-
\boxed{D}
|
| 61 |
-
$$
|
| 62 |
```
|
| 63 |
|
| 64 |
**Incorrect** (1 shown)
|
| 65 |
|
| 66 |
-
- **reference**: `
|
| 67 |
- **overall** (0/1 completions correct)
|
| 68 |
-
- **extracted** (✗): `
|
| 69 |
- **completion**:
|
| 70 |
|
| 71 |
```text
|
| 72 |
-
|
| 73 |
-
Okay, let's see. I need to find the heat transfer coefficient for water flowing through a pipe using Sieder and Tate's formula and Hausen's formula. The problem gives me the flow rate, pipe diameter, length, inlet and outlet temperatures, and the assumption that the flow is laminar.
|
| 74 |
-
|
| 75 |
-
First, I should recall the formulas for both Sieder and Tate and Hausen. I remember that Sieder and Tate's
|
| 76 |
-
|
| 77 |
-
... [truncated 823 chars] ...
|
| 78 |
-
|
| 79 |
-
3 \cdot \text{Re}^{0.8} \cdot \text{Pr}^{0.333}
|
| 80 |
-
$$
|
| 81 |
-
|
| 82 |
-
$$
|
| 83 |
-
\text{Nu} \approx 0.023 \cdot (23370)^{0.8} \cdot 1^{0.333} \approx 0.023 \cdot 3142 \approx 72.3
|
| 84 |
-
$$
|
| 85 |
-
|
| 86 |
-
---
|
| 87 |
-
|
| 88 |
-
### **Step 4: Calculate the Heat Transfer Coefficient (h)**
|
| 89 |
-
|
| 90 |
-
$$
|
| 91 |
-
h = \frac{\text{Nu} \cdot k}{D} = \frac{72.3 \cdot 0.585}{0.0492} \approx 857 \text{ Btu/hr-ft}^2\degree\text{F}
|
| 92 |
-
$$
|
| 93 |
-
|
| 94 |
-
---
|
| 95 |
-
|
| 96 |
-
### **Step 5: Apply Sieder and Tate's Formula**
|
| 97 |
-
|
| 98 |
-
$$
|
| 99 |
-
\text{Sieder and Tate} = 0.023 \cdot (23370)^{0.8} \cdot 1^{0.333} \approx 60.73 \text{ Btu/hr-ft}^2\degree\text{F}
|
| 100 |
-
$$
|
| 101 |
-
|
| 102 |
-
---
|
| 103 |
-
|
| 104 |
-
### **Step 6: Apply Hausen's Formula**
|
| 105 |
-
|
| 106 |
-
**Hausen's formula for laminar flow:**
|
| 107 |
-
|
| 108 |
-
$$
|
| 109 |
-
\text{Nu} = 4.36 \cdot \text{Re}^{0.5} \cdot \text{Pr}^{0.333}
|
| 110 |
-
$$
|
| 111 |
-
|
| 112 |
-
$$
|
| 113 |
-
\text{Nu} \approx 4.36 \cdot \sqrt{23370} \cdot 1^{0.333} \approx 65.73
|
| 114 |
-
$$
|
| 115 |
-
|
| 116 |
-
$$
|
| 117 |
-
h = \frac{65.73 \cdot 0.585}{0.0492} \approx 52.66 \text{ Btu/hr-ft}^2\degree\text{F}
|
| 118 |
-
$$
|
| 119 |
-
|
| 120 |
-
---
|
| 121 |
-
|
| 122 |
-
### **Final Answer:**
|
| 123 |
-
|
| 124 |
-
- **Sieder and Tate's formula:** $\boxed{60.73}$ Btu/hr-ft²-°F
|
| 125 |
-
- **Hausen's formula:** $\boxed{52.66}$ Btu/hr-ft²-°F
|
| 126 |
-
|
| 127 |
-
---
|
| 128 |
-
|
| 129 |
-
### **Conclusion:**
|
| 130 |
-
|
| 131 |
-
The heat transfer coefficient for the given conditions is:
|
| 132 |
-
|
| 133 |
-
- **Sieder and Tate's formula:** 60.73 Btu/hr-ft²-°F
|
| 134 |
-
- **Hausen's formula:** 52.66 Btu/hr-ft²-°F
|
| 135 |
-
|
| 136 |
-
$$
|
| 137 |
-
\boxed{A}
|
| 138 |
-
$$
|
| 139 |
```
|
|
|
|
| 2 |
|
| 3 |
- **Model repo:** [`cs-552-2026-MMRF/general_knowledge_model`](https://huggingface.co/cs-552-2026-MMRF/general_knowledge_model)
|
| 4 |
- **Owner(s):** group **MMRF**
|
| 5 |
+
- **Generated at:** 2026-06-09T09:42:46+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._
|
| 9 |
|
| 10 |
## Evaluated checkpoint
|
| 11 |
|
| 12 |
+
- **Commit:** [`6ea1219`](https://huggingface.co/cs-552-2026-MMRF/general_knowledge_model/commit/6ea121935875e775bec1684f1f92390e0c9cd9a4)
|
| 13 |
- **Message:** Upload folder using huggingface_hub
|
| 14 |
+
- **Committed:** 2026-06-08T13:21:07+00:00
|
| 15 |
|
| 16 |
## Summary
|
| 17 |
|
| 18 |
| Benchmark | Accuracy | Status |
|
| 19 |
|---|---:|---|
|
| 20 |
| Math | — | not run |
|
| 21 |
+
| Knowledge | 0.3700 | ok |
|
| 22 |
| Multilingual | — | not run |
|
| 23 |
| Safety | — | not run |
|
| 24 |
|
|
|
|
| 30 |
|
| 31 |
**Correct** (1 shown)
|
| 32 |
|
| 33 |
+
- **reference**: `F`
|
| 34 |
- **overall** (1/1 completions correct)
|
| 35 |
+
- **extracted** (✓): `F`
|
| 36 |
- **completion**:
|
| 37 |
|
| 38 |
```text
|
| 39 |
+
\boxed{F}
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 40 |
```
|
| 41 |
|
| 42 |
**Incorrect** (1 shown)
|
| 43 |
|
| 44 |
+
- **reference**: `B`
|
| 45 |
- **overall** (0/1 completions correct)
|
| 46 |
+
- **extracted** (✗): `D`
|
| 47 |
- **completion**:
|
| 48 |
|
| 49 |
```text
|
| 50 |
+
\boxed{D}
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 51 |
```
|