Text Generation
Transformers
Safetensors
qwen2
llama-factory
full
Generated from Trainer
conversational
text-generation-inference
Instructions to use mlfoundations-dev/SCP_40k_qwq_solution_verified with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mlfoundations-dev/SCP_40k_qwq_solution_verified with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="mlfoundations-dev/SCP_40k_qwq_solution_verified") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("mlfoundations-dev/SCP_40k_qwq_solution_verified") model = AutoModelForCausalLM.from_pretrained("mlfoundations-dev/SCP_40k_qwq_solution_verified") 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
- vLLM
How to use mlfoundations-dev/SCP_40k_qwq_solution_verified with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "mlfoundations-dev/SCP_40k_qwq_solution_verified" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "mlfoundations-dev/SCP_40k_qwq_solution_verified", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/mlfoundations-dev/SCP_40k_qwq_solution_verified
- SGLang
How to use mlfoundations-dev/SCP_40k_qwq_solution_verified 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 "mlfoundations-dev/SCP_40k_qwq_solution_verified" \ --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": "mlfoundations-dev/SCP_40k_qwq_solution_verified", "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 "mlfoundations-dev/SCP_40k_qwq_solution_verified" \ --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": "mlfoundations-dev/SCP_40k_qwq_solution_verified", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use mlfoundations-dev/SCP_40k_qwq_solution_verified with Docker Model Runner:
docker model run hf.co/mlfoundations-dev/SCP_40k_qwq_solution_verified
End of training
Browse files- README.md +2 -1
- all_results.json +8 -0
- train_results.json +8 -0
- trainer_state.json +0 -0
- training_loss.png +0 -0
README.md
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base_model: Qwen/Qwen2.5-7B-Instruct
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tags:
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- llama-factory
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- generated_from_trainer
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model-index:
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- name: SCP_40k_qwq_solution_verified
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# SCP_40k_qwq_solution_verified
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This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on
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## Model description
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base_model: Qwen/Qwen2.5-7B-Instruct
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tags:
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- llama-factory
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- full
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- generated_from_trainer
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model-index:
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- name: SCP_40k_qwq_solution_verified
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# SCP_40k_qwq_solution_verified
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This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the mlfoundations-dev/SCP_40k_qwq_solution_verified dataset.
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## Model description
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all_results.json
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{
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"epoch": 3.0,
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"total_flos": 4823744474644480.0,
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"train_loss": 0.42331435592460115,
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"train_runtime": 136035.2407,
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"train_samples_per_second": 2.875,
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"train_steps_per_second": 0.03
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}
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train_results.json
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{
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"epoch": 3.0,
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"total_flos": 4823744474644480.0,
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"train_loss": 0.42331435592460115,
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"train_runtime": 136035.2407,
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"train_samples_per_second": 2.875,
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"train_steps_per_second": 0.03
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}
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trainer_state.json
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training_loss.png
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