Text Generation
Transformers
Safetensors
qwen3
Generated from Trainer
trl
sft
conversational
text-generation-inference
Instructions to use cs-552-2026-ChatMODS/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-ChatMODS/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-ChatMODS/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-ChatMODS/general_knowledge_model") model = AutoModelForCausalLM.from_pretrained("cs-552-2026-ChatMODS/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-ChatMODS/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-ChatMODS/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-ChatMODS/general_knowledge_model", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/cs-552-2026-ChatMODS/general_knowledge_model
- SGLang
How to use cs-552-2026-ChatMODS/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-ChatMODS/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-ChatMODS/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-ChatMODS/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-ChatMODS/general_knowledge_model", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use cs-552-2026-ChatMODS/general_knowledge_model with Docker Model Runner:
docker model run hf.co/cs-552-2026-ChatMODS/general_knowledge_model
Update Automated MNLP evaluation report (2026-06-10)
Browse files- EVAL_REPORT.md +15 -17
EVAL_REPORT.md
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- **Model repo:** [`cs-552-2026-ChatMODS/general_knowledge_model`](https://huggingface.co/cs-552-2026-ChatMODS/general_knowledge_model)
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- **Owner(s):** group **ChatMODS**
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- **Generated at:** 2026-06-
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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:** [`
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- **Message:** M3: GK
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- **Committed:** 2026-06-
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## Summary
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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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- **overall** (1/1 completions correct)
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- **completion**:
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```text
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Answer: $\boxed{D}$
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```
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**Incorrect** (1 shown)
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- **overall** (0/1 completions correct)
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- **completion**:
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```text
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Answer: \boxed{I}
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```
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- **Model repo:** [`cs-552-2026-ChatMODS/general_knowledge_model`](https://huggingface.co/cs-552-2026-ChatMODS/general_knowledge_model)
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- **Owner(s):** group **ChatMODS**
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- **Generated at:** 2026-06-10T21:20:55+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:** [`cb79f21`](https://huggingface.co/cs-552-2026-ChatMODS/general_knowledge_model/commit/cb79f21acf8a6a2cbb982d5f1386bb562cabd293)
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- **Message:** M3: GK lora v2 50pct local fixed generation config
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- **Committed:** 2026-06-09T18:20:07+00:00
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## Summary
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| Benchmark | Accuracy | Status |
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| Math | β | not run |
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| Knowledge | 0.2800 | 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**: `A`
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- **overall** (1/1 completions correct)
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- **extracted** (β): `A`
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- **completion**:
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```text
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Let me analyze each option:
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The correct answer is A. No, the school exercised its appropriate function of trying to restrict the use of vulgarity and offensive discourse in a high school setting where free speech may be limited..
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\boxed{A}
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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** (β): `B`
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- **completion**:
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```text
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Let me analyze each option:
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The correct answer is B.
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\boxed{B}
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```
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